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The process of heme binding to a protein is prevalent in almost all forms of life to control many important biological properties, such as O 2 -binding, electron transfer, gas sensing or to build catalytic power. In these cases, heme typically binds tightly (irreversibly) to a protein in a discrete heme binding pocket, with one or two heme ligands provided most commonly to the heme iron by His, Cys or Tyr residues. Heme binding can also be used as a regulatory mechanism, for example in transcriptional regulation or ion channel control. When used as a regulator, heme binds more weakly, with different heme ligations and without the need for a discrete heme pocket. This makes the characterization of heme regulatory proteins difficult, and new approaches are needed to predict and understand the heme-protein interactions. We apply a modified version of the ProFunc bioinformatics tool to identify heme-binding sites in a test set of heme-dependent regulatory proteins taken from the Protein Data Bank and AlphaFold models. The potential heme binding sites identified can be easily visualized in PyMol and, if necessary, optimized with RosettaDOCK. We demonstrate that the methodology can be used to identify heme-binding sites in proteins, including in cases where there is no crystal structure available, but the methodology is more accurate when the quality of the structural information is high. The ProFunc tool, with the modification used in this work, is publicly available at https://www.ebi.ac.uk/thornton-srv/databases/profunc and can be readily adopted for the examination of new heme binding targets., Competing Interests: Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140 214 unrelated UK Biobank (UKB) participants found that each of them carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade gene (DGG) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12 367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18 410 nodes and 571 917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1 or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants., (© 2023 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.)

Constrained Coding Regions (CCRs) in the human genome have been derived from DNA sequencing data of large cohorts of healthy control populations, available in the Genome Aggregation Database (gnomAD) [1]. They identify regions depleted of protein-changing variants and thus identify segments of the genome that have been constrained during human evolution. By mapping these DNA-defined regions from genomic coordinates onto the corresponding protein positions and combining this information with protein annotations, we have explored the distribution of CCRs and compared their co-occurrence with different protein functional features, previously annotated at the amino acid level in public databases. As expected, our results reveal that functional amino acids involved in interactions with DNA/RNA, protein-protein contacts and catalytic sites are the protein features most likely to be highly constrained for variation in the control population. More surprisingly, we also found that linear motifs, linear interacting peptides (LIPs), disorder-order transitions upon binding with other protein partners and liquid-liquid phase separating (LLPS) regions are also strongly associated with high constraint for variability. We also compared intra-species constraints in the human CCRs with inter-species conservation and functional residues to explore how such CCRs may contribute to the analysis of protein variants. As has been previously observed, CCRs are only weakly correlated with conservation, suggesting that intraspecies constraints complement interspecies conservation and can provide more information to interpret variant effects., Competing Interests: Declaration of Competing Interest, (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

PDBsum1 is a standalone set of programs to perform the same structural analyses as provided by the PDBsum web server (https://www.ebi.ac.uk/pdbsum). The server has pages for every entry in the Protein Data Bank (PDB) and can also process user-uploaded PDB files, returning a password-protected set of pages that are retained for around 3 months. The standalone version described here allows for in-house processing and indefinite retention of the results. All data files and images are pre-generated, rather than on-the-fly as in the web version, so can be easily accessed. The program runs on Linux, Windows, and mac operating systems and is freely available for academic use at https://www.ebi.ac.uk/thornton-srv/software/PDBsum1., (© 2022 The Author. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research., (© 2022. The Author(s).)

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS-CoV-2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum., (© 2021 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

DNA-Damage Response (DDR) proteins are crucial for maintaining the integrity of the genome by identifying and repairing errors in DNA. Variants affecting their function can have severe consequences since failure to repair damaged DNA can result in cells turning cancerous. Here, we compare germline and somatic variants in DDR genes, specifically looking at their locations in the corresponding three-dimensional (3D) structures, Pfam domains, and protein-protein interaction interfaces. We show that somatic variants in metastatic cases are more likely to be found in Pfam domains and protein interaction interfaces than are pathogenic germline variants or variants of unknown significance (VUS). We also show that there are hotspots in the structures of ATM and BRCA2 proteins where pathogenic germline, and recurrent somatic variants from primary and metastatic tumours, cluster together in 3D. Moreover, in the ATM, BRCA1 and BRCA2 genes from prostate cancer patients, the distributions of germline benign, pathogenic, VUS, and recurrent somatic variants differ across Pfam domains. Together, these results provide a better characterisation of the most recurrent affected regions in DDRs and could help in the understanding of individual susceptibility to tumour development., (© 2021. The Author(s).)

The prediction of peptide binders to Major Histocompatibility Complex (MHC) class II receptors is of great interest to study autoimmune diseases and for vaccine development. Most approaches predict the affinities using sequence-based models trained on experimental data and multiple alignments from known peptide substrates. However, detecting activity differences caused by single-point mutations is a challenging task. In this work, we used interactions calculated from simulations to build scoring matrices for quickly estimating binding differences by single-point mutations. We modelled a set of 837 peptides bound to an MHC class II allele, and optimized the sampling of the conformations using the Rosetta backrub method by comparing the results to molecular dynamics simulations. From the dynamic trajectories of each complex, we averaged and compared structural observables for each amino acid at each position of the 9°mer peptide core region. With this information, we generated the scoring-matrices to predict the sign of the binding differences. We then compared the performance of the best scoring-matrix to different computational methodologies that range in computational costs. Overall, the prediction of the activity differences caused by single mutated peptides was lower than 60% for all the methods. However, the developed scoring-matrix in combination with existing methods reports an increase in the performance, up to 86% with a scoring method that uses molecular dynamics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Ochoa, Laskowski, Thornton and Cossio.)

Background: Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. However, depending on the family and molecular function, they can also display substrate promiscuity which can also be essential. Databases compiling specificity matrices derived from experimental assays have provided valuable insights into protease substrate recognition. Despite this, there are still gaps in our knowledge of the structural determinants. Here, we compile a set of protease crystal structures with bound peptide-like ligands to create a protocol for modelling substrates bound to protease structures, and for studying observables associated to the binding recognition., Results: As an application, we modelled a subset of protease-peptide complexes for which experimental cleavage data are available to compare with informational entropies obtained from protease-specificity matrices. The modelled complexes were subjected to conformational sampling using the Backrub method in Rosetta, and multiple observables from the simulations were calculated and compared per peptide position. We found that some of the calculated structural observables, such as the relative accessible surface area and the interaction energy, can help characterize a protease's substrate recognition, giving insights for the potential prediction of novel substrates by combining additional approaches., Conclusion: Overall, our approach provides a repository of protease structures with annotated data, and an open source computational protocol to reproduce the modelling and dynamic analysis of the protease-peptide complexes.

VarSite is a web server mapping known disease-associated variants from UniProt and ClinVar, together with natural variants from gnomAD, onto protein 3D structures in the Protein Data Bank. The analyses are primarily image-based and provide both an overview for each human protein, as well as a report for any specific variant of interest. The information can be useful in assessing whether a given variant might be pathogenic or benign. The structural annotations for each position in the protein include protein secondary structure, interactions with ligand, metal, DNA/RNA, or other protein, and various measures of a given variant's possible impact on the protein's function. The 3D locations of the disease-associated variants can be viewed interactively via the 3dmol.js JavaScript viewer, as well as in RasMol and PyMOL. Users can search for specific variants, or sets of variants, by providing the DNA coordinates of the base change(s) of interest. Additionally, various agglomerative analyses are given, such as the mapping of disease and natural variants onto specific Pfam or CATH domains. The server is freely accessible to all at: https://www.ebi.ac.uk/thornton-srv/databases/VarSite., (© 2019 The Authors. Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.)

Motivation: Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence., Results: Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information., Availability and Implementation: https://www.ebi.ac.uk/thornton-srv/databases/VarMap., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press.)

Objectives: Numerous scales assessing the aesthetics of cleft lip repair exist. Most, including the Asher-McDade scale, use frontal and lateral views, while neglecting a basal view. We believe this view is important for properly assessing the aesthetics of repair. In this study, we evaluate the basal view in comparison to the Asher-McDade scale., Methods: This scale was based on progressive columellar shortening and alar flaring/slumping. A panel of 4 plastic surgeons applied the basal and Asher-McDade scales to pictures of patients with repaired unilateral cleft lip in 56 multiethnic participants aged 5 years to 18 years; images were scored from 1 to 5 (best). Statistical analysis was done via Spearman correlation., Results: Scores from plastic surgeons correlated strongly for each view. There was moderate correlation for the basal view with both nasal form and deviation scores ( P < .05). As expected, there were no strong correlations between the basal view and vermillion border or profile., Discussion: The Asher-McDade scale has been used to evaluate cleft lift repairs but is limited due to its subjective nature. The basal view scale grades these repairs by using a scale of progressive columellar shortening and alar flaring/slumping, which provides an opportunity for quantification and standardization. Our results show that the basal view correlates with the Asher-McDade scale among raters, thus providing an objective and validated measure of cleft lip repair.

PDBsum is a web server providing structural information on the entries in the Protein Data Bank (PDB). The analyses are primarily image-based and include protein secondary structure, protein-ligand and protein-DNA interactions, PROCHECK analyses of structural quality, and many others. The 3D structures can be viewed interactively in RasMol, PyMOL, and a JavaScript viewer called 3Dmol.js. Users can upload their own PDB files and obtain a set of password-protected PDBsum analyses for each. The server is freely accessible to all at: http://www.ebi.ac.uk/pdbsum., (© 2017 The Protein Society.)

Background: Syntaxin-binding protein 1, encoded by STXBP1 , is highly expressed in the brain and involved in fusing synaptic vesicles with the plasma membrane. Studies have shown that pathogenic loss-of-function variants in this gene result in various types of epilepsies, mostly beginning early in life. We were interested to model pathogenic missense variants on the protein structure to investigate the mechanism of pathogenicity and genotype-phenotype correlations., Methods: We report 11 patients with pathogenic de novo mutations in STXBP1 identified in the first 4293 trios of the Deciphering Developmental Disorder (DDD) study, including six missense variants. We analyzed the structural locations of the pathogenic missense variants from this study and the literature, as well as population missense variants extracted from Exome Aggregation Consortium (ExAC)., Results: Pathogenic variants are significantly more likely to occur at highly conserved locations than population variants, and be buried inside the protein domain. Pathogenic mutations are also more likely to destabilize the domain structure compared with population variants, increasing the proportion of (partially) unfolded domains that are prone to aggregation or degradation. We were unable to detect any genotype-phenotype correlation, but unlike previously reported cases, most of the DDD patients with STXBP1 pathogenic variants did not present with very early-onset or severe epilepsy and encephalopathy, though all have developmental delay with intellectual disability and most display behavioral problems and suffered seizures in later childhood., Conclusion: Variants across STXBP1 that cause loss of function can result in severe intellectual disability with or without seizures, consistent with a haploinsufficiency mechanism. Pathogenic missense mutations act through destabilization of the protein domain, making it prone to aggregation or degradation. The presence or absence of early seizures may reflect ascertainment bias in the literature as well as the broad recruitment strategy of the DDD study.

The ProFunc web server is a tool for helping identify the function of a given protein whose 3D coordinates have been experimentally determined or homology modeled. It uses a cocktail of both sequence- and structure-based methods to identify matches to other proteins that may, in turn, suggest the query protein's most likely function. The server was originally developed to aid the worldwide structural genomics effort at the start of the millennium. It accepts a file containing the protein's 3D coordinates in PDB format, and, when processing is complete, sends an email containing a link to the password-protected result pages. The results include an at-a-glance summary, as well as separate pages containing more detailed analyses. The server can be found at: http://www.ebi.ac.uk/thornton-srv/databases/profunc .

Flavin-dependent monooxygenases play a variety of key physiological roles and are also very powerful biotechnological tools. These enzymes have been classified into eight different classes (A-H) based on their sequences and biochemical features. By combining structural and sequence analysis, and phylogenetic inference, we have explored the evolutionary history of classes A, B, E, F, and G and demonstrate that their multidomain architectures reflect their phylogenetic relationships, suggesting that the main evolutionary steps in their divergence are likely to have arisen from the recruitment of different domains. Additionally, the functional divergence within in each class appears to have been the result of other mechanisms such as a complex set of single-point mutations. Our results reinforce the idea that a main constraint on the evolution of cofactor-dependent enzymes is the functional binding of the cofactor. Additionally, a remarkable feature of this family is that the sequence of the key flavin adenine dinucleotide-binding domain is split into at least two parts in all classes studied here. We propose a complex set of evolutionary events that gave rise to the origin of the different classes within this family., (Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Decades of intensive experimental studies of the recognition of DNA sequences by proteins have provided us with a view of a diverse and complicated world in which few to no features are shared between individual DNA-binding protein families. The originally conceived direct readout of DNA residue sequences by amino acid side chains offers very limited capacity for sequence recognition, while the effects of the dynamic properties of the interacting partners remain difficult to quantify and almost impossible to generalise. In this work we investigated the energetic characteristics of all DNA residue-amino acid side chain combinations in the conformations found at the interaction interface in a very large set of protein-DNA complexes by the means of empirical potential-based calculations. General specificity-defining criteria were derived and utilised to look beyond the binding motifs considered in previous studies. Linking energetic favourability to the observed geometrical preferences, our approach reveals several additional amino acid motifs which can distinguish between individual DNA bases. Our results remained valid in environments with various dielectric properties.

In mammals, the master transcription regulator of antioxidant defences is provided by the Nrf2 protein. Phylogenetic analyses of Nrf2 sequences are used here to derive a molecular clock that manifests persuasive evidence that Nrf2 orthologues emerged, and then diverged, at two time points that correlate with well-established geochemical and palaeobiological chronologies during progression of the 'Great Oxygenation Event'. We demonstrate that orthologues of Nrf2 first appeared in fungi around 1.5 Ga during the Paleoproterozoic when photosynthetic oxygen was being absorbed into the oceans. A subsequent significant divergence in Nrf2 is seen during the split between fungi and the Metazoa approximately 1.0-1.2 Ga, at a time when oceanic ventilation released free oxygen to the atmosphere, but with most being absorbed by methane oxidation and oxidative weathering of land surfaces until approximately 800 Ma. Atmospheric oxygen levels thereafter accumulated giving rise to metazoan success known as the Cambrian explosion commencing at ~541 Ma. Atmospheric O2 levels then rose in the mid Paleozoic (359-252 Ma), and Nrf2 diverged once again at the division between mammals and non-mammalian vertebrates during the Permian-Triassic boundary (~252 Ma). Understanding Nrf2 evolution as an effective antioxidant response may have repercussions for improved human health.

We present a generic, multidisciplinary approach for improving our understanding of novel missense variants in recently discovered disease genes exhibiting genetic heterogeneity, by combining clinical and population genetics with protein structural analysis. Using six new de novo missense diagnoses in TBL1XR1 from the Deciphering Developmental Disorders study, together with population variation data, we show that the β-propeller structure of the ubiquitous WD40 domain provides a convincing way to discriminate between pathogenic and benign variation. Children with likely pathogenic mutations in this gene have severely delayed language development, often accompanied by intellectual disability, autism, dysmorphology and gastrointestinal problems. Amino acids affected by likely pathogenic missense mutations are either crucial for the stability of the fold, forming part of a highly conserved symmetrically repeating hydrogen-bonded tetrad, or located at the top face of the β-propeller, where 'hotspot' residues affect the binding of β-catenin to the TBLR1 protein. In contrast, those altered by population variation are significantly less likely to be spatially clustered towards the top face or to be at buried or highly conserved residues. This result is useful not only for interpreting benign and pathogenic missense variants in this gene, but also in other WD40 domains, many of which are associated with disease., (© The Author 2016. Published by Oxford University Press.)

Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses, and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these are a vast number of databases for the more specialized user, dealing with specific families, diseases, structural features, and so on.

How do the surface side chains of a protein behave when it binds to another protein? Do they optimize interactions by crumpling inwards or by extending outwards?

Representative pairs of amino acid side chains and nucleic acid bases extracted from available high-quality structures of protein-DNA complexes were analyzed using a range of computational methods. CCSD(T)/CBS interaction energies were calculated for the chosen 272 pairs. These reference interaction energies were used to test the MP2.5/CBS, MP2.X/CBS, MP2-F12, DFT-D3, PM6, and Amber force field methods. Method MP2.5 provided excellent agreement with reference data (root-mean-square error (RMSE) of 0.11 kcal/mol), which is more than 1 order of magnitude faster than the CCSD(T) method. When MP2-F12 and MP2.5 were combined, the results were within reasonable accuracy (0.20 kcal/mol), with a computational savings of almost 2 orders of magnitude. Therefore, this method is a promising tool for accurate calculations of interaction energies in protein-DNA motifs of up to ∼100 atoms, for which CCSD(T)/CBS benchmark calculations are not feasible. B3-LYP-D3 calculated with def2-TZVPP and def2-QZVP basis sets yielded sufficiently good results with a reasonably small RMSE. This method provided better results for neutral systems, whereas positively charged species exhibited the worst agreement with the benchmark data. The Amber force field yielded unbalanced results-performing well for systems containing nonpolar amino acids but severely underestimating interaction energies for charged complexes. The semiempirical PM6 method with corrections for hydrogen bonding and dispersion energy (PM6-D3H4) exhibited considerably smaller error than the Amber force field, which makes it an effective tool for modeling extended protein-ligand complexes (of up to 10,000 atoms).

Molecular cavities, which include voids and channels, are critical for molecular function. We present a webserver, BetaCavityWeb, which computes these cavities for a given molecular structure and a given spherical probe, and reports their geometrical properties: volume, boundary area, buried area, etc. The server's algorithms are based on the Voronoi diagram of atoms and its derivative construct: the beta-complex. The correctness of the computed result and computational efficiency are both mathematically guaranteed. BetaCavityWeb is freely accessible at the Voronoi Diagram Research Center (VDRC) (http://voronoi.hanyang.ac.kr/betacavityweb)., (© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The growing number of high-quality experimental (X-ray, NMR) structures of protein–DNA complexes has sufficient enough information to assess whether universal rules governing the DNA sequence recognition process apply. While previous studies have investigated the relative abundance of various modes of amino acid–base contacts (van der Waals contacts, hydrogen bonds), relatively little is known about the energetics of these noncovalent interactions. In the present study, we have performed the first large-scale quantitative assessment of binding preferences in protein–DNA complexes by calculating the interaction energies in all 80 possible amino acid–DNA base combinations. We found that several mutual amino acid–base orientations featuring bidentate hydrogen bonds capable of unambiguous one-to-one recognition correspond to unique minima in the potential energy space of the amino acid–base pairs. A clustering algorithm revealed that these contacts form a spatially well-defined group offering relatively little conformational freedom. Various molecular mechanics force field and DFT-D ab initio calculations were performed, yielding similar results.

The latest version of the CATH-Gene3D protein structure classification database (4.0, http://www.cathdb.info) provides annotations for over 235,000 protein domain structures and includes 25 million domain predictions. This article provides an update on the major developments in the 2 years since the last publication in this journal including: significant improvements to the predictive power of our functional families (FunFams); the release of our 'current' putative domain assignments (CATH-B); a new, strictly non-redundant data set of CATH domains suitable for homology benchmarking experiments (CATH-40) and a number of improvements to the web pages., (© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Enzyme active sites can be connected to the exterior environment by one or more channels passing through the protein. Despite our current knowledge of enzyme structure and function, surprisingly little is known about how often channels are present or about any structural features such channels may have in common., Results: Here, we analyze the long channels (i.e. >15 Å) leading to the active sites of 4,306 enzyme structures. We find that over 64% of enzymes contain two or more long channels, their typical length being 28 Å. We show that amino acid compositions of the channel significantly differ both to the composition of the active site, surface and interior of the protein., Conclusions: The majority of enzymes have buried active sites accessible via a network of access channels. This indicates that enzymes tend to have buried active sites, with channels controlling access to, and egress from, them, and that suggests channels may play a key role in helping determine enzyme substrate.

Molecular external structure is important for molecular function, with voids on the surface and interior being one of the most important features. Hence, recognition of molecular voids and accurate computation of their geometrical properties, such as volume, area and topology, are crucial, yet most popular algorithms are based on the crude use of sampling points and thus are approximations even with a significant amount of computation. In this article, we propose an analytic approach to the problem using the Voronoi diagram of atoms and the beta-complex. The correctness and efficiency of the proposed algorithm is mathematically proved and experimentally verified. The benchmark test clearly shows the superiority of BetaVoid to two popular programs: VOIDOO and CASTp. The proposed algorithm is implemented in the BetaVoid program which is freely available at the Voronoi Diagram Research Center (http://voronoi.hanyang.ac.kr)., (© 2014 Wiley Periodicals, Inc.)

PDBsum, http://www.ebi.ac.uk/pdbsum, is a website providing numerous pictorial analyses of each entry in the Protein Data Bank. It portrays the structural features of all proteins, DNA and ligands in the entry, as well as depicting the interactions between them. The latest features, described here, include annotation of human protein sequences with their naturally occurring amino acid variants, dynamic graphs showing the relationships between related protein domain architectures, analyses of ligand binding clusters across different experimental determinations of the same protein, analyses of tunnels in proteins and new search options.

Identifying which ligands might bind to a protein before crystallization trials could provide a significant saving in time and resources. LigSearch, a web server aimed at predicting ligands that might bind to and stabilize a given protein, has been developed. Using a protein sequence and/or structure, the system searches against a variety of databases, combining available knowledge, and provides a clustered and ranked output of possible ligands. LigSearch can be accessed at http://www.ebi.ac.uk/thornton-srv/databases/LigSearch.

In the 40 years since its inception, the Protein Data Bank (PDB) has amassed over 80,000 experimentally determined structural models of proteins, plus many models of DNA and RNA fragments. The majority of the protein models have contributed, in some way, to an understanding of their particular protein's function, be it through the conformation of its catalytic residues, the details of its interactions with other proteins, substrate molecules, DNA, and so on. However, the totality of the data in the PDB provides a rich source of more generalized knowledge about proteins, their molecular biology, and evolution. Here, we describe how the focus of protein structural analysis has developed over the past 40 years. © 2012 Wiley Periodicals, Inc. Biopolymers 99: 183-188, 2013., (Copyright © 2012 Wiley Periodicals, Inc.)

The 1000 Genomes Project data provides a natural background dataset for amino acid germline mutations in humans. Since the direction of mutation is known, the amino acid exchange matrix generated from the observed nucleotide variants is asymmetric and the mutabilities of the different amino acids are very different. These differences predominantly reflect preferences for nucleotide mutations in the DNA (especially the high mutation rate of the CpG dinucleotide, which makes arginine mutability very much higher than other amino acids) rather than selection imposed by protein structure constraints, although there is evidence for the latter as well. The variants occur predominantly on the surface of proteins (82%), with a slight preference for sites which are more exposed and less well conserved than random. Mutations to functional residues occur about half as often as expected by chance. The disease-associated amino acid variant distributions in OMIM are radically different from those expected on the basis of the 1000 Genomes dataset. The disease-associated variants preferentially occur in more conserved sites, compared to 1000 Genomes mutations. Many of the amino acid exchange profiles appear to exhibit an anti-correlation, with common exchanges in one dataset being rare in the other. Disease-associated variants exhibit more extreme differences in amino acid size and hydrophobicity. More modelling of the mutational processes at the nucleotide level is needed, but these observations should contribute to an improved prediction of the effects of specific variants in humans.

FunTree is a new resource that brings together sequence, structure, phylogenetic, chemical and mechanistic information for structurally defined enzyme superfamilies. Gathering together this range of data into a single resource allows the investigation of how novel enzyme functions have evolved within a structurally defined superfamily as well as providing a means to analyse trends across many superfamilies. This is done not only within the context of an enzyme's sequence and structure but also the relationships of their reactions. Developed in tandem with the CATH database, it currently comprises 276 superfamilies covering ~1800 (70%) of sequence assigned enzyme reactions. Central to the resource are phylogenetic trees generated from structurally informed multiple sequence alignments using both domain structural alignments supplemented with domain sequences and whole sequence alignments based on commonality of multi-domain architectures. These trees are decorated with functional annotations such as metabolite similarity as well as annotations from manually curated resources such the catalytic site atlas and MACiE for enzyme mechanisms. The resource is freely available through a web interface: www.ebi.ac.uk/thorton-srv/databases/FunTree.

In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life.

We describe a graphical system for automatically generating multiple 2D diagrams of ligand-protein interactions from 3D coordinates. The diagrams portray the hydrogen-bond interaction patterns and hydrophobic contacts between the ligand(s) and the main-chain or side-chain elements of the protein. The system is able to plot, in the same orientation, related sets of ligand-protein interactions. This facilitates popular research tasks, such as analyzing a series of small molecules binding to the same protein target, a single ligand binding to homologous proteins, or the completely general case where both protein and ligand change.

Background: Inflammation of the middle ear (otitis media) is very common and can lead to serious complications if not resolved. Genetic studies suggest an inherited component, but few of the genes that contribute to this condition are known. Mouse mutants have contributed significantly to the identification of genes predisposing to otitis media, Results: The dearisch mouse mutant is an ENU-induced mutant detected by its impaired Preyer reflex (ear flick in response to sound). Auditory brainstem responses revealed raised thresholds from as early as three weeks old. Pedigree analysis suggested a dominant but partially penetrant mode of inheritance. The middle ear of dearisch mutants shows a thickened mucosa and cellular effusion suggesting chronic otitis media with effusion with superimposed acute infection. The inner ear, including the sensory hair cells, appears normal. Due to the low penetrance of the phenotype, normal backcross mapping of the mutation was not possible. Exome sequencing was therefore employed to identify a non-conservative tyrosine to cysteine (Y71C) missense mutation in the Islet1 gene, Isl1(Drsh). Isl1 is expressed in the normal middle ear mucosa. The findings suggest the Isl1(Drsh) mutation is likely to predispose carriers to otitis media., Conclusions: Dearisch, Isl1(Drsh), represents the first point mutation in the mouse Isl1 gene and suggests a previously unrecognized role for this gene. It is also the first recorded exome sequencing of the C3HeB/FeJ background relevant to many ENU-induced mutants. Most importantly, the power of exome resequencing to identify ENU-induced mutations without a mapped gene locus is illustrated.

Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds-particularly useful for newly solved structures, and especially those of unknown function. Beyond these there are a vast number of databases for the most specialized user, dealing with specific families, diseases, structural features and so on.

Background: The Midwest Center for Structural Genomics (MCSG) is one of the large-scale centres of the Protein Structure Initiative (PSI). During the first two phases of the PSI the MCSG has solved over a thousand protein structures. A criticism of structural genomics is that target selection strategies mean that some structures are solved without having a known function and thus are of little biomedical significance. Structures of unknown function have stimulated the development of methods for function prediction from structure., Results: We show that the MCSG has met the stated goals of the PSI and use online resources and readily available function prediction methods to provide functional annotations for more than 90% of the MCSG structures. The structure-to-function prediction method ProFunc provides likely functions for many of the MCSG structures that cannot be annotated by sequence-based methods., Conclusions: Although the focus of the PSI was structural coverage, many of the structures solved by the MCSG can also be associated with functional classes and biological roles of possible biomedical value.

The interactions between amino acid side chains in proteins are generally considered to be the most important stabilizing factor controlling the precise arrangement of the polypeptide chain into a well-defined spatial structure. We used the RI-DFT-D method to calculate the full 20 × 20 matrix of interaction energies between all pairs of amino acid side chains. For each pair, we used a representative 3D conformation extracted from an analysis of known protein structures from Protein Data Bank (PDB). The representative comes from the largest cluster of relative orientations of the two side chains. We find that all of the calculated interaction energies between selected pairs of amino acids are attractive in the gas phase with the exception of side chain pairs having the same total charge. We compared these data with those calculated by the parm03 and OPLS-AA/L force fields to investigate the reliability of simple methods in modeling biomolecules and their behavior. The force fields yield good overall interaction energies for our set but have problems in evaluation of some particular interactions which could be of principal importance for protein stability. We then looked in detail at the 20 side chain interactions involving tryptophan. The histograms of interaction energies showed that the distributions of the interaction energies are neither normal nor Boltzmann-like and that our representative geometries correspond mostly to the minimum energy geometry which is rather poorly populated in the whole pairwise energy distribution. We concluded that cluster representatives obtained by the clusterization algorithm based on geometry criteria cannot be considered as a typical interaction for the whole side chain/side chain interaction distribution. They seem to epitomize the strongest interactions in a protein and are often functionally or structurally important.

Unlabelled: ArchSchema is a Java Web Start application that generates a dynamic 2D network of related Pfam domain architectures. Each node corresponds to a different architecture (shown as a sequence of coloured boxes) and indicates whether any 3D structural information is available in the PDB. Satellite nodes can show either the UniProt codes or the PDB codes of proteins having the given architecture. Search options allow search by UniProt code or Pfam domain identifier, and results can be filtered by domain, organism, or by selecting only proteins in the PDB., Availability: ArchSchema can be freely accessed at http://www.ebi.ac.uk/Tools/archschema.

We present an analysis of the chemical fragments from lead-like ligands in the Protein Data Bank (PDB) that form hydrogen bonds to the side chains of Asp, Glu, Arg, and His, which are the most common residues found in ligand binding sites. A fragment is defined as the largest ring assembly containing the atoms involved in hydrogen bonding. In total, 462 fragments were found in 2038 ligands from over 8000 protein-ligand structures in the PDB. The results show which fragments have a higher propensity for interaction with specific side chains. Some fragments interact with Asp but not with Glu, and vice versa, despite these side chains sharing the same chemical moiety. Arg side chains form hydrogen bonds almost exclusively with O-mediated ligands, and the fragments are the most diverse. Hydrogen bond distances from the imidazole of His showed a wider range than the other three amino acids.

Most function prediction methods that identify cognate ligands from binding site analyses work on the assumption of molecular complementarity. These approaches build on the conjectured complementarity of geometrical and physicochemical properties between ligands and binding sites so that similar binding sites will bind similar ligands. We found that this assumption does not generally hold for protein-ligand interactions and observed that it is not the chemical composition of ligand molecules that dictates the complementarity between protein and ligand molecules, but that the ligand's share within the functional mechanism of a protein determines the degree of complementarity. Here, we present for a set of cognate ligands a descriptive analysis and comparison of the physicochemical properties that each ligand experiences in various nonhomologous binding pockets. The comparisons in each ligand set reveal large variations in their experienced physicochemical properties, suggesting that the same ligand can bind to distinct physicochemical environments. In some protein ligand complexes, the variation was found to correlate with the electrochemical characteristic of ligand molecules, whereas in others it was disclosed as a prerequisite for the biochemical function of the protein. To achieve binding, proteins were observed to engage in subtle balancing acts between electrostatic and hydrophobic interactions to generate stabilizing free energies of binding. For the presented analysis, a new method for scoring hydrophobicity from molecular environments was developed showing high correlations with experimental determined desolvation energies. The presented results highlight the complexities of molecular recognition and underline the challenges of computational structural biology in developing methods to detect these important subtleties.

Structural biology is rapidly accumulating a wealth of detailed information about protein function, binding sites, RNA, large assemblies and molecular motions. These data are increasingly of interest to a broader community of life scientists, not just structural experts. Visualization is a primary means for accessing and using these data, yet visualization is also a stumbling block that prevents many life scientists from benefiting from three-dimensional structural data. In this review, we focus on key biological questions where visualizing three-dimensional structures can provide insight and describe available methods and tools.

Web-based protein structure databases come in a wide variety of types and levels of information content. Those having the most general interest are the various atlases that describe each experimentally determined protein structure and provide useful links, analyses, and schematic diagrams relating to its 3D structure and biological function. Also of great interest are the databases that classify 3D structures by their folds as these can reveal evolutionary relationships which may be hard to detect from sequence comparison alone. Related to these are the numerous servers that compare folds--particularly useful for newly solved structures, and especially those of unknown function. Beyond these there are a vast number of databases for the more specialized user, dealing with specific families, diseases, structural features, and so on.