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.

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.

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.

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.

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.

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.

MOTIVATION: Annotation tools help scientists to traverse the gap between characterized and uncharacterized proteins. Tools for the prediction of protein function include those which predict the function of entire proteins or complexes, those annotating functional domains and those which predict specific residues within the domain. We have developed WSsas, a web service focused on the annotation of essential functional residues. WSsas uses similarity searches and pairwise alignments to transfer functional information about binding, catalytic and protein-protein interaction residues from solved structures to query sequences. In addition, WSsas can supply information about the relevant functional atoms. The web service definition (WSDL) file and a Perl client are freely available at http://www.ebi.ac.uk/thornton-srv/databases/WSsas/.

Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

MOTIVATION: The Protein Data Bank (PDB) contains over 43,800 experimentally determined 3D models of macromolecular structures and their complexes. Each 3D model reveals something interesting and important about the given molecule's function and biological significance. Usually the best source of this information is the original article describing it, and it is often possible to discern the key aspects of the structure from just one or two of the figures in that article. RESULTS: Here we describe how, with the permission of the journals and their publishers, we have endeavoured to make these key figures publicly available to enhance the functional information relating to each PDB entry in our PDBsum database. AVAILABILITY: http://www.ebi.ac.uk/pdbsum.

Physicochemical complementarity is commonly believed to be the driving force for molecular binding. The complementarity for example of electrostatic potentials is regarded as the force that draws the ligand from the solvent into the binding site [ 1 ]. If this hypothesis is true, the same ligand should encounter complementarity environmental properties in all proteins to which it binds. We have used our recently published ligand and binding pocket matching algorithm [ 2 ] to test this common assumption by searching for property distributions that are similar for the same ligand bound to different proteins. The algorithm bases on real spherical harmonic functions, which are applicable to approximate any property function on a unit sphere. These property functions can either be of geometrical or physicochemical nature. For our current analysis we used the shape of binding pockets to test their geometrical similarity and mapped electrostatic, van der Waals and hydrophobicity potentials of the protein on the ligand surface to simulate the physicochemical forces that a ligand may feel in its binding site. It was discovered that, of these properties the two that vary least for a given ligand are the binding conformation of the ligand followed by the shape of the binding pocket. Conversely, the same ligand encountered very different electrostatic and van der Waals potential environments in the different proteins to which it is bound. These properties were often found not to be complementary to the ligand's properties, which is in conflict with the general assumption stated above. However, the hydrophobicity of the binding pocket did seem to correlate with the properties of the ligand bound to the protein. Hydrophobic parts of the ligand are often confronted with hydrophobic parts of the protein, giving rise to similar hydrophobicity distributions within different binding pockets binding the same ligand (see Figure 1 ). A set of Adenosine-mono-phosphate (AMP) ligands bound to non-homologous binding sites is shown . Each row displays different geometrical and physicochemical properties of the binding site and ligand respectively. From top to bottom are shown the variation of the ligand shape, the binding pocket shape, the hydrophobicity of the protein mapped on the ligand shape, the van der Waals potential and the electrostatic potential both again mapped on the ligand shape. The properties were ordered according to their average degree of similarity among the different binding pockets from highest to lowest from top to bottom. In addition the AMP binding pockets were ranked according to the similarity of their bound ligand to the AMP ligand of the Protein Data Bank [3] structure 1amu [4]. These results demonstrate that binding sites that bind the same ligand can exhibit a large variation of properties by facing different physicochemical forces within different binding sites. The results urge a re-evaluation of the total contribution of some physicochemical properties to molecular recognition and the factors that drive molecular binding.

MOTIVATION: Low-complexity or cryptically simple sequences are widespread in protein sequences but their evolution and function are poorly understood. To date methods for the detection of low complexity in proteins have been directed towards the filtering of such regions prior to sequence homology searches but not to the analysis of the regions per se. However, many of these regions are encoded by non-repetitive DNA sequences and may therefore result from selection acting on protein structure and/or function. RESULTS: We have developed a new tool, based on the SIMPLE algorithm, that facilitates the quantification of the amount of simple sequence in proteins and determines the type of short motifs that show clustering above a certain threshold. By modifying the sensitivity of the program simple sequence content can be studied at various levels, from highly organised tandem structures to complex combinations of repeats. We compare the relative amount of simplicity in different functional groups of yeast proteins and determine the level of clustering of the different amino acids in these proteins. AVAILABILITY: The program is available on request or online at http://www.biochem.ucl.ac.uk/bsm/SIMPLE.