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The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.

The Protein Data Bank (PDB) archive is a rich source of information in the form of atomic-level 3D structures of biomolecules experimentally determined using macromolecular crystallography (MX), nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy (3DEM). Originally established in 1971 as a resource for protein crystallographers to freely exchange data, today PDB data drive research and education across scientific disciplines. In 2011, the online portal PDB-101 was launched to support teachers, students, and the general public in PDB archive exploration (pdb101.rcsb.org). Maintained by the Research Collaboratory for Structural Bioinformatics PDB, PDB-101 aims to help train the next generation of PDB users and to promote the overall importance of structural biology and protein science to non-experts. Regularly published features include the highly popular Molecule of the Month series, 3D model activities, molecular animation videos, and educational curricula. Materials are organized into various categories (Health and Disease, Molecules of Life, Biotech and Nanotech, and Structures and Structure Determination) and searchable by keyword. A biennial health focus frames new resource creation and provides topics for annual video challenges for high school students. Web analytics document that PDB-101 materials relating to fundamental topics (e.g., hemoglobin, catalase) are highly accessed year-on-year. In addition, PDB-101 materials created in response to topical health matters (e.g., Zika, measles, coronavirus) are well-received. PDB-101 shows how learning about the diverse shapes and functions of PDB structures promotes understanding of all aspects of biology, from central dogma of biology to health and disease to biological energy. This article is protected by copyright. All rights reserved.

Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through https://albiorix.bioenv.gu.se/LiBiSCo/HomePage.py.

This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14(th) round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies.

Human papillomavirus (HPV) is the main cervical cancer‐promoting element that is transmitted through sexual routes. Anal, head, and throat cancers are also reported to be accompanied by HPV infection. E6 is one of the HPV nonstructural proteins, which is responsible for cell differentiation by targeting tumor suppressor genes, p105Rb and p53. E6 was reported to be stabilized by two chaperone proteins; glucose‐regulated protein 78 (GRP78) and heat shock protein 90. GRP78 is responsible for the unfolded protein response of the cells, and it was reported to be upregulated in many cancers, including cervical cancer. It was reported that knocking out GRP78 destabilizes E6 leading to faster degradation of E6 in vivo. The current work predicts the possible binding mode between E6 and GRP78 based on sequence and structural similarities., Research Highlights Understanding the binding behavior of glucose‐regulated protein 78 (GRP78) can pave the way for drugs that can prevent this interaction between the host cell proteins and the viral protein E6 that is crucial for cancer propagation.Once GRP78 binding is inhibited, E6 will be destabilized and prone to a faster degradation rate by the host cell degradation machinery.

Are there any generalized molecular principles of thermal adaptation? Here, integrating the concepts of structural bioinformatics, sequence analysis, and classical knot theory, we develop a robust computational framework that seeks for mechanisms of thermal adaptation by comparing orthologous mesophilic-thermophilic and mesophilic-hyperthermophilic proteins of remarkable structural and topological similarities, and still leads us to context-independent results. A comprehensive analysis of 4741 high-resolution, non-redundant X-ray crystallographic structures collected from 11 hyperthermophilic, 32 thermophilic and 53 mesophilic prokaryotes unravels at least five "nearly universal" signatures of thermal adaptation, irrespective of the enormous sequence, structure, and functional diversity of the proteins compared. A careful investigation further extracts a set of amino acid changes that can potentially enhance protein thermal stability, and remarkably, these mutations are overrepresented in protein crystallization experiments, in disorder-to-order transitions and in engineered thermostable variants of existing mesophilic proteins. These results could be helpful to find a precise, global picture of thermal adaptation.

Bioinformatics was recently introduced as a module for both undergraduate and postgraduate biological sciences students at our institution. Our experience shows that inquiry-based hands-on exercises provide the most efficient approach to bioinformatic straining. In this article, we report a structural bioinformatics project carried out by Master degree students to determine structure-function relationships of the uncharacterized prokaryotic 5-oxoprolinase subunit A (PxpA). PxpA associates with the PxpBC complex to form a functional 5-oxoprolinase enzyme for conversion of 5-oxoproline to L-glutamate. Although the exact role of PxpA is yet to be determined, it has been demonstrated that PxpBC catalyses the first step of the reaction, which is phosphorylation of 5-oxoproline. Here, we provide evidence that PxpA is involved in the last two steps of the reaction:decyclization of the labile phosphorylated 5-oxoproline to the equally labile γ-glutamylphosphate, and subsequent dephosphorylation to L-glutamate. Structural bioinformatics analysis of four putative PxpA structures revealed that PxpA adopts a non-canonical TIM barrel fold with well-characterized TIM barrel enzyme features. These include a C-terminal groove comprising potentially essential conserved amino acid residues organized into putative motifs. Phylogenetic analysis suggests a relationship between taxonomic grouping and PxpA oligomerization. PxpA forms a tunnel upon ligand binding, thus suggesting that the PxpABC complex employs the mechanism of substrate channeling to protect labile intermediates. Ultimately, students were able to form a testable hypothesis on the function of PxpA, an achievement we consider encouraging other students to emulate. © 2019 International Union of Biochemistry and Molecular Biology, 47(6):620-631, 2019.

We combined computational and experimental methods to interrogate the binding determinants of angiopoietin-2 (Ang2) to its receptor tyrosine kinase (RTK) Tie2-a central signaling system in angiogenesis, inflammation, and tumorigenesis. We used physics-based electrostatic and surface-area calculations to identify the subset of interfacial Ang2 and Tie2 residues that can affect binding directly. Using random and site-directed mutagenesis and yeast surface display (YSD), we validated these predictions and identified additional Ang2 positions that affected receptor binding. We then used burial-based calculations to classify the larger set of Ang2 residues that are buried in the Ang2 core, whose mutations can perturb the Ang2 structure and thereby affect interactions with Tie2 indirectly. Our analysis showed that the Ang2-Tie2 interface is dominated by nonpolar contributions, with only three Ang2 and two Tie2 residues that contribute electrostatically to intermolecular interactions. Individual interfacial residues contributed only moderately to binding, suggesting that engineering of this interface will require multiple mutations to reach major effects. Conversely, substitutions in substantially buried Ang2 residues were more prevalent in our experimental screen, reduced binding substantially, and are therefore more likely to have a deleterious effect that might contribute to oncogenesis. Computational analysis of additional RTK-ligand complexes, c-Kit-SCF and M-CSF-c-FMS, and comparison to previous YSD results, further show the utility of our combined methodology.

Contemporary biology is currently undergoing a revolution, driven by the availability of high-throughput technologies and a wide variety of bioinformatics tools. However, bioinformatics education and practice is still in its infancy in most of the African continent. Consequently, concerted efforts have been made in recent years to incorporate bioinformatics modules into biological sciences curriculum of African Universities. Despite this, one aspect of bioinformatics that is yet to be incorporated is structural bioinformatics. In this article, we report on a structural bioinformatics project carried out by final year project students in a Nigerian university. The target protein was the thermoacidophilic Sulfolobus islandicus rod-shaped virus 1 (SIRV1) Rep protein, which was further characterized using various free, user-friendly and online sequence-based and structure-based bioinformatics tools. This exercise gave students the opportunity to generate new data, interpret the data, and acquire collaborative research skills. In this report, emphasis is placed on analysis of the data generated to further encourage analytical skills. By sharing this experience, it is anticipated that other similar institutions would adopt parallel strategies to expose undergraduate students to structural biology, and increase awareness of freely available bioinformatics tools for tackling pertinent biological questions. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):547-554, 2018.

The increased availability of user-friendly and accessible computational tools for biomolecular modeling would expand the reach and application of biomolecular engineering and design. For protein modeling, one key challenge is to reduce the complexities of 3D protein folds to sets of parametric equations that nonetheless capture the salient features of these structures accurately. At present, this is possible for a subset of proteins, namely, repeat proteins. The α-helical coiled coil provides one such example, which represents ≈ 3-5% of all known protein-encoding regions of DNA. Coiled coils are bundles of α helices that can be described by a small set of structural parameters. Here we describe how this parametric description can be implemented in an easy-to-use web application, called CCBuilder 2.0, for modeling and optimizing both α-helical coiled coils and polyproline-based collagen triple helices. This has many applications from providing models to aid molecular replacement for X-ray crystallography, in silico model building and engineering of natural and designed protein assemblies, and through to the creation of completely de novo "dark matter" protein structures. CCBuilder 2.0 is available as a web-based application, the code for which is open-source and can be downloaded freely. http://coiledcoils.chm.bris.ac.uk/ccbuilder2. Lay summary We have created CCBuilder 2.0, an easy to use web-based application that can model structures for a whole class of proteins, the α-helical coiled coil, which is estimated to account for 3-5% of all proteins in nature. CCBuilder 2.0 will be of use to a large number of protein scientists engaged in fundamental studies, such as protein structure determination, through to more-applied research including designing and engineering novel proteins that have potential applications in biotechnology.

A recent scientific discipline, bioinformatics, defined as using informatics for the study of biological problems, is now a requirement for the study of biological sciences. Bioinformatics has become such a powerful and popular discipline that several academic institutions have created programs in this field, allowing students to become specialized. However, biology students who are not involved in a bioinformatics program also need a solid toolbox of bioinformatics software and skills. Therefore, we have developed a completely online bioinformatics course for non-bioinformaticians, entitled "BIF-1901 Introduction a la bio-informatique et a ses outils (Introduction to bioinformatics and bioinformatics tools)," given by the Department of Biochemistry, Microbiology, and Bioinformatics of Universite Laval (Quebec City, Canada). This course requires neither a bioinformatics background nor specific skills in informatics. The underlying main goal was to produce a completely online up-to-date bioinformatics course, including practical exercises, with an intuitive pedagogical framework. The course, BIF-1901, was conceived to cover the three fundamental aspects of bioinformatics: (1) informatics, (2) biological sequence analysis, and (3) structural bioinformatics. This article discusses the content of the modules, the evaluations, the pedagogical framework, and the challenges inherent to a multidisciplinary, fully online course. © 2017 by The International Union of Biochemistry and Molecular Biology, 46(1):31-38, 2018.

Nucleoside triphosphate (NTP) ligands are of high biological importance and are essential for all life forms. A pre-requisite for them to participate in diverse biochemical processes is their recognition by diverse proteins. It is thus of great interest to understand the basis for such recognition in different proteins. Towards this, we have used a structural bioinformatics approach and analyze structures of 4677 NTP complexes available in Protein Data Bank (PDB). Binding sites were extracted and compared exhaustively using PocketMatch, a sensitive in-house site comparison algorithm, which resulted in grouping the entire dataset into 27 site-types. Each of these site-types represent a structural motif comprised of two or more residue conservations, derived using another in-house tool for superposing binding sites, PocketAlign. The 27 site-types could be grouped further into 9 super-types by considering partial similarities in the sites, which indicated that the individual site-types comprise different combinations of one or more site features. A scan across PDB using the 27 structural motifs determined the motifs to be specific to NTP binding sites, and a computational alanine mutagenesis indicated that residues identified to be highly conserved in the motifs are also most contributing to binding. Alternate orientations of the ligand in several site-types were observed and rationalized, indicating the possibility of some residues serving as anchors for NTP recognition. The presence of multiple site-types and the grouping of multiple folds into each site-type is strongly suggestive of convergent evolution. Knowledge of determinants obtained from this study will be useful for detecting function in unknown proteins. Proteins 2017; 85:1699-1712. © 2017 Wiley Periodicals, Inc.

Targeting non-native-ligand binding sites for potential investigative and therapeutic applications is an attractive strategy in proteins that share common native ligands, as in Rab1 protein. Rab1 is a subfamily member of Rab proteins, which are members of Ras GTPase superfamily. All Ras GTPase superfamily members bind to native ligands GTP and GDP, that switch on and off the proteins, respectively. Rab1 is physiologically essential for autophagy and transport between endoplasmic reticulum and Golgi apparatus. Pathologically, Rab1 is implicated in human cancers, a neurodegenerative disease, cardiomyopathy, and bacteria-caused infectious diseases. We have performed structural analyses on Rab1 protein using a unique ensemble of clustering methods, including multi-step principal component analysis, non-negative matrix factorization, and independent component analysis, to better identify representative Rab1 proteins than the application of a single clustering method alone does. We then used the identified representative Rab1 structures, resolved in multiple ligand states, to map their known and novel binding sites. We report here at least a novel binding site on Rab1, involving Rab1-specific residues that could be further explored for the rational design and development of investigative probes and/or therapeutic small molecules against the Rab1 protein. Proteins 2017; 85:859-871. © 2016 Wiley Periodicals, Inc.

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc.

Here we describe the updated MolProbity rotamer-library distributions derived from an order-of-magnitude larger and more stringently quality-filtered dataset of about 8000 (vs. 500) protein chains, and we explain the resulting changes and improvements to model validation as seen by users. To include only side-chains with satisfactory justification for their given conformation, we added residue-specific filters for electron-density value and model-to-density fit. The combined new protocol retains a million residues of data, while cleaning up false-positive noise in the multi- χ datapoint distributions. It enables unambiguous characterization of conformational clusters nearly 1000-fold less frequent than the most common ones. We describe examples of local interactions that favor these rare conformations, including the role of authentic covalent bond-angle deviations in enabling presumably strained side-chain conformations. Further, along with favored and outlier, an allowed category (0.3-2.0% occurrence in reference data) has been added, analogous to Ramachandran validation categories. The new rotamer distributions are used for current rotamer validation in MolProbity and PHENIX, and for rotamer choice in PHENIX model-building and refinement. The multi-dimensional χ distributions and Top8000 reference dataset are freely available on GitHub. These rotamers are termed "ultimate" because data sampling and quality are now fully adequate for this task, and also because we believe the future of conformational validation should integrate side-chain with backbone criteria. Proteins 2016; 84:1177-1189. © 2016 Wiley Periodicals, Inc.

Rho-kinase dimerization is essential for its kinase activity and biological function; disruption of the dimerization has recently been established as a new and promising therapeutics strategy for cerebrovascular malformation (CM). Based on Rho-kinase dimer crystal structure we herein combined in silico analysis and in vitro assay to rationally derive self-inhibitory peptides from the dimerization interface. Three peptides namely Hlp1, Hlp2 and Hlp3 were successfully designed that have potential capability to rebind at the dimerization domain of Rho-kinase. Molecular dynamics (MD) simulations revealed that these peptides are helically structured when bound to Rho-kinase, but exhibit partially intrinsic disorder in unbound state. Binding free energy (BFE) analysis suggested that the peptides have a satisfactory energetic profile to interact with Rho-kinase. The computational findings were then substantiated by fluorescence anisotropy assays, conforming that the helical peptides can bind tightly to Rho-kinase with affinity KD at micromolar level. These designed peptides are considered as lead molecular entities that can be further modified and optimized to obtain more potent peptidomimetics as self-competitors to disrupt Rho-kinase dimerization in CM.

Proteins and their domains evolve by a set of events commonly including the duplication and divergence of small motifs. The presence of short repetitive regions in domains has generally constituted a difficult case for structural domain classifications and their hierarchies. We developed the Evolutionary Classification Of protein Domains (ECOD) in part to implement a new schema for the classification of these types of proteins. Here we document the ways in which ECOD classifies proteins with small internal repeats, widespread functional motifs, and assemblies of small domain-like fragments in its evolutionary schema. We illustrate the ways in which the structural genomics project impacted the classification and characterization of new structural domains and sequence families over the decade.

Protein structure prediction, when construed as a fold recognition problem, is one of the most important applications of similarity search in bioinformatics. A new protein fold recognition method is reported which combines a single-source k diverse shortest path (SSKDSP) algorithm with Enrichment of Network Topological Similarity (ENTS) algorithm to search a graphic feature space generated using sequence similarity and structural similarity metrics. A modified, more efficient SSKDSP algorithm is developed to improve the performance of graph searching. The new implementation of the SSKDSP algorithm empirically requires 82% less memory and 61% less time than the current implementation, allowing for the analysis of larger, denser graphs. Furthermore, the statistical significance of fold ranking generated from SSKDSP is assessed using ENTS. The reported ENTS-SSKDSP algorithm outperforms original ENTS that uses random walk with restart (RWR) for the graph search as well as other state-of-the-art protein structure prediction algorithms HHSearch and Sparks-X, as evaluated by a benchmark of 600 query proteins. The reported methods may easily be extended to other similarity search problems in bioinformatics and chemoinformatics. The SSKDSP software is available at http://compsci.hunter.cuny.edu/~leixie/sskdsp.html.

Cysteine (Cys) is a critically important amino acid, serving a variety of functions within proteins including structural roles, catalysis, and regulation of function through post-translational modifications. Predicting which Cys residues are likely to be reactive is a very sought after feature. Few methods are currently available for the task, either based on evaluation of physicochemical features (e.g., pKa and exposure) or based on similarity with known instances. In this study, we developed an algorithm (named HAL-Cy) which blends previous work with novel implementations to identify reactive Cys from nonreactive. HAL-Cy present two major components: (i) an energy based part, rooted on the evaluation of H-bond network contributions and (ii) a knowledge based part, composed of different profiling approaches (including a newly developed weighting matrix for sequence profiling). In our evaluations, HAL-Cy provided significantly improved performances, as tested in comparisons with existing approaches. We implemented our algorithm in a web service (Cy-preds), the ultimate product of our work; we provided it with a variety of additional features, tools, and options: Cy-preds is capable of performing fully automated calculations for a thorough analysis of Cys reactivity in proteins, ranging from reactivity predictions (e.g., with HAL-Cy) to functional characterization. We believe it represents an original, effective, and very useful addition to the current array of tools available to scientists involved in redox biology, Cys biochemistry, and structural bioinformatics.

We present an in silico method to estimate the contribution of each residue in a protein to its overall stability using three database-derived statistical potentials that are based on inter-residue distances, backbone torsion angles and solvent accessibility, respectively. Residues that contribute very unfavorably to the folding free energy are defined as stability weaknesses, whereas residues that show a highly stabilizing contribution are called stability strengths. Strengths and/or weaknesses on residues that are in spatial contact are clustered into 3-dimensional (3D) stability patches. The identification and analysis of strength- and weakness-containing regions in a protein may reveal structural or functional characteristics, and/or interesting spots to introduce mutations. To illustrate the power of our method, we apply it to bovine seminal ribonuclease. This enzyme catalyzes the degradation of RNA strands, and has the peculiarity of undergoing 3D domain swapping in physiological conditions. The weaknesses and strengths were compared among the monomeric, dimeric and swapped dimeric forms. We identified weaknesses among the catalytic residues and a mixture of weaknesses and strengths among the substrate-binding residues in the three forms. In the regions involved in 3D swapping, we observed an accumulation of weaknesses in the monomer, which disappear in the dimer and especially in the swapped dimer. Moreover, monomeric homologous proteins were found to exhibit less weaknesses in these regions, whereas mutants known to favor unswapped dimerization appear stabilized in this form. Our method has several perspectives for functional annotation, rational prediction of targeted mutations, and mapping of stability changes upon conformational rearrangements.

Nonribosomal peptide synthetases (NRPSs) synthesize a diverse array of bioactive small peptides, many of which are used in medicine. There is considerable interest in predicting NRPS substrate specificity in order to facilitate investigation of the many "cryptic" NRPS genes that have not been linked to any known product. However, the current sequence similarity-based methods are unable to produce reliable predictions when there is a lack of prior specificity data, which is a particular problem for fungal NRPSs. We conducted virtual screening on the specificity-determining domain of NRPSs, the adenylation domain, and found that virtual screening using experimentally determined structures results in good enrichment of the cognate substrate. Our results indicate that the conformation of the adenylation domain and in particular the conformation of a key conserved aromatic residue is important in determining the success of the virtual screening. When homology models of NRPS adenylation domains of known specificity, rather than experimentally determined structures, were built and used for virtual screening, good enrichment of the cognate substrate was also achieved in many cases. However, the accuracy of the models was key to the reliability of the predictions and there was a large variation in the results when different models of the same domain were used. This virtual screening approach is promising and is able to produce enrichment of the cognate substrates in many cases, but improvements in building and assessing homology models are required before the approach can be reliably applied to these models.

We have developed an online NMR / X-ray Structure Pair Data Repository. The NIGMS Protein Structure Initiative (PSI) has provided many valuable reagents, 3D structures, and technologies for structural biology. The Northeast Structural Genomics Consortium was one of several PSI centers. NESG used both X-ray crystallography and NMR spectroscopy for protein structure determination. A key goal of the PSI was to provide experimental structures for at least one representative of each of hundreds of targeted protein domain families. In some cases, structures for identical (or nearly identical) constructs were determined by both NMR and X-ray crystallography. NMR spectroscopy and X-ray diffraction data for 41 of these "NMR / X-ray" structure pairs determined using conventional triple-resonance NMR methods with extensive sidechain resonance assignments have been organized in an online NMR / X-ray Structure Pair Data Repository. In addition, several NMR data sets for perdeuterated, methyl-protonated protein samples are included in this repository. As an example of the utility of this repository, these data were used to revisit questions about the precision and accuracy of protein NMR structures first outlined by Levy and coworkers several years ago (Andrec et al., Proteins 2007;69:449-465). These results demonstrate that the agreement between NMR and X-ray crystal structures is improved using modern methods of protein NMR spectroscopy. The NMR / X-ray Structure Pair Data Repository will provide a valuable resource for new computational NMR methods development.

Proteins have many different functions in biological systems, and the molecular functions of proteins are dependent on their three-dimensional structures. Mapping protein structures is therefore an important strategy in understanding gene and protein function. Structural proteomics or structural genomics refers to systematic efforts to functionally annotate protein molecular structures of whole or selected parts of genomes and/or proteomes. Structural proteomics studies have significantly added to our knowledge of protein structures over the past few years and a large fraction of available protein structures in public databases result from high-throughput structural proteomics studies. Although structural proteomics techniques are continually being improved, significant challenges remain in protein expression and crystallisation and in particular for solving protein structures for challenging classes of protein such as membrane proteins. Key Concepts Protein function is directly linked to the three-dimensional structure of the protein. Structural proteomics refers to large-scale mapping of protein structures. Techniques such as protein crystallisation and X-ray bombardment allow the three-dimensional arrangement of atoms in protein molecules to be determined. Technical and biological challenges remain in structural proteomics – in particular for certain classes of proteins such as proteins that span membranes. Large-scale structural proteomics is an enabling technology for systems biology. Keywords: structural genomics; high throughput; protein; X-ray; nuclear magnetic resonance methods; crystallography; systems biology

Finding the root mean sum of squared deviations (RMSDs) between two coordinate vectors that correspond to the rigid body motion of a macromolecule is an important problem in structural bioinformatics, computational chemistry, and molecular modeling. Standard algorithms compute the RMSD with time proportional to the number of atoms in the molecule. Here, we present RigidRMSD, a new algorithm that determines a set of RMSDs corresponding to a set of rigid body motions of a macromolecule in constant time with respect to the number of atoms in the molecule. Our algorithm is particularly useful for rigid body modeling applications, such as rigid body docking, and also for high-throughput analysis of rigid body modeling and simulation results. We also introduce a constant-time rotation RMSD as a similarity measure for rigid molecules. A C++ implementation of our algorithm is available at http://nano-d.inrialpes.fr/software/RigidRMSD.

Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category.

Structure comparison is widely used to quantify protein relationships. Although there are several approaches to calculate structural similarity, specifying significance thresholds for similarity metrics is difficult due to the inherent likeness of common secondary structure elements. In this study, metal co-factor location is used to assess the biological relevance of structural alignments. The distance between the centroids of bound co-factors adds a chemical and function-relevant constraint to the structural superimposition of two proteins. This additional dimension can be used to define cut-off values for discriminating valid and spurious alignments in large alignment sets. The hypothesis underlying our approach is that metal coordination sites constrain structural evolution, thus revealing functional relationships between distantly related proteins. A comparison of three related nitrogenases shows the sequence and fold constraints imposed on the protein structures up to 18 A away from the centers of their bound metal clusters. Proteins 2014; 82:648–656. © 2013 Wiley Periodicals, Inc.

β-secretase (BACE-1) is a potential target for the treatment of Alzheimer's disease (AD). Despite its potential, only few compounds targeting BACE have entered the clinical trials. Herein, we describe the identification of Gefitinib as a potential lead compound for BACE through an integrated approach of structural bioinformatics analysis, experimental assessment and computational analysis. In particular, we performed ELISA and western analysis to assess the effect of Gefitinib using N2a human APP695 cells. In addition, we investigated the binding mechanism of Gefitinib with BACE through molecular docking coupled with molecular dynamics simulations. The computational analyses revealed that hydrophobic contact is a major contributing factor to the binding of Gefitinib with BACE. The results obtained in the study have rendered Gefitinib as a putative lead compound for BACE. Further optimization studies are warranted to improve its potency and pharmacological properties against BACE for potential AD treatment.

Accurate prediction of DNA-binding residues has become a problem of increasing importance in structural bioinformatics. Here, we presented DNABind, a novel hybrid algorithm for identifying these crucial residues by exploiting the complementarity between machine learning- and template-based methods. Our machine learning-based method was based on the probabilistic combination of a structure-based and a sequence-based predictor, both of which were implemented using support vector machines algorithms. The former included our well-designed structural features, such as solvent accessibility, local geometry, topological features, and relative positions, which can effectively quantify the difference between DNA-binding and nonbinding residues. The latter combined evolutionary conservation features with three other sequence attributes. Our template-based method depended on structural alignment and utilized the template structure from known protein-DNA complexes to infer DNA-binding residues. We showed that the template method had excellent performance when reliable templates were found for the query proteins but tended to be strongly influenced by the template quality as well as the conformational changes upon DNA binding. In contrast, the machine learning approach yielded better performance when high-quality templates were not available (about 1/3 cases in our dataset) or the query protein was subject to intensive transformation changes upon DNA binding. Our extensive experiments indicated that the hybrid approach can distinctly improve the performance of the individual methods for both bound and unbound structures. DNABind also significantly outperformed the state-of-art algorithms by around 10% in terms of Matthews's correlation coefficient. The proposed methodology could also have wide application in various protein functional site annotations. DNABind is freely available at http://mleg.cse.sc.edu/DNABind/.

Protein-protein interactions are central to cell function. In order to fully understand these interactions, one has to elucidate the three-dimensional structures of the underlying complexes. While experimental methods have advanced significantly in the last decade, there are still few structures of protein complexes in the Protein Data Bank. Reliable computational techniques are required to fill in this gap. Better understanding of protein-protein interactions has also opened a large number of potential targets for the pharmaceutical industry, which previously viewed these interactions as “undruggable”. In this review, we focus on the algorithms developed by the Tel Aviv University Structural Bioinformatics (Bioinfo3D) Lab to model protein-protein interactions, and on a preliminary attempt to search for peptide inhibitors for these interactions. All the algorithms presented are among the fastest available today and can be accessed via the group web server.

Coarse-grained models for protein structure are increasingly used in simulations and structural bioinformatics. In this study, we evaluated the effectiveness of three granularities of protein representation based on their ability to discriminate between correctly folded native structures and incorrectly folded decoy structures. The three levels of representation used one bead per amino acid (coarse), two beads per amino acid (medium), and all atoms (fine). Multiple structure features were compared at each representation level including two-body interactions, three-body interactions, solvent exposure, contact numbers, and angle bending. In most cases, the all-atom level was most successful at discriminating decoys, but the two-bead level provided a good compromise between the number of model parameters which must be estimated and the accuracy achieved. The most effective feature type appeared to be two-body interactions. Considering three-body interactions increased accuracy only marginally when all atoms were used and not at all in medium and coarse representations. Though two-body interactions were most effective for the coarse representations, the accuracy loss for using only solvent exposure or contact number was proportionally less at these levels than in the all-atom representation. We propose an optimization method capable of selecting bead types of different granularities to create a mixed representation of the protein. We illustrate its behavior on decoy discrimination and discuss implications for data-driven protein model selection.

Angiogenin (Ang) is a potent inducer of neovascularization. Point mutations in human Ang have been linked to cancer progression and two neurodegenerative diseases: amyotrophic lateral sclerosis and Parkinson's disease. Intensive structural and functional analyses of Ang have been paramount in assigning functions to this novel homologue of bovine pancreatic RNase A. However, inhibitor-binding studies with crystalline Ang (for designing potential anti-cancer drugs) have been hampered as a result of the inaccessibility of the active site. Experiments with the murine homologues of Ang have not only overcome the obvious practical limitations encountered when studying the role of a human protein in healthy individuals, but also the crystal structures of murine angiogenins (mAng and mAng-4) have revealed themselves to have greater potential for the visualization of small-molecule inhibitor binding at the active site. In the present study, we report the crystal structures of two more murine Ang paralogues, mAng-2 and mAng-3, at 1.6 and 1.8 A resolution, respectively. These constitute the first crystal structures of an Ang with a zinc ion bound at the active site and provide some insight into the possible mode of inhibition of the ribonucleolytic activity of the enzyme by these divalent cations. Both structures show that the residues forming the putative P1, B1 and B2 subsites occupy positions similar to their counterparts in human Ang and are likely to have conserved roles. However, a less obtrusive conformation of the C-terminal segment in mAng-3 and the presence of a sulfate ion in the B1 subsite of mAng-2 suggest that these proteins have the potential to be used for inhibitor-binding studies. We also discuss the biological relevance of the structural similarities and differences between the different Ang homologues. Database The atomic coordinates and structure factors for mAng-2 (3ZBV) and mAng-3 (3ZBW) have been deposited in the Protein Data Bank, Research Collaboratory for Structural Bioinformatics, Rutgers University, New Brunswick, NJ, USA (http://www.rcsb.org/). Structured digital abstract mAng2 and mAng3 bind by x-ray crystallography (View interaction)

Protein interactions have been at the focus of computational biology in recent years. In particular, interest has come from two different communities – structural and systems biology. Here, we will discuss key systems and structural biology methods that have been used for analysis and prediction of protein–protein interactions and the insight these approaches have provided on the nature and organization of protein–protein interactions inside cells.

Structural bioinformatics of membrane proteins is still in its infancy, and the picture of their fold space is only beginning to emerge. Because only a handful of three-dimensional structures are available, sequence comparison and structure prediction remain the main tools for investigating sequence-structure relationships in membrane protein families. Here we present a comprehensive analysis of the structural families corresponding to α-helical membrane proteins with at least three transmembrane helices. The new version of our CAMPS database (CAMPS 2.0) covers nearly 1300 eukaryotic, prokaryotic, and viral genomes. Using an advanced classification procedure, which is based on high-order hidden Markov models and considers both sequence similarity as well as the number of transmembrane helices and loop lengths, we identified 1353 structurally homogeneous clusters roughly corresponding to membrane protein folds. Only 53 clusters are associated with experimentally determined three-dimensional structures, and for these clusters CAMPS is in reasonable agreement with structure-based classification approaches such as SCOP and CATH. We therefore estimate that ∼1300 structures would need to be determined to provide a sufficient structural coverage of polytopic membrane proteins. CAMPS 2.0 is available at http://webclu.bio.wzw.tum.de/CAMPS2.0/.

Computational investigation of protein functions is one of the most urgent and demanding tasks in the field of structural bioinformatics. Exhaustive pairwise comparison of known and putative ligand-binding sites, across protein families and folds, is essential in elucidating the biological functions and evolutionary relationships of proteins. Given the vast amounts of data available now, existing 3D structural comparison methods are not adequate due to their computation time complexity. In this article, we propose a new bit string representation of binding sites called structural sketches, which is obtained by random projections of triplet descriptors. It allows us to use ultra-fast all-pair similarity search methods for strings with strictly controlled error rates. Exhaustive comparison of 1.2 million known and putative binding sites finished in ∼30 h on a single core to yield 88 million similar binding site pairs. Careful investigation of 3.5 million pairs verified by TM-align revealed several notable analogous sites across distinct protein families or folds. In particular, we succeeded in finding highly plausible functions of several pockets via strong structural analogies. These results indicate that our method is a promising tool for functional annotation of binding sites derived from structural genomics projects.

The purpose of this manuscript is threefold: (1) to describe an update to DockoMatic that allows the user to generate cyclic peptide analog structure files based on protein database (pdb) files, (2) to test the accuracy of the peptide analog structure generation utility, and (3) to evaluate the high throughput capacity of DockoMatic. The DockoMatic graphical user interface interfaces with the software program Treepack to create user defined peptide analogs. To validate this approach, DockoMatic produced cyclic peptide analogs were tested for three-dimensional structure consistency and binding affinity against four experimentally determined peptide structure files available in the Research Collaboratory for Structural Bioinformatics database. The peptides used to evaluate this new functionality were alpha-conotoxins ImI, PnIA, and their published analogs. Peptide analogs were generated by DockoMatic and tested for their ability to bind to X-ray crystal structure models of the acetylcholine binding protein originating from Aplysia californica. The results, consisting of more than 300 simulations, demonstrate that DockoMatic predicts the binding energy of peptide structures to within 3.5 kcal mol(-1), and the orientation of bound ligand compares to within 1.8 Å root mean square deviation for ligand structures as compared to experimental data. Evaluation of high throughput virtual screening capacity demonstrated that Dockomatic can collect, evaluate, and summarize the output of 10,000 AutoDock jobs in less than 2 hours of computational time, while 100,000 jobs requires approximately 15 hours and 1,000,000 jobs is estimated to take up to a week.

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) supports scientific research and education by providing an essential resource of information about biomolecular structures. As a member of the Worldwide Protein Data Bank (wwPDB), the RCSB PDB curates and annotates the data about the experimentally determined three-dimensional structures of proteins and nucleic acids that are deposited into the PDB archive. The RCSB PDB also provides online resources to access the data in the archive, including a relational database supporting simple and complex query and reporting, visualization tools, structure-sequence comparison tools, access to the associated literature, and educational services. In the 11 years (1999–2010) since RCSB PDB has been in operation, the amount of data in the archive has increased six-fold, along with an increase in the complexity of structures being determined and in the number of experimental methods used. The evolution required by RCSB PDB to meet these challenges provides insight into the motivation and challenges of developing and maintaining a major biological resource, particularly the one used in understanding the molecular details of living systems in both normal and disease states. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 782–789 DOI: 10.1002/wcms.57

Salt bridges occur frequently in proteins, providing conformational specificity and contributing to molecular recognition and catalysis. We present a comprehensive analysis of these interactions in protein structures by surveying a large database of protein structures. Salt bridges between Asp or Glu and His, Arg, or Lys display extremely well-defined geometric preferences. Several previously observed preferences are confirmed, and others that were previously unrecognized are discovered. Salt bridges are explored for their preferences for different separations in sequence and in space, geometric preferences within proteins and at protein-protein interfaces, co-operativity in networked salt bridges, inclusion within metal-binding sites, preference for acidic electrons, apparent conformational side chain entropy reduction on formation, and degree of burial. Salt bridges occur far more frequently between residues at close than distant sequence separations, but, at close distances, there remain strong preferences for salt bridges at specific separations. Specific types of complex salt bridges, involving three or more members, are also discovered. As we observe a strong relationship between the propensity to form a salt bridge and the placement of salt-bridging residues in protein sequences, we discuss the role that salt bridges might play in kinetically influencing protein folding and thermodynamically stabilizing the native conformation. We also develop a quantitative method to select appropriate crystal structure resolution and B-factor cutoffs. Detailed knowledge of these geometric and sequence dependences should aid de novo design and prediction algorithms.

The aim of the Structural Biology Extensible Visualization Scripting Language (SBEVSL) project is to allow users who are experts in one scripting language to use that language in a second molecular visualization environment without requiring the user to learn a new scripting language. ConSCRIPT, the first SBEVSL release, is a plug-in for PyMOL that accepts RasMol scripting commands either as premade scripts or as line-by-line entries from PyMOL's own command line. The plug-in is available for download at http://sourceforge.net/projects/sbevsl/files in the ConSCRIPT folder. Biochemistry and Molecular Biology Education Vol. 38, No. 6, pp. 419-422, 2010.

Protein crystals contain two different types of interfaces: biologically relevant ones, observed in protein-protein complexes and oligomeric proteins, and nonspecific ones, corresponding to crystal lattice contacts. Because of the increasing complexity of the objects being tackled in structural biology, distinguishing biological contacts from crystal contacts is not always a trivial task and can lead to wrong interpretation of macromolecular structures. We devised an approach (CRK, core-rim K(a)/K(s) ratio) for distinguishing biologically relevant interfaces from nonspecific ones. Given a protein-protein interface, CRK finds a set of homologs to the sequences of the proteins involved in the interface, retrieves and aligns the corresponding coding sequences, on which it carries out a residue-by-residue K(a)/K(s) ratio (omega) calculation. It divides interface residues into a "rim" and a "core" set and analyzes the selection pressure on the residues belonging to the two sets. We developed and tested CRK on different datasets and test cases, consisting of biologically relevant contacts, nonspecific ones or of both types. The method proves very effective in distinguishing the two categories of interfaces, with an overall accuracy rate of 84%. As it relies on different principles when compared with existing tools, CRK is optimally suited to be used in combination with them. In addition, CRK has potential applications in the validation of structures of oligomeric proteins and protein complexes.

Protein structure alignment methods are essential for many different challenges in protein science, such as the determination of relations between proteins in the fold space or the analysis and prediction of their biological function. A number of different pairwise and multiple structure alignment (MStA) programs have been developed and provided to the community. Prior knowledge of the expected alignment accuracy is desirable for the user of such tools. To retrieve an estimate of the performance of current structure alignment methods, we compiled a test suite taken from literature and the SISYPHUS database consisting of proteins that are difficult to align. Subsequently, different MStA programs were evaluated regarding alignment correctness and general limitations. The analysis shows that there are large differences in the success between the methods in terms of applicability and correctness. The latter ranges from 44 to 75% correct core positions. Taking only the best method result per test case this number increases to 84%. We conclude that the methods available are applicable to difficult cases, but also that there is still room for improvements in both, practicability and alignment correctness. An approach that combines the currently available methods supported by a proper score would be useful. Until then, a user should not rely on just a single program.

SWISS-MODEL pioneered the field of automated modeling as the first protein modeling service on the Internet. In combination with the visualization tool Swiss-PdbViewer, the Internet-based Workspace and the SWISS-MODEL Repository, it provides a fully integrated sequence to structure analysis and modeling platform. This computational environment is made freely available to the scientific community with the aim to hide the computational complexity of structural bioinformatics and encourage bench scientists to make use of the ever-increasing structural information available. Indeed, over the last decade, the availability of structural information has significantly increased for many organisms as a direct consequence of the complementary nature of comparative protein modeling and experimental structure determination. This has a very positive and enabling impact on many different applications in biomedical research as described in this paper.

The Structural Bioinformatics Core Facility at the University of North Carolina at Chapel Hill (SBI Core) assists researchers university-wide in computational structural biology techniques and incorporating structural biology/bioinformatics into their grants and publications. The SBI Core works with a diverse population of researchers from numerous departments and provides support to an ever-changing body of research. The computational biology services provided by the SBI Core are data-intensive and use a diverse and distributed set of applications for processing, data storage, and data management. As the amount of data and number of projects have increased, the SBI Core requires an effective strategy for managing data and facilitating data sharing between the SBI Core and the researchers it assists. The UNC-CH Health Sciences Library (HSL) has begun a collaborative project with the SBI Core to identify the crucial data management needs and to envision new roles for the library in e-science and data management. In partnership, the SBI Core and the HSL have identified major obstacles in data sharing, data management, and data access. Furthermore, the SBI Core and the HSL will develop solutions in which the library facilitates collaboration among campus resources and matches unmet needs to external resources. One of the library's goals in this proof-of-concept project with the SBI Core is to become a central campus resource for research support and data management.

We have developed a soft energy function, termed GEMSCORE, for the protein structure prediction, which is one of emergent issues in the computational biology. The GEMSORE consists of the van der Waals, the hydrogen-bonding potential and the solvent potential with 12 parameters which are optimized by using a generic evolutionary method. The GEMSCORE is able to successfully identify 86 native proteins among 96 target proteins on six decoy sets from more 70,000 near-native structures. For these six benchmark datasets, the predictive performance of the GEMSCORE, based on native structure ranking and Z-scores, was superior to eight other energy functions. Our method is based solely on a simple and linear function and thus is considerably faster than other methods that rely on the additional complex calculations. In addition, the GEMSCORE recognized 17 and 2 native structures as the first and the second rank, respectively, among 21 targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). These results suggest that the GEMSCORE is fast and performs well to discriminate between native and nonnative structures from thousands of protein structure candidates. We believe that GEMSCORE is robust and should be a useful energy function for the protein structure prediction. © 2008 Wiley Periodicals, Inc. J Comput Chem 2008

Protein–protein interactions networks has come to be a buzzword associated with nets containing edges that represent a pair of interacting proteins (e.g. hormone-receptor, enzyme-inhibitor, antigen-antibody, and a subset of multichain biological machines). Yet, each such interaction composes its own unique network, in which vertices represent amino acid residues, and edges represent atomic contacts. Recent studies have shown that analyses of the data encapsulated in these detailed networks may impact predictions of structure–function correlation. Here, we study homologous families of protein–protein interfaces, which share the same fold but vary in sequence. In this context, we address what properties of the network are shared among relatives with different sequences (and hence different atomic interactions) and which are not. Herein, we develop the general mathematical framework needed to compare the modularity of homologous networks. We then apply this analysis to the structural data of a few interface families, including hemoglobin α–β, growth hormone-receptor, and Serine protease-inhibitor. Our results suggest that interface modularity is an evolutionarily conserved property. Hence, protein–protein interfaces can be clustered down to a few modules, with the boundaries being evolutionarily conserved along homologous complexes. This suggests that protein engineering of protein–protein binding sites may be simplified by varying each module, but retaining the overall modularity of the interface. Proteins 2008. © 2007 Wiley-Liss, Inc.

Revealing the fundamental principles of protein interactions is essential for the basic knowledge of molecular processes and designing better predictive tools. Protein docking procedures allow systematic sampling of intermolecular energy landscapes, revealing the distribution of energy basins and their characteristics. A systematic search docking procedure GRAMM-X was applied to a comprehensive nonredundant database of nonobligate protein–protein complexes to determine the size of the intermolecular energy funnel. The unbound structures were simulated using rotamer library. The procedure generated grid-based matches, based on a smoothed Lennard-Jones potential, and minimized them off the grid with the same potential. The minimization generated a distribution of distances, based on a variety of metrics, between the grid-based and the minimized matches. The metric selected for the analysis, ligand interface RMSD, provided three independent estimates of the funnel size: based on the distribution amplitude for the near-native matches, deviation from random, and correlation with the energy values. The three methods converge to similar estimates of ∼6–8 A ligand interface RMSD. The results indicated dependence of the funnel size on the type of the complex (smaller for antigen–antibody, medium for enzyme–inhibitor, and larger for the rest of the complexes) and the funnel size correlation with the size of the interface. Guidelines for the optimal sampling of docking coordinates, based on the funnel size estimates, were explored. Proteins 2008. © 2008 Wiley-Liss, Inc.