Search

Human genetic variation is the incarnation of diverse evolutionary history, which reflects both selectively advantageous and selectively neutral change. In this study, we catalogue structural and functional features of proteins that restrain genetic variation leading to single amino acid substitutions. Our variation dataset is divided into three categories: i) Mendelian disease-related variants, ii) neutral polymorphisms and iii) cancer somatic mutations. We characterize structural environments of the amino acid variants by the following properties: i) side-chain solvent accessibility, ii) main-chain secondary structure, and iii) hydrogen bonds from a side chain to a main chain or other side chains. To address functional restraints, amino acid substitutions in proteins are examined to see whether they are located at functionally important sites involved in protein-protein interactions, protein-ligand interactions or catalytic activity of enzymes. We also measure the likelihood of amino acid substitutions and the degree of residue conservation where variants occur. We show that various types of variants are under different degrees of structural and functional restraints, which affect their occurrence in human proteome.

Background Alternative polyadenylation as a mechanism in gene expression regulation has been widely recognized in recent years. Arabidopsis polyadenylation factor PCFS4 was shown to function in leaf development and in flowering time control. The function of PCFS4 in controlling flowering time was correlated with the alternative polyadenylation of FCA, a flowering time regulator. However, genetic evidence suggested additional targets of PCFS4 that may mediate its function in both flowering time and leaf development. Methodology/Principal Findings To identify further targets, we investigated the whole transcriptome of a PCFS4 mutant using Affymetrix Arabidopsis genomic tiling 1.0R array and developed a data analysis pipeline, termed RADPRE (Ratio-based Analysis of Differential mRNA Processing and Expression). In RADPRE, ratios of normalized probe intensities between wild type Columbia and a pcfs4 mutant were first generated. By doing so, one of the major problems of tiling array data—variations caused by differential probe affinity—was significantly alleviated. With the probe ratios as inputs, a hierarchy of statistical tests was carried out to identify differentially processed genes (DPG) and differentially expressed genes (DEG). The false discovery rate (FDR) of this analysis was estimated by using the balanced random combinations of Col/pcfs4 and pcfs4/Col ratios as inputs. Gene Ontology (GO) analysis of the DPGs and DEGs revealed potential new roles of PCFS4 in stress responses besides flowering time regulation. Conclusion/Significance We identified 68 DPGs and 114 DEGs with FDR at 1% and 2%, respectively. Most of the 68 DPGs were subjected to alternative polyadenylation, splicing or transcription initiation. Quantitative PCR analysis of a set of DPGs confirmed that most of these genes were truly differentially processed in pcfs4 mutant plants. The enriched GO term “regulation of flower development” among PCFS4 targets further indicated the efficacy of the RADPRE pipeline. This simple but effective program is available upon request.

Significant progress has been made in isolating novel alkaline β-mannanases, however, there is a paucity of information concerning the structural basis for alkaline tolerance displayed by these β-mannanases. We report the catalytic domain structure of an industrially important β-mannanase from the alkaliphilic Bacillus sp. N16-5 (BSP165 MAN) at a resolution of 1.6 Å. This enzyme, classified into subfamily 8 in glycosyl hydrolase family 5 (GH5), has a pH optimum of enzymatic activity at pH 9.5 and folds into a classic (β/α)(8)-barrel. In order to gain insight into molecular features for alkaline adaptation, we compared BSP165 MAN with previously reported GH5 β-mannanases. It was revealed that BSP165 MAN and other subfamily 8 β-mannanases have significantly increased hydrophobic and Arg residues content and decreased polar residues, comparing to β-mannanases of subfamily 7 or 10 in GH5 which display optimum activities at lower pH. Further, extensive structural comparisons show alkaline β-mannanases possess a set of distinctive features. Position and length of some helices, strands and loops of the TIM barrel structures are changed, which contributes, to a certain degree, to the distinctly different shaped (β/α)(8)-barrels, thus affecting the catalytic environment of these enzymes. The number of negatively charged residues is increased on the molecular surface, and fewer polar residues are exposed to the solvent. Two amino acid substitutions in the vicinity of the acid/base catalyst were proposed to be possibly responsible for the variation in pH optimum of these homologous enzymes in subfamily 8 of GH5, identified by sequence homology analysis and pK(a) calculations of the active site residues. Mutational analysis has proved that Gln91 and Glu226 are important for BSP165 MAN to function at high pH. These findings are proposed to be possible factors implicated in the alkaline adaptation of GH5 β-mannanases and will help to further understanding of alkaline adaptation mechanism.

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks.

We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale. The approach has been applied to the genome of Mycobacterium tuberculosis (M.tb), the causative agent of one of today's most widely spread infectious diseases. The resulting drug-target interaction network for all structurally characterized approved drugs bound to putative M.tb receptors, we refer to as the ‘TB-drugome’. The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets. Furthermore, a detailed analysis of the TB-drugome has shed new light on the controversial issues surrounding drug-target networks [1]–[3]. Indeed, our results support the idea that drug-target networks are inherently modular, and further that any observed randomness is mainly caused by biased target coverage. The TB-drugome (http://funsite.sdsc.edu/drugome/TB) has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics., Author Summary The worldwide increase in multi-drug resistant TB poses a great threat to human health and highlights the need to identify new anti-tubercular agents. We have developed a computational strategy to link the structural proteome of Mycobacterium tuberculosis, the causative agent of tuberculosis, to all structurally characterized approved drugs, and hence construct a proteome-wide drug-target network – the TB-drugome. The TB-drugome has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally, the proteome-wide and multi-scale view of target and drug space may facilitate a systematic drug discovery process, which concurrently takes into account the disease mechanism and druggability of targets, the drug-likeness and ADMET properties of chemical compounds, and the genetic dispositions of individuals. Ultimately it may help to reduce the high attrition rate in drug development through a better understanding of drug-receptor interactions on a large scale.

Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution., Author Summary Enzymes are responsible for several critical cellular functions. The so-called catalytic residues are fundamental to attain the enzyme function. Those residues are often highly conserved within protein families sharing similar structure and function. Characterization of catalytic residues is essential for the understanding of enzyme function. However, this is a difficult task because conservation is a poor discriminator of catalytic residues due to the fact that many non-catalytic residues are highly conserved in a given protein family. We anticipate that variations in the structural environment of a catalytic site should be highly restrained in order for the protein to maintain its function along the course of evolution, and hypothesise that catalytic residues, due to these restrains, must carry a particular signature defined by networks of proximity sharing high mutual information (MI). We validated this hypothesis on a large data set of protein sequences with known catalytic residues, and demonstrated that catalytic sites are indeed surrounded by networks of coevolved residues. Such networks should also be present in other classes of proteins and we suggest that MI networks could be a novel feature of general importance beneficial for the prediction of functional residues.

Heterotrimeric G proteins act as the physical nexus between numerous receptors that respond to extracellular signals and proteins that drive the cytoplasmic response. The Gα subunit of the G protein, in particular, is highly constrained due to its many interactions with proteins that control or react to its conformational state. Various organisms contain differing sets of Gα-interacting proteins, clearly indicating that shifts in sequence and associated Gα functionality were acquired over time. These numerous interactions constrained much of Gα evolution; yet Gα has diversified, through poorly understood processes, into several functionally specialized classes, each with a unique set of interacting proteins. Applying a synthetic sequence-based approach to mammalian Gα subunits, we established a set of seventy-five evolutionarily important class-distinctive residues, sites where a single Gα class is differentiated from the three other classes. We tested the hypothesis that shifts at these sites are important for class-specific functionality. Importantly, we mapped known and well-studied class-specific functionalities from all four mammalian classes to sixteen of our class-distinctive sites, validating the hypothesis. Our results show how unique functionality can evolve through the recruitment of residues that were ancestrally functional. We also studied acquisition of functionalities by following these evolutionarily important sites in non-mammalian organisms. Our results suggest that many class-distinctive sites were established early on in eukaryotic diversification and were critical for the establishment of new Gα classes, whereas others arose in punctuated bursts throughout metazoan evolution. These Gα class-distinctive residues are rational targets for future structural and functional studies., Author Summary Proteins evolve new protein-protein interactions through changes to their residues. Many residue changes are harmful because they disrupt important existing interactions and functions. The more interactions a protein participates in, the more difficult it is to make changes that are not harmful to the protein. And yet, proteins with many existing interactions are also likely to evolve new functions or new interactions. How does evolution occur in the context of a well-constrained protein with many interactions? We studied the heterotrimeric G protein subunit Gα, a multi-functional protein that acts at the nexus between receptors responding to extracellular signals and the cytoplasmic proteins driving the response within the cell. The Gα subunit participates in numerous interactions that have constrained much of Gα evolution; yet Gα has diversified into four functionally specialized classes. We developed an approach that identifies key residue changes important to the evolution of Gα functionality and class, and gained insight into the types of residue changes that occurred both early and late in the evolution of Gα function. By studying these critical residues in Gα we can de-couple the many functionalities of this signaling nexus.

Soluble proteins must maintain backbone hydrogen bonds (BHBs) water-tight to ensure structural integrity. This protection is often achieved by burying the BHBs or wrapping them through intermolecular associations. On the other hand, water has low coordination resilience, with loss of hydrogen-bonding partnerships carrying significant thermodynamic cost. Thus, a core problem in structural biology is whether natural design actually exploits the water coordination stiffness to seal the backbone in regions that are exposed to the solvent. This work explores the molecular design features that make this type of seal operative, focusing on the side-chain arrangements that shield the protein backbone. We show that an efficient sealing is achieved by adapting the sub-nanoscale surface topography to the stringency of water coordination: an exposed BHB may be kept dry if the local concave curvature is small enough to impede formation of the coordination shell of a penetrating water molecule. Examination of an exhaustive database of uncomplexed proteins reveals that exposed BHBs invariably occur within such sub-nanoscale cavities in native folds, while this level of local ruggedness is absent in other regions. By contrast, BHB exposure in misfolded proteins occurs with larger local curvature promoting backbone hydration and consequently, structure disruption. These findings unravel physical constraints fitting a spatially dependent least-action for water coordination, introduce a molecular design concept, and herald the advent of water-tight peptide based materials with sufficient backbone exposure to remain flexible. © 2010 Schulz et al. Fil: Schulz, Erica Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina Fil: Frechero, Marisa Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina Fil: Appignanesi, Gustavo Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina Fil: Fernández, Ariel. Rice University; Estados Unidos

Single nucleotide polymorphisms (SNPs) constitute an important mode of genetic variations observed in the human genome. A small fraction of SNPs, about four thousand out of the ten million, has been associated with genetic disorders and complex diseases. The present study focuses on SNPs that fall on protein domains, 3D structures that facilitate connectivity of proteins in cell signaling and metabolic pathways. We scanned the human proteome using the PROSITE web tool and identified proteins with SNP containing domains. We showed that SNPs that fall on protein domains are highly statistically enriched among SNPs linked to hereditary disorders and complex diseases. Proteins whose domains are dramatically altered by the presence of an SNP are even more likely to be present among proteins linked to hereditary disorders. Proteins with domain-altering SNPs comprise highly connected nodes in cellular pathways such as the focal adhesion, the axon guidance pathway and the autoimmune disease pathways. Statistical enrichment of domain/motif signatures in interacting protein pairs indicates extensive loss of connectivity of cell signaling pathways due to domain-altering SNPs, potentially leading to hereditary disorders.

The DEG/ENaC (Degenerin/Epithelial Sodium Channel) protein family comprises related ion channel subunits from all metazoans, including humans. Members of this protein family play roles in several important biological processes such as transduction of mechanical stimuli, sodium re-absorption and blood pressure regulation. Several blocks of amino acid sequence are conserved in DEG/ENaC proteins, but structure/function relations in this channel class are poorly understood. Given the considerable experimental limitations associated with the crystallization of integral membrane proteins, knowledge-based modeling is often the only route towards obtaining reliable structural information. To gain insight into the structural characteristics of DEG/ENaC ion channels, we derived three-dimensional models of MEC-4 and UNC-8, based on the available crystal structures of ASIC1 (Acid Sensing Ion Channel 1). MEC-4 and UNC-8 are two DEG/ENaC family members involved in mechanosensation and proprioception respectively, in the nematode Caenorhabditis elegans. We used these models to examine the structural effects of specific mutations that alter channel function in vivo. The trimeric MEC-4 model provides insight into the mechanism by which gain-of-function mutations cause structural alterations that result in increased channel permeability, which trigger cell degeneration. Our analysis provides an introductory framework to further investigate the multimeric organization of the DEG/ENaC ion channel complex.

International audience; Mycobacterium tuberculosis DNA gyrase, an indispensable nanomachine involved in the regulation of DNA topology, is the only type II topoisomerase present in this organism and is hence the sole target for quinolone action, a crucial drug active against multidrug-resistant tuberculosis. To understand at an atomic level the quinolone resistance mechanism, which emerges in extensively drug resistant tuberculosis, we performed combined functional, biophysical and structural studies of the two individual domains constituting the catalytic DNA gyrase reaction core, namely the Toprim and the breakage-reunion domains. This allowed us to produce a model of the catalytic reaction core in complex with DNA and a quinolone molecule, identifying original mechanistic properties of quinolone binding and clarifying the relationships between amino acid mutations and resistance phenotype of M. tuberculosis DNA gyrase. These results are compatible with our previous studies on quinolone resistance. Interestingly, the structure of the entire breakage-reunion domain revealed a new interaction, in which the Quinolone-Binding Pocket (QBP) is blocked by the N-terminal helix of a symmetry-related molecule. This interaction provides useful starting points for designing peptide based inhibitors that target DNA gyrase to prevent its binding to DNA.

Background Weak intermolecular interactions such as hydrogen bonding and hydrophobic interactions are key players in stabilizing energetically-favored ligands, in an open conformational environment of protein structures. However, it is still poorly understood how the binding parameters associated with these interactions facilitate a drug-lead to recognize a specific target and improve drugs efficacy. To understand this, comprehensive analysis of hydrophobic interactions, hydrogen bonding and binding affinity have been analyzed at the interface of c-Src and c-Abl kinases and 4-amino substituted 1H-pyrazolo [3, 4-d] pyrimidine compounds. Methodology In-silico docking studies were performed, using Discovery Studio software modules LigandFit, CDOCKER and ZDOCK, to investigate the role of ligand binding affinity at the hydrophobic pocket of c-Src and c-Abl kinase. Hydrophobic and hydrogen bonding interactions of docked molecules were compared using LigPlot program. Furthermore, 3D-QSAR and MFA calculations were scrutinized to quantify the role of weak interactions in binding affinity and drug efficacy. Conclusions The in-silico method has enabled us to reveal that a multi-targeted small molecule binds with low affinity to its respective targets. But its binding affinity can be altered by integrating the conformationally favored functional groups at the active site of the ligand-target interface. Docking studies of 4-amino-substituted molecules at the bioactive cascade of the c-Src and c-Abl have concluded that 3D structural folding at the protein-ligand groove is also a hallmark for molecular recognition of multi-targeted compounds and for predicting their biological activity. The results presented here demonstrate that hydrogen bonding and optimized hydrophobic interactions both stabilize the ligands at the target site, and help alter binding affinity and drug efficacy.

To understand how symmetric structures of many proteins are formed from asymmetric sequences, the proteins with two repeated beta-trefoil domains in Plant Cytotoxin B-chain family and all presently known beta-trefoil proteins are analyzed by structure-based multi-sequence alignments. The results show that all these proteins have similar key structural residues that are distributed symmetrically in their structures. These symmetric key structural residues are further analyzed in terms of inter-residues interaction numbers and B-factors. It is found that they can be distinguished from other residues and have significant propensities for structural framework. This indicates that these key structural residues may conduct the formation of symmetric structures although the sequences are asymmetric.

Viral fusogenic envelope proteins are important targets for the development of inhibitors of viral entry. We report an approach for the computational design of peptide inhibitors of the dengue 2 virus (DENV-2) envelope (E) protein using high-resolution structural data from a pre-entry dimeric form of the protein. By using predictive strategies together with computational optimization of binding “pseudoenergies”, we were able to design multiple peptide sequences that showed low micromolar viral entry inhibitory activity. The two most active peptides, DN57opt and 1OAN1, were designed to displace regions in the domain II hinge, and the first domain I/domain II beta sheet connection, respectively, and show fifty percent inhibitory concentrations of 8 and 7 µM respectively in a focus forming unit assay. The antiviral peptides were shown to interfere with virus:cell binding, interact directly with the E proteins and also cause changes to the viral surface using biolayer interferometry and cryo-electron microscopy, respectively. These peptides may be useful for characterization of intermediate states in the membrane fusion process, investigation of DENV receptor molecules, and as lead compounds for drug discovery., Author Summary Virus surface proteins mediate interactions with target cells during the initial events in the process of infection. Inhibiting these proteins is therefore a major target for the development of antiviral drugs. However, there are a very large number of different viruses, each with their own distinct surface proteins and, with just a few exceptions, it is not clear how to build novel molecules to inhibit them. Here we applied a computational binding optimization strategy to an atomic resolution structure of dengue virus serotype 2 envelope protein to generate peptide sequences that should interact strongly with this protein. We picked dengue virus as a target because it is the causative agent for the most important mosquito transmitted viral disease. Out of a small number of candidates designed and tested, we identified two different highly inhibitory peptides. To verify our results, we showed that these peptides block virus:cell binding, interfere with a step during viral entry, alter the surface structure of dengue viral particles, and that they interact directly with dengue virus envelope protein. We expect that our approach may be generally applicable to other viral surface proteins where a high resolution structure is available.

Predicting off-targets by computational methods is getting increasing importance in early drug discovery stages. We herewith present a computational method based on binding site three-dimensional comparisons, which prompted us to investigate the cross-reaction of protein kinase inhibitors with synapsin I, an ATP-binding protein regulating neurotransmitter release in the synapse. Systematic pair-wise comparison of the staurosporine-binding site of the proto-oncogene Pim-1 kinase with 6,412 druggable protein-ligand binding sites suggested that the ATP-binding site of synapsin I may recognize the pan-kinase inhibitor staurosporine. Biochemical validation of this hypothesis was realized by competition experiments of staurosporine with ATP-gamma(35)S for binding to synapsin I. Staurosporine, as well as three other inhibitors of protein kinases (cdk2, Pim-1 and casein kinase type 2), effectively bound to synapsin I with nanomolar affinities and promoted synapsin-induced F-actin bundling. The selective Pim-1 kinase inhibitor quercetagetin was shown to be the most potent synapsin I binder (IC50 = 0.15 microM), in agreement with the predicted binding site similarities between synapsin I and various protein kinases. Other protein kinase inhibitors (protein kinase A and chk1 inhibitor), kinase inhibitors (diacylglycerolkinase inhibitor) and various other ATP-competitors (DNA topoisomerase II and HSP-90alpha inhibitors) did not bind to synapsin I, as predicted from a lower similarity of their respective ATP-binding sites to that of synapsin I. The present data suggest that the observed downregulation of neurotransmitter release by some but not all protein kinase inhibitors may also be contributed by a direct binding to synapsin I and phosphorylation-independent perturbation of synapsin I function. More generally, the data also demonstrate that cross-reactivity with various targets may be detected by systematic pair-wise similarity measurement of ligand-annotated binding sites.

The inhibition of specific SH2 domain mediated protein-protein interactions as an effective chemotherapeutic approach in the treatment of diseases remains a challenge. That different conformations of peptide-ligands are preferred by different SH2 domains is an underappreciated observation from the structural analysis of phosphotyrosine peptide binding to SH2 domains that may aid in future drug design. To explore the nature of ligand binding, we use simulated annealing (SA) to sample the conformational space of phosphotyrosine-containing peptides complexed with the Src SH2 domain. While in good agreement with the crystallographic and NMR studies of high-affinity phosphopeptide-SH2 domain complexes, the results suggest that the structural basis for phopsphopeptide- Src SH2 interactions is more complex than the "two-pronged plug two-hole socket" model. A systematic study of peptides of type pYEEX, where pY is phosphotyrosine and X is a hydrophobic residue, indicates that these peptides can assume two conformations, one extended and one helical, representing the balance between the interaction of residue X with the hydrophobic hole on the surface of the Src SH2 domain, and its contribution to the inherent tendency of the two glutamic acids to form an alpha-helix. In contrast, a beta-turn conformation, almost identical to that observed in the crystal structure of pYVNV bound to the Grb2 SH2 domain, predominates for pYXNX peptides, even in the presence of isoleucine at the third position. While peptide binding affinities, as measured by fluorescence polarization, correlate with the relative proportion of extended peptide conformation, these results suggest a model where all three residues C-terminal to the phosphotyrosine determine the conformation of the bound phosphopeptide. The information obtained in this work can be used in the design of specific SH2 domain inhibitors.

Streptococcus pneumoniae, like many other Gram-positive bacteria, assembles long filamentous pili on their surface through which they adhere to host cells. Pneumococcal pili are formed by a backbone, consisting of the repetition of the major component RrgB, and two accessory proteins (RrgA and RrgC). Here we reconstruct by transmission electron microscopy and single particle image reconstruction method the three dimensional arrangement of two neighbouring RrgB molecules, which represent the minimal repetitive structural domain of the native pilus. The crystal structure of the D2-D4 domains of RrgB was solved at 1.6 A resolution. Rigid-body fitting of the X-ray coordinates into the electron density map enabled us to define the arrangement of the backbone subunits into the S. pneumoniae native pilus. The quantitative fitting provide evidence that the pneumococcal pilus consists uniquely of RrgB monomers assembled in a head-to-tail organization. The presence of short intra-subunit linker regions connecting neighbouring domains provides the molecular basis for the intrinsic pilus flexibility.

Background Trypanosoma brucei is the etiological agent of Human African Trypanosomiasis, an endemic parasitic disease of sub-Saharan Africa. TbCatB and rhodesain are the sole Clan CA papain-like cysteine proteases produced by the parasite during infection of the mammalian host and are implicated in the progression of disease. Of considerable interest is the exploration of these two enzymes as targets for cysteine protease inhibitors that are effective against T. brucei. Methods and Findings We have determined, by X-ray crystallography, the first reported structure of TbCatB in complex with the cathepsin B selective inhibitor CA074. In addition we report the structure of rhodesain in complex with the vinyl-sulfone K11002. Conclusions The mature domain of our TbCat•CA074 structure contains unique features for a cathepsin B-like enzyme including an elongated N-terminus extending 16 residues past the predicted maturation cleavage site. N-terminal Edman sequencing reveals an even longer extension than is observed amongst the ordered portions of the crystal structure. The TbCat•CA074 structure confirms that the occluding loop, which is an essential part of the substrate-binding site, creates a larger prime side pocket in the active site cleft than is found in mammalian cathepsin B-small molecule structures. Our data further highlight enhanced flexibility in the occluding loop main chain and structural deviations from mammalian cathepsin B enzymes that may affect activity and inhibitor design. Comparisons with the rhodesain•K11002 structure highlight key differences that may impact the design of cysteine protease inhibitors as anti-trypanosomal drugs., Author Summary Proteases are ubiquitous in all forms of life and catalyze the enzymatic degradation of proteins. These enzymes regulate and coordinate a vast number of cellular processes and are therefore essential to many organisms. While serine proteases dominate in mammals, parasitic organisms commonly rely on cysteine proteases of the Clan CA family throughout their lifecycle. Clan CA cysteine proteases are therefore regarded as promising targets for the selective design of drugs to treat parasitic diseases, such as Human African Trypanosomiasis caused by Trypanosoma brucei. The genomes of kinetoplastids such as Trypanosoma spp. and Leishmania spp. encode two Clan CA C1 family cysteine proteases and in T. brucei these are represented by rhodesain and TbCatB. We have determined three-dimensional structures of these two enzymes as part of our ongoing efforts to synthesize more effective anti-trypanosomal drugs.

Understanding the mechanisms of protein–protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein–protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein–protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution., Author Summary Interaction between proteins is a fundamental process, generic to most biological pathways. The increasing number of protein–protein complexes with atomic data should help us to understand the major factors that guide protein interactions. In particular, a number of examples are available of similar proteins that interact with proteins that are very different in terms of structure and function. An intuitive hypothesis to explain the ability of these different proteins to recognize the same partner is that they display the same local region for interaction, in other words, they imitate the same binding site. Here, we quantify the similarity between these putatively mimicking binding sites. We show that it is not statistically significant. We confirm this observation on the small sets of evolutionarily conserved residues. Our results suggest that different proteins that bind the same protein do not imitate binding sites, but probably target specific locations or residues at the binding site.

The prediction of functional RNA structures has attracted increased interest, as it allows us to study the potential functional roles of many genes. RNA structure prediction methods, however, assume that there is a unique functional RNA structure and also do not predict functional features required for in vivo folding. In order to understand how functional RNA structures form in vivo, we require sophisticated experiments or reliable prediction methods. So far, there exist only a few, experimentally validated transient RNA structures. On the computational side, there exist several computer programs which aim to predict the co-transcriptional folding pathway in vivo, but these make a range of simplifying assumptions and do not capture all features known to influence RNA folding in vivo. We want to investigate if evolutionarily related RNA genes fold in a similar way in vivo. To this end, we have developed a new computational method, Transat, which detects conserved helices of high statistical significance. We introduce the method, present a comprehensive performance evaluation and show that Transat is able to predict the structural features of known reference structures including pseudo-knotted ones as well as those of known alternative structural configurations. Transat can also identify unstructured sub-sequences bound by other molecules and provides evidence for new helices which may define folding pathways, supporting the notion that homologous RNA sequence not only assume a similar reference RNA structure, but also fold similarly. Finally, we show that the structural features predicted by Transat differ from those assuming thermodynamic equilibrium. Unlike the existing methods for predicting folding pathways, our method works in a comparative way. This has the disadvantage of not being able to predict features as function of time, but has the considerable advantage of highlighting conserved features and of not requiring a detailed knowledge of the cellular environment., Author Summary Many non-coding genes exert their function via an RNA structure which starts emerging while the RNA sequence is being transcribed from the genome. The resulting folding pathway is known to depend on a variety of features such as the transcription speed, the concentration of various ions and the binding of proteins and other molecules. Not all of these influences can be adequately captured by the existing computational methods which try to replicate what happens in vivo. So far, it has been challenging to experimentally investigate co-transcriptional folding pathways in vivo and only little data from in vitro experiments exists. In order to investigate if functionally similar RNA sequences from different organisms fold in a similar way, we have developed a new computational method, called Transat, which does not require the detailed computational modeling of the cellular environment. We show in a comprehensive analysis that our method is capable of detecting known structural features and provide evidence that structural features of the in vivo folding pathways have been conserved for several biologically interesting classes of RNA sequences.

DNase I requires Ca2+ and Mg2+ for hydrolyzing double-stranded DNA. However, the number and the location of DNase I ion-binding sites remain unclear, as well as the role of these counter-ions. Using molecular dynamics simulations, we show that bovine pancreatic (bp) DNase I contains four ion-binding pockets. Two of them strongly bind Ca2+ while the other two sites coordinate Mg2+. These theoretical results are strongly supported by revisiting crystallographic structures that contain bpDNase I. One Ca2+ stabilizes the functional DNase I structure. The presence of Mg2+ in close vicinity to the catalytic pocket of bpDNase I reinforces the idea of a cation-assisted hydrolytic mechanism. Importantly, Poisson-Boltzmann-type electrostatic potential calculations demonstrate that the divalent cations collectively control the electrostatic fit between bpDNase I and DNA. These results improve our understanding of the essential role of cations in the biological function of bpDNase I. The high degree of conservation of the amino acids involved in the identified cation-binding sites across DNase I and DNase I-like proteins from various species suggests that our findings generally apply to all DNase I-DNA interactions., Author Summary DNase I requires Ca2+ and Mg2+ for hydrolyzing double-stranded DNA. Here, we show that bovine pancreatic (bp) DNase I contains four ion-binding pockets. Two of them, previously observed in the crystallographic structure of free bpDNase I, strongly bind Ca2+. The other two sites bind Mg2+ and are described in detail for the first time. One Ca2+ stabilizes the functional DNase I structure. The presence of Mg2+ in close vicinity to the catalytic pocket of bpDNase I reinforces the idea of a cation-assisted hydrolytic mechanism. Poisson-Boltzmann-type electrostatic potential calculations demonstrate that the divalent cations collectively control the electrostatic fit between bpDNase I and DNA. Thus, this work reveals the link between cation binding and the biological function of bpDNase I. The high degree of conservation of the amino acids involved in the identified cation-binding sites across DNase I and DNase I-like proteins from various species suggests that our findings generally apply to all DNase I-DNA interactions.

International audience; BACKGROUND: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases. METHODOLOGY/PRINCIPAL FINDINGS: WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed. CONCLUSIONS/SIGNIFICANCE: For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.

Background Formin proteins utilize a conserved formin homology 2 (FH2) domain to nucleate new actin filaments. In mammalian diaphanous-related formins (DRFs) the FH2 domain is inhibited through an unknown mechanism by intramolecular binding of the diaphanous autoinhibitory domain (DAD) and the diaphanous inhibitory domain (DID). Methodology/Principal Findings Here we report the crystal structure of a complex between DID and FH2-DAD fragments of the mammalian DRF, mDia1 (mammalian diaphanous 1 also called Drf1 or p140mDia). The structure shows a tetrameric configuration (4 FH2 + 4 DID) in which the actin-binding sites on the FH2 domain are sterically occluded. However biochemical data suggest the full-length mDia1 is a dimer in solution (2 FH2 + 2 DID). Based on the crystal structure, we have generated possible dimer models and found that architectures of all of these models are incompatible with binding to actin filament but not to actin monomer. Furthermore, we show that the minimal functional monomeric unit in the FH2 domain, termed the bridge element, can be inhibited by isolated monomeric DID. NMR data on the bridge-DID system revealed that at least one of the two actin-binding sites on the bridge element is accessible to actin monomer in the inhibited state. Conclusions/Significance Our findings suggest that autoinhibition in the native DRF dimer involves steric hindrance with the actin filament. Although the structure of a full-length DRF would be required for clarification of the presented models, our work here provides the first structural insights into the mechanism of the DRF autoinhibition.

The completion of the rice genome sequence has made it possible to identify and characterize new genes and to perform comparative genomics studies across taxa. The aldehyde dehydrogenase (ALDH) gene superfamily encoding for NAD(P)(+)-dependent enzymes is found in all major plant and animal taxa. However, the characterization of plant ALDHs has lagged behind their animal- and prokaryotic-ALDH homologs. In plants, ALDHs are involved in abiotic stress tolerance, male sterility restoration, embryo development and seed viability and maturation. However, there is still no structural property-dependent functional characterization of ALDH protein superfamily in plants. In this paper, we identify members of the rice ALDH gene superfamily and use the evolutionary nesting events of retrotransposons and protein-modeling-based structural reconstitution to report the genetic and molecular and structural features of each member of the rice ALDH superfamily in abiotic/biotic stress responses and developmental processes. Our results indicate that rice-ALDHs are the most expanded plant ALDHs ever characterized. This work represents the first report of specific structural features mediating functionality of the whole families of ALDHs in an organism ever characterized.

Multiple variants of the AMBER all-atom force field were quantitatively evaluated with respect to their ability to accurately characterize helix-coil equilibria in explicit solvent simulations. Using a global distributed computing network, absolute conformational convergence was achieved for large ensembles of the capped A(21) and F(s) helical peptides. Further assessment of these AMBER variants was conducted via simulations of a flexible 164-residue five-helix-bundle protein, apolipophorin-III, on the 100 ns timescale. Of the contemporary potentials that had not been assessed previously, the AMBER-99SB force field showed significant helix-destabilizing tendencies, with beta bridge formation occurring in helical peptides, and unfolding of apolipophorin-III occurring on the tens of nanoseconds timescale. The AMBER-03 force field, while showing adequate helical propensities for both peptides and stabilizing apolipophorin-III, (i) predicts an unexpected decrease in helicity with ALA-->ARG(+) substitution, (ii) lacks experimentally observed 3(10) helical content, and (iii) deviates strongly from average apolipophorin-III NMR structural properties. As is observed for AMBER-99SB, AMBER-03 significantly overweighs the contribution of extended and polyproline backbone configurations to the conformational equilibrium. In contrast, the AMBER-99phi force field, which was previously shown to best reproduce experimental measurements of the helix-coil transition in model helical peptides, adequately stabilizes apolipophorin-III and yields both an average gyration radius and polar solvent exposed surface area that are in excellent agreement with the NMR ensemble.

Background YqeH, a circularly permuted GTPase (cpGTPase), which is conserved across bacteria and eukaryotes including humans is important for the maturation of small (30S) ribosomal subunit in Bacillus subtilis. Recently, we have shown that it binds 30S in a GTP/GDP dependent fashion. However, the catalytic machinery employed to hydrolyze GTP is not recognized for any of the cpGTPases, including YqeH. This is because they possess a hydrophobic substitution in place of a catalytic glutamine (present in Ras-like GTPases). Such GTPases were categorized as HAS-GTPases and were proposed to follow a catalytic mechanism, different from the Ras-like proteins. Methodology/principal findings MnmE, another HAS-GTPase, but not circularly permuted, utilizes a potassium ion and water mediated interactions to drive GTP hydrolysis. Though the G-domain of MnmE and YqeH share only approximately 25% sequence identity, the conservation of characteristic sequence motifs between them prompted us to probe GTP hydrolysis machinery in YqeH, by employing homology modeling in conjunction with biochemical experiments. Here, we show that YqeH too, uses a potassium ion to drive GTP hydrolysis and stabilize the transition state. However, unlike MnmE, it does not dimerize in the transition state, suggesting alternative ways to stabilize switches I and II. Furthermore, we identify a potential catalytic residue in Asp-57, whose recognition, in the absence of structural information, was non-trivial due to the circular permutation in YqeH. Interestingly, when compared with MnmE, helix alpha2 that presents Asp-57 is relocated towards the N-terminus in YqeH. An analysis of the YqeH homology model, suggests that despite such relocation, Asp-57 may facilitate water mediated catalysis, similarly as the catalytic Glu-282 of MnmE. Indeed, an abolished catalysis by D57I mutant supports this inference. Conclusions/significance An uncommon means to achieve GTP hydrolysis utilizing a K(+) ion has so far been demonstrated only for MnmE. Here, we show that YqeH also utilizes a similar mechanism. While the catalytic machinery is similar in both, mechanistic differences may arise based on the way they are deployed. It appears that K(+) driven mechanism emerges as an alternative theme to stabilize the transition state and hydrolyze GTP in a subset of GTPases, such as the HAS-GTPases.

Protein knots, mostly regarded as intriguing oddities, are gradually being recognized as significant structural motifs. Seven distinctly knotted folds have already been identified. It is by and large unclear how these exceptional structures actually fold, and only recently, experiments and simulations have begun to shed some light on this issue. In checking the new protein structures submitted to the Protein Data Bank, we encountered the most complex and the smallest knots to date: A recently uncovered α-haloacid dehalogenase structure contains a knot with six crossings, a so-called Stevedore knot, in a projection onto a plane. The smallest protein knot is present in an as yet unclassified protein fragment that consists of only 92 amino acids. The topological complexity of the Stevedore knot presents a puzzle as to how it could possibly fold. To unravel this enigma, we performed folding simulations with a structure-based coarse-grained model and uncovered a possible mechanism by which the knot forms in a single loop flip., Author Summary Knots are ubiquitous in many aspects of our life, but remain elusive in proteins. The multitude of protein structures archived in the Protein Data Bank can be grouped into several hundred patterns, but only a handful are folded into knots. Combing through the recently added structures we found several novel knotted proteins. A microbial enzyme that catalyzes the breakdown of pollutants is the most complex protein knot encountered so far (similar to a knot used by stevedores for lifting cargo). The smallest knotted protein on the other hand consists of only 92 amino acids. The existence of these complex motifs demonstrates that the ability of self assembly goes far beyond normal expectations. Aided by computer simulations we present evidence which suggests that the Stevedore protein knot, despite its topological complexity, may actually form in a single flipping movement.

International audience; The assembly of subunits in protein oligomers is an important topic to study as a vast number of proteins exists as stable or transient oligomer and because it is a mechanism used by some protein oligomers for killing cells (e.g., perforin from the human immune system, pore-forming toxins from bacteria, phage, amoeba, protein misfolding diseases, etc.). Only a few of the amino acids that constitute a protein oligomer seem to regulate the capacity of the protein to assemble (to form interfaces), and some of these amino acids are localized at the interfaces that link the different chains. The identification of the residues of these interfaces is rather difficult. We have developed a series of programs, under the common name of Gemini, that can select the subset of the residues that is involved in the interfaces of a protein oligomer of known atomic structure, and generate a 2D interaction network (or graph) of the subset. The graphs generated for several oligomers demonstrate the accuracy of the selection of subsets that are involved in the geometrical and the chemical properties of interfaces. The results of the Gemini programs are in good agreement with those of similar programs with an advantage that Gemini programs can perform the residue selection much more rapidly. Moreover, Gemini programs can also perform on a single protein oligomer without the need of comparison partners. The graphs are extremely useful for comparative studies that would help in addressing questions not only on the sequence specificity of protein interfaces but also on the mechanism of the assembly of unrelated protein oligomers.

Background DNA topoisomerases are enzymes that control the topology of DNA in all cells. DNA gyrase is unique among the topoisomerases in that it is the only enzyme that can actively supercoil DNA using the free energy of ATP hydrolysis. Until recently gyrase was thought to be unique to bacteria, but has now been discovered in plants. The genome of the model plant, Arabidopsis thaliana, is predicted to encode four gyrase subunits: AtGyrA, AtGyrB1, AtGyrB2 and AtGyrB3. Methodology/Principal Findings We found, contrary to previous data, that AtGyrB3 is not essential to the survival of A. thaliana. Bioinformatic analysis suggests AtGyrB3 is considerably shorter than other gyrase B subunits, lacking part of the ATPase domain and other key motifs found in all type II topoisomerases; but it does contain a putative DNA-binding domain. Partially purified AtGyrB3 cannot bind E. coli GyrA or support supercoiling. AtGyrB3 cannot complement an E. coli gyrB temperature-sensitive strain, whereas AtGyrB2 can. Yeast two-hybrid analysis suggests that AtGyrB3 cannot bind to AtGyrA or form a dimer. Conclusions/Significance These data strongly suggest that AtGyrB3 is not a gyrase subunit but has another unknown function. One possibility is that it is a nuclear protein with a role in meiosis in pollen.

Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover "behind-the-scenes" aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm.

International audience; Small G-proteins of the superfamily Ras function as molecular switches, interacting with different cellular partners according to their activation state. G-protein activation involves the dissociation of bound GDP and its replacement by GTP, in an exchange reaction that is accelerated and regulated in the cell by guanine-nucleotide exchange factors (GEFs). Large conformational changes accompany the exchange reaction, and our understanding of the mechanism is correspondingly incomplete. However, much knowledge has been derived from structural studies of blocked or inactive mutant GEFs, which presumably closely represent intermediates in the exchange reaction and yet which are by design incompetent for carrying out the nucleotide exchange reaction. In this study we have used comparative modelling to recreate an exchange-competent form of a late, pre-GDP-ejection intermediate species in Arf1, a well-characterized small G-protein. We extensively characterized three distinct models of this intermediate using molecular dynamics simulations, allowing us to address ambiguities related to the mutant structural studies. We observed in particular the unfavorable nature of Mg 2z associated forms of the complex and the establishment of closer Arf1-GEF contacts in its absence. The results of this study shed light on GEF-mediated activation of this small G protein and on predicting the fate of the Mg ion at a critical point in the exchange reaction. The structural models themselves furnish additional targets for interfacial inhibitor design, a promising direction for exploring potentially druggable targets with high biological specificity.

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p, Author Summary Proteins function through their interactions with other biological molecules, including other proteins. Often times, these interactions underlie cellular processes that go awry in disease. Therefore, modulating these interactions with small molecules is an active area of research for new drugs to treat diseases and new chemical tools to dissect cellular interaction networks. However, targeting protein–protein interactions has proven to be more challenging than the typical drug targets found on individual proteins. Here, we present a computational approach that aims to help in this challenge by identifying regions of protein–protein interfaces that may be amenable to targeting by small molecules. Through a comprehensive analysis of all known protein structures, we identify closely related proteins that in one case bind a protein and in another case bind a small molecule. We find that a significant number of protein–protein interactions occur through surface regions that bind small molecules in related proteins. These “bi-functional” positions, which can bind both proteins and ligands, will serve as an additional piece of structural information that can aid experimentalists in developing small molecules that modulate protein interactions.

Etanercept, a TNF receptor 2-Fc fusion protein, is currently being used for the treatment of rheumatoid arthritis (RA). However, 25% to 38% of patients show no response which is suspected to be partially due to insufficient affinity of this protein to TNFα. By using computational protein design, we found that residue W89 and E92 of TNFR2 were critical for ligand binding. Among several mutants tested, W89Y/E92N displayed 1.49-fold higher neutralizing activity to TNFα, as compared to that of Etanercept. Surface plasmon resonance (SPR) based binding assay revealed that the equilibrium dissociation constant of W89Y/E92N to TNFα was 3.65-fold higher than that of Etanercept. In a rat model of collagen-induced arthritis (CIA), W89Y/E92N showed a significantly better ability than Etanercept in reducing paw swelling and improvement of arthritic joint histopathologically. These data demonstrate that W89Y/E92N is potentially a better candidate with improved efficacy in treating RA and other autoimmune diseases., Author Summary Rheumatoid arthritis (RA) is a frequently occurring, chronic, debilitating disease. TNFα plays a pivotal role in regulating its inflammatory response. TNFα inhibition with TNF receptor 2-Fc fusion protein (TNFR2-Fc) was effective in the treatment of RA. However, quite a few patients may not achieve good clinical outcomes after TNFR2-Fc therapy, and the high dosage of TNFR2-Fc in clinical treatment usually causes some side effects, such as injection site reactions. Enhancing the affinity of TNFR2-Fc to TNFα would be of benefit to its therapeutic effect on RA, and may reduce the clinical dosage. We modeled the interactions of TNFα and TNFR2, and figured out the sites that may be critical for ligand-receptor binding. A high affinity TNFR2-Fc variant (E92N/W89Y) was obtained by rational mutagenesis at residue 89 and 92. It shows significant improvements compared with wild type TNFR2-Fc in suppressing rat arthritis induced by collagen. This variant is more potent in neutralizing TNFα, and thereafter may offer a higher degree of RA symptom relief, and be in a much lower dosage.

Mycobacterium tuberculosis (Mtb), a dreaded pathogen, has a unique cell envelope composed of high fatty acid content that plays a crucial role in its pathogenesis. Acetyl Coenzyme A Carboxylase (ACC), an important enzyme that catalyzes the first reaction of fatty acid biosynthesis, is biotinylated by biotin acetyl-CoA carboxylase ligase (BirA). The ligand-binding loops in all known apo BirAs to date are disordered and attain an ordered structure only after undergoing a conformational change upon ligand-binding. Here, we report that dehydration of Mtb-BirA crystals traps both the apo and active conformations in its asymmetric unit, and for the first time provides structural evidence of such transformation. Recombinant Mtb-BirA was crystallized at room temperature, and diffraction data was collected at 295 K as well as at 120 K. Transfer of crystals to paraffin and paratone-N oil (cryoprotectants) prior to flash-freezing induced lattice shrinkage and enhancement in the resolution of the X-ray diffraction data. Intriguingly, the crystal lattice rearrangement due to shrinkage in the dehydrated Mtb-BirA crystals ensued structural order of otherwise flexible ligand-binding loops L4 and L8 in apo BirA. In addition, crystal dehydration resulted in a shift of approximately 3.5 A in the flexible loop L6, a proline-rich loop unique to Mtb complex as well as around the L11 region. The shift in loop L11 in the C-terminal domain on dehydration emulates the action responsible for the complex formation with its protein ligand biotin carboxyl carrier protein (BCCP) domain of ACCA3. This is contrary to the involvement of loop L14 observed in Pyrococcus horikoshii BirA-BCCP complex. Another interesting feature that emerges from this dehydrated structure is that the two subunits A and B, though related by a noncrystallographic twofold symmetry, assemble into an asymmetric dimer representing the ligand-bound and ligand-free states of the protein, respectively. In-depth analyses of the sequence and the structure also provide answers to the reported lower affinities of Mtb-BirA toward ATP and biotin substrates. This dehydrated crystal structure not only provides key leads to the understanding of the structure/function relationships in the protein in the absence of any ligand-bound structure, but also demonstrates the merit of dehydration of crystals as an inimitable technique to have a glance at proteins in action.

Predicting protein structure from primary sequence is one of the ultimate challenges in computational biology. Given the large amount of available sequence data, the analysis of co-evolution, i.e., statistical dependency, between columns in multiple alignments of protein domain sequences remains one of the most promising avenues for predicting residues that are contacting in the structure. A key impediment to this approach is that strong statistical dependencies are also observed for many residue pairs that are distal in the structure. Using a comprehensive analysis of protein domains with available three-dimensional structures we show that co-evolving contacts very commonly form chains that percolate through the protein structure, inducing indirect statistical dependencies between many distal pairs of residues. We characterize the distributions of length and spatial distance traveled by these co-evolving contact chains and show that they explain a large fraction of observed statistical dependencies between structurally distal pairs. We adapt a recently developed Bayesian network model into a rigorous procedure for disentangling direct from indirect statistical dependencies, and we demonstrate that this method not only successfully accomplishes this task, but also allows contacts with weak statistical dependency to be detected. To illustrate how additional information can be incorporated into our method, we incorporate a phylogenetic correction, and we develop an informative prior that takes into account that the probability for a pair of residues to contact depends strongly on their primary-sequence distance and the amount of conservation that the corresponding columns in the multiple alignment exhibit. We show that our model including these extensions dramatically improves the accuracy of contact prediction from multiple sequence alignments., Author Summary Whenever two residues are in close contact in the structure of a protein, their interaction will often constrain which amino acid substitutions can occur without perturbing the functionality of the protein, leading to “co-evolution” of the residues. With the large amount of data currently available, deep multiple alignments can be constructed of protein sequences that likely fold into a common structure, and several methods have been proposed for predicting contacting residues from statistical dependencies exhibited by pairs of alignment columns. Unfortunately, strong statistical dependencies are also observed between many pairs of residues that are distal in the structure. Through a comprehensive analysis of 2009 protein domains, we show that a large fraction of these distal dependencies are indirect and result from chains of contacting pairs that percolate through the protein. We present a Bayesian network model that rigorously disentangles direct from indirect dependencies and show that this greatly improves contact prediction. Additionally, we develop an informative prior that takes into account that the probability for residues to be in contact depends on their primary sequence separation, and that highly conserved residues tend to participate in a larger number of contacts. With this prior, the accuracy of the contact predictions is dramatically improved.

The immune system is engaged in a constant antigenic surveillance through the Major Histocompatibility Complex (MHC) class I antigen presentation pathway. This is an efficient mechanism for detection of intracellular infections, especially viral ones. In this work we describe conformational patterns shared by epitopes presented by a given MHC allele and use these features to develop a docking approach that simulates the peptide loading into the MHC cleft. Our strategy, to construct in silico MHC:peptide complexes, was successfully tested by reproducing four different crystal structures of MHC-I molecules available at the Protein Data Bank (PDB). An in silico study of cross-reactivity potential was also performed between the wild-type complex HLA-A2-NS31073 and nine MHC:peptide complexes presenting alanine exchange peptides. This indicates that structural similarities among the complexes can give us important clues about cross reactivity. The approach used in this work allows the selection of epitopes with potential to induce cross-reactive immune responses, providing useful tools for studies in autoimmunity and to the development of more comprehensive vaccines.

This work presents a novel detection method for three-dimensional domain swapping (DS), a mechanism for forming protein quaternary structures that can be visualized as if monomers had "opened" their "closed" structures and exchanged the opened portion to form intertwined oligomers. Since the first report of DS in the mid 1990s, an increasing number of identified cases has led to the postulation that DS might occur in a protein with an unconstrained terminus under appropriate conditions. DS may play important roles in the molecular evolution and functional regulation of proteins and the formation of depositions in Alzheimer's and prion diseases. Moreover, it is promising for designing auto-assembling biomaterials. Despite the increasing interest in DS, related bioinformatics methods are rarely available. Owing to a dramatic conformational difference between the monomeric/closed and oligomeric/open forms, conventional structural comparison methods are inadequate for detecting DS. Hence, there is also a lack of comprehensive datasets for studying DS. Based on angle-distance (A-D) image transformations of secondary structural elements (SSEs), specific patterns within A-D images can be recognized and classified for structural similarities. In this work, a matching algorithm to extract corresponding SSE pairs from A-D images and a novel DS score have been designed and demonstrated to be applicable to the detection of DS relationships. The Matthews correlation coefficient (MCC) and sensitivity of the proposed DS-detecting method were higher than 0.81 even when the sequence identities of the proteins examined were lower than 10%. On average, the alignment percentage and root-mean-square distance (RMSD) computed by the proposed method were 90% and 1.8Å for a set of 1,211 DS-related pairs of proteins. The performances of structural alignments remain high and stable for DS-related homologs with less than 10% sequence identities. In addition, the quality of its hinge loop determination is comparable to that of manual inspection. This method has been implemented as a web-based tool, which requires two protein structures as the input and then the type and/or existence of DS relationships between the input structures are determined according to the A-D image-based structural alignments and the DS score. The proposed method is expected to trigger large-scale studies of this interesting structural phenomenon and facilitate related applications.

Group IIA secreted/synovial phospholipase A(2) (GIIAPLA(2)) is an enzyme involved in the synthesis of eicosanoids such as prostaglandin E(2) (PGE(2)), the main eicosanoid contributing to pain and inflammation in rheumatic diseases. We designed, by molecular modeling, 7 novel analogs of 3-{4-[5(indol-1-yl)pentoxy]benzyl}-4H-1,2,4-oxadiazol-5-one, denoted C1, an inhibitor of the GIIAPLA(2) enzyme. We report the results of molecular dynamics studies of the complexes between these derivatives and GIIAPLA(2), along with their chemical synthesis and results from PLA(2) inhibition tests. Modeling predicted some derivatives to display greater GIIAPLA(2) affinities than did C1, and such predictions were confirmed by in vitro PLA(2) enzymatic tests. Compound C8, endowed with the most favorable energy balance, was shown experimentally to be the strongest GIIAPLA(2) inhibitor. Moreover, it displayed an anti-inflammatory activity on rabbit articular chondrocytes, as shown by its capacity to inhibit IL-1beta-stimulated PGE(2) secretion in these cells. Interestingly, it did not modify the COX-1 to COX-2 ratio. C8 is therefore a potential candidate for anti-inflammatory therapy in joints.

PLoS one 5(8), e12394 (2010). doi:10.1371/journal.pone.0012394, Published by PLoS [u.a.], Lawrence, Kan.

Background It has been shown that molecular interactions between site-specific chemical modifications such as acetylation and methylation on DNA-packing histones and conserved structural modules present in transcriptional proteins are closely associated with chromatin structural changes and gene activation. Unlike methyl-lysine that can interact with different protein modules including chromodomains, Tudor and MBT domains, as well as PHD fingers, acetyl-lysine (Kac) is known thus far to be recognized only by bromodomains. While histone lysine acetylation plays a crucial role in regulation of chromatin-mediated gene transcription, a high degree of sequence variation of the acetyl-lysine binding site in the bromodomains has limited our understanding of histone binding selectivity of the bromodomain family. Here, we report a systematic family-wide analysis of 14 yeast bromodomains binding to 32 lysine-acetylated peptides derived from known major acetylation sites in four core histones that are conserved in eukaryotes. Methodology The histone binding selectivity of purified recombinant yeast bromodomains was assessed by using the native core histones in an overlay assay, as well as N-terminally biotinylated lysine-acetylated histone peptides spotted on streptavidin-coated nitrocellulose membrane in a dot blot assay. NMR binding analysis further validated the interactions between histones and selected bromodomain. Structural models of all yeast bromodomains were built using comparative modeling to provide insights into the molecular basis of their histone binding selectivity. Conclusions Our study reveals that while not all members of the bromodomain family are privileged to interact with acetylated-lysine, identifiable sequence features from those that bind histone emerge. These include an asparagine residue at the C-terminus of the third helix in the 4-helix bundle, negatively charged residues around the ZA loop, and preponderance of aromatic amino acid residues in the binding pocket. Further, while bromodomains exhibit selectivity for different sites in histones, individual interactions are of modest affinity. Finally, electrostatic interactions appear to be a primary determining factor that guides productive association between a bromodomain and a lysine-acetylated histone.

Compartmentalized bacteria have proteins that are structurally related to eukaryotic membrane coats, and one of these proteins localizes at the membrane of vesicles formed inside bacterial cells., The development of the endomembrane system was a major step in eukaryotic evolution. Membrane coats, which exhibit a unique arrangement of β-propeller and α-helical repeat domains, play key roles in shaping eukaryotic membranes. Such proteins are likely to have been present in the ancestral eukaryote but cannot be detected in prokaryotes using sequence-only searches. We have used a structure-based detection protocol to search all proteomes for proteins with this domain architecture. Apart from the eukaryotes, we identified this protein architecture only in the Planctomycetes-Verrucomicrobia-Chlamydiae (PVC) bacterial superphylum, many members of which share a compartmentalized cell plan. We determined that one such protein is partly localized at the membranes of vesicles formed inside the cells in the planctomycete Gemmata obscuriglobus. Our results demonstrate similarities between bacterial and eukaryotic compartmentalization machinery, suggesting that the bacterial PVC superphylum contributed significantly to eukaryogenesis., Author Summary Despite decades of research, the origin of eukaryotic cells remains an unsolved issue. The endomembrane system defines the eukaryotic cell, and its origin is linked to that of eukaryotes. A search was conducted within all known sequences for proteins that are characteristic of the eukaryotic endomembrane system, using a combination of fold types that is uniquely found in the membrane coat proteins. Outside eukaryotes, such proteins were solely found in the Planctomycetes-Verrucomicrobia-Chlamydiae (PVC) bacterial superphylum. By immuno-electron microscopy, one of these bacterial proteins was found to localize adjacent to the membranes of vesicles found within the cells of one member of the PVC superphylum. Thus, there appear to be similarities between bacterial and eukaryotic compartmentalization systems, suggesting that the bacterial PVC superphylum may have contributed significantly to eukaryogenesis.

BACKGROUND: A central tenet of structural biology is that related proteins of common function share structural similarity. This has key practical consequences for the derivation and analysis of protein structures, and is exploited by the process of "molecular sieving" whereby a common core is progressively distilled from a comparison of two or more protein structures. This paper reports a novel web server for "sieving" of protein structures, based on the multiple structural alignment program MUSTANG. METHODOLOGY/PRINCIPAL FINDINGS: "Sieved" models are generated from MUSTANG-generated multiple alignment and superpositions by iteratively filtering out noisy residue-residue correspondences, until the resultant correspondences in the models are optimally "superposable" under a threshold of RMSD. This residue-level sieving is also accompanied by iterative elimination of the poorly fitting structures from the input ensemble. Therefore, by varying the thresholds of RMSD and the cardinality of the ensemble, multiple sieved models are generated for a given multiple alignment and superposition from MUSTANG. To aid the identification of structurally conserved regions of functional importance in an ensemble of protein structures, Lesk-Hubbard graphs are generated, plotting the number of residue correspondences in a superposition as a function of its corresponding RMSD. The conserved "core" (or typically active site) shows a linear trend, which becomes exponential as divergent parts of the structure are included into the superposition. CONCLUSIONS: The application addresses two fundamental problems in structural biology: first, the identification of common substructures among structurally related proteins--an important problem in characterization and prediction of function; second, generation of sieved models with demonstrated uses in protein crystallographic structure determination using the technique of Molecular Replacement.

Human Immunodeficiency Virus 1 uses for entry into host cells a receptor (CD4) and one of two co-receptors (CCR5 or CXCR4). Recently, a new class of antiretroviral drugs has entered clinical practice that specifically bind to the co-receptor CCR5, and thus inhibit virus entry. Accurate prediction of the co-receptor used by the virus in the patient is important as it allows for personalized selection of effective drugs and prognosis of disease progression. We have investigated whether it is possible to predict co-receptor usage accurately by analyzing the amino acid sequence of the main determinant of co-receptor usage, i.e., the third variable loop V3 of the gp120 protein. We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences. The second level combines the two predictions of the first level. The two-level method predicts usage of CXCR4 co-receptor for new V3 sequences within seconds, with an area under the ROC curve of 0.937±0.004. Moreover, it is relatively robust against insertions and deletions, which frequently occur in V3. The approach could help clinicians to find optimal personalized treatments, and it offers new insights into the molecular basis of co-receptor usage. For instance, it quantifies the importance for co-receptor usage of a pocket that probably is responsible for binding sulfated tyrosine., Author Summary Human Immunodeficiency Virus is the pathogen causing the disease AIDS. A precondition for virus entry into human cells is the contact of its glycoprotein gp120 with two cellular proteins, a receptor and a co-receptor. Depending on the viral strain, one specific co-receptor is used. The type of co-receptor used is crucial for the aggressiveness of the viral strain and the available treatment options. Hence, it is important to identify which co-receptor is used by the virus in an individual patient. Since the genome of the virus in the patient can be readily sequenced, and thus the composition of the viral proteins be determined, it could be possible to predict co-receptor usage from the viral genome sequences. To this end, we developed a method that is motivated by the insight that physical properties of gp120 will determine its specificity for a co-receptor. The method learns a computational model from structures and sequences of a crucial part of gp120, and the corresponding experimentally measured co-receptor usage. It then employs the model to predict co-receptor usage for new sequences. The high accuracy of the method could make it helpful for diagnosis and suggests that the model captures the determinants of co-receptor usage.

8 páginas, 3 figuras, 1 tabla.-- This is an open-access article distributed under the terms of the Creative Commons Attribution License., This work has been supported by funds provided by the Local Government Junta de Castilla y León (JCyL, ref. project: CSI07A09), by the Spanish Ministry of Science and Innovation (MICINN - ISCiii, ref. projects: PI061153 and PS09/00843) and by the European Commission Research Grant PSIMEx (ref. FP7-HEALTH-2007-223411).

The network of native non-covalent residue contacts determines the three-dimensional structure of a protein. However, not all contacts are of equal structural significance, and little knowledge exists about a minimal, yet sufficient, subset required to define the global features of a protein. Characterisation of this “structural essence” has remained elusive so far: no algorithmic strategy has been devised to-date that could outperform a random selection in terms of 3D reconstruction accuracy (measured as the Ca RMSD). It is not only of theoretical interest (i.e., for design of advanced statistical potentials) to identify the number and nature of essential native contacts—such a subset of spatial constraints is very useful in a number of novel experimental methods (like EPR) which rely heavily on constraint-based protein modelling. To derive accurate three-dimensional models from distance constraints, we implemented a reconstruction pipeline using distance geometry. We selected a test-set of 12 protein structures from the four major SCOP fold classes and performed our reconstruction analysis. As a reference set, series of random subsets (ranging from 10% to 90% of native contacts) are generated for each protein, and the reconstruction accuracy is computed for each subset. We have developed a rational strategy, termed “cone-peeling” that combines sequence features and network descriptors to select minimal subsets that outperform the reference sets. We present, for the first time, a rational strategy to derive a structural essence of residue contacts and provide an estimate of the size of this minimal subset. Our algorithm computes sparse subsets capable of determining the tertiary structure at approximately 4.8 Å Ca RMSD with as little as 8% of the native contacts (Ca-Ca and Cb-Cb). At the same time, a randomly chosen subset of native contacts needs about twice as many contacts to reach the same level of accuracy. This “structural essence” opens new avenues in the fields of structure prediction, empirical potentials and docking., Author Summary A protein structure can be visualized as a network of non-covalent contacts existing between amino acids. But not all such contacts are important structural determinants of a protein. We have attempted to identify a subset of amino acid contacts that are essential for reconstructing protein structures. Initially, we followed random sampling of contacts and tested their efficacy to successfully represent the three-dimensional structure. Further, we also developed an algorithm that selects a subset of amino acid contacts from proteins based on the sequence and network properties. The subsets picked by our algorithm represent protein three-dimensional structure better than random subsets, thereby offering direct evidence for the existence of a structural essence in protein structures. The identification of such structure-defining subsets finds application in experimental and computational protein structure determination.

Macromolecular surfaces are fundamental representations of their three-dimensional geometric shape. Accurate calculation of protein surfaces is of critical importance in the protein structural and functional studies including ligand-protein docking and virtual screening. In contrast to analytical or parametric representation of macromolecular surfaces, triangulated mesh surfaces have been proved to be easy to describe, visualize and manipulate by computer programs. Here, we develop a new algorithm of EDTSurf for generating three major macromolecular surfaces of van der Waals surface, solvent-accessible surface and molecular surface, using the technique of fast Euclidean Distance Transform (EDT). The triangulated surfaces are constructed directly from volumetric solids by a Vertex-Connected Marching Cube algorithm that forms triangles from grid points. Compared to the analytical result, the relative error of the surface calculations by EDTSurf is

Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/)., Author Summary Protein molecules are ubiquitous in the cell; they perform thousands of functions crucial for life. Proteins accomplish nearly all of these functions by interacting with other molecules. These interactions are mediated by specific amino acid positions in the proteins. Knowledge of these “functional sites” is crucial for understanding the molecular mechanisms by which proteins carry out their functions; however, functional sites have not been identified in the vast majority of proteins. Here, we present ConCavity, a computational method that predicts small molecule binding sites in proteins by combining analysis of evolutionary sequence conservation and protein 3D structure. ConCavity provides significant improvement over previous approaches, especially on large, multi-chain proteins. In contrast to earlier methods which only predict entire binding sites, ConCavity makes specific predictions of positions in space that are likely to overlap ligand atoms and of residues that are likely to contact bound ligands. These predictions can be used to aid computational function prediction, to guide experimental protein analysis, and to focus computationally intensive techniques used in drug discovery.

Nanomechanical properties of filamentous biopolymers, such as the persistence length, may be determined from two-dimensional images of molecules immobilized on surfaces. For a single filament in solution, two principal adsorption scenarios are possible. Both scenarios depend primarily on the interaction strength between the filament and the support: i) For interactions in the range of the thermal energy, the filament can freely equilibrate on the surface during adsorption; ii) For interactions much stronger than the thermal energy, the filament will be captured by the surface without having equilibrated. Such a 'trapping' mechanism leads to more condensed filament images and hence to a smaller value for the apparent persistence length. To understand the capture mechanism in more detail we have performed Brownian dynamics simulations of relatively short filaments by taking the two extreme scenarios into account. We then compared these 'ideal' adsorption scenarios with observed images of immobilized vimentin intermediate filaments on different surfaces. We found a good agreement between the contours of the deposited vimentin filaments on mica ('ideal' trapping) and on glass ('ideal' equilibrated) with our simulations. Based on these data, we have developed a strategy to reliably extract the persistence length of short worm-like chain fragments or network forming filaments with unknown polymer-surface interactions.

During cell entry of flaviviruses, low endosomal pH triggers the rearrangement of the viral surface glycoproteins to a fusion-active state that allows the release of the infectious RNA into the cytoplasm. In this work, West Nile virus was complexed with Fab fragments of the neutralizing mAb E16 and was subsequently exposed to low pH, trapping the virions in a pre-fusion intermediate state. The structure of the complex was studied by cryo-electron microscopy and provides the first structural glimpse of a flavivirus fusion intermediate near physiological conditions. A radial expansion of the outer protein layer of the virion was observed compared to the structure at pH 8. The resulting ∼60 Å-wide shell of low density between lipid bilayer and outer protein layer is likely traversed by the stem region of the E glycoprotein. By using antibody fragments, we have captured a structural intermediate of a virus that likely occurs during cell entry. The trapping of structural transition states by antibody fragments will be applicable for other processes in the flavivirus life cycle and delineating other cellular events that involve conformational rearrangements., Author Summary West Nile Virus (WNV) and other related viruses such as dengue virus enter their host cell by a process that involves fusion between the viral membrane and the membrane of cellular vesicles (endosomes) resulting in the release of the viral genome into the cytoplasm of the cell. This fusion event is initiated by low pH in the endosomes. Little is known regarding structural changes within the viral particle that render the viral surface proteins capable of fusion. In the present study, we used antibody fragments as a tool to trap virions in a pre-fusion intermediate state and examined these particles by cryo-electron microscopy. We showed that low pH triggered a radial displacement of the virion's external protein layer. The surface proteins moved away from the viral membrane, a shift made possible by the outward extension of a small alpha-helical region of the surface protein. The process gives the proteins greater sideways freedom for their reorganization into the fusion-active state. Our results provide a first structural glimpse into the low pH-induced conformational rearrangement of the flavivirus particle that occurs prior to fusion of viral and endosomal membranes. Such dissection of the fusion process highlights targets for the development of antiviral strategies.

The genome of Saccharomyces cerevisiae is arguably the best studied eukaryotic genome, and yet, it contains approximately 1000 genes that are still relatively uncharacterized. As the majority of these ORFs have no homologs with characterized sequence or protein structure, traditional sequence-based approaches cannot be applied to deduce their biological function. Here, we characterize YER067W, a conserved gene of unknown function that is strongly induced in response to many stress conditions and repressed in drug resistant yeast strains. Gene expression patterns of YER067W and its paralog YIL057C suggest an involvement in energy metabolism. We show that yeast lacking YER067W display altered levels of reserve carbohydrates and a growth deficiency in media that requires aerobic metabolism. Impaired mitochondrial function and overall reduction of ergosterol content in the YER067W deleted strain explained the observed 2- and 4-fold increase in resistance to the drugs fluconazole and amphotericin B, respectively. Cell fractionation and immunofluorescence microscopy revealed that Yer067w is associated with cellular membranes despite the absence of a transmembrane domain in the protein. Finally, the 1.7 A resolution crystal structure of Yer067w shows an alpha-beta fold with low similarity to known structures and a putative functional site. YER067W's involvement with aerobic energetic metabolism suggests the assignment of the gene name RGI1, standing for respiratory growth induced 1. Altogether, the results shed light on a previously uncharacterized protein family and provide basis for further studies of its apparent role in energy metabolism control and drug resistance.