Search

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 advent of sequencing and structural genomics projects has provided a dramatic boost in the number of protein structures and sequences. Due to the high-throughput nature of these projects, many of the molecules are uncharacterised and their functions unknown. This, in turn, has led to the need for a greater number and diversity of tools and databases providing annotation through transfer based on homology and prediction methods. Though many such tools to annotate protein sequence and structure exist, they are spread throughout the world, often with dedicated individual web pages. This situation does not provide a consensus view of the data and hinders comparison between methods. Integration of these methods is needed. So far this has not been possible since there was no common vocabulary available that could be used as a standard language. A variety of terms could be used to describe any particular feature ranging from different spellings to completely different terms. The Protein Feature Ontology (http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=BS) is a structured controlled vocabulary for features of a protein sequence or structure. It provides a common language for tools and methods to use, so that integration and comparison of their annotations is possible. The Protein Feature Ontology comprises approximately 100 positional terms (located in a particular region of the sequence), which have been integrated into the Sequence Ontology (SO). 40 non-positional terms which describe general protein properties have also been defined and, in addition, post-translational modifications are described by using an already existing ontology, the Protein Modification Ontology (MOD). The Protein Feature Ontology has been used by the BioSapiens Network of Excellence, a consortium comprising 19 partner sites in 14 European countries generating over 150 distinct annotation types for protein sequences and structures.

Motivation: In the present work we combine computational analysis and experimental data to explore the extent to which binding site similarities between members of the human cytosolic sulfotransferase family correlate with small-molecule binding profiles. Conversely, from a small-molecule point of view, we explore the extent to which structural similarities between small molecules correlate to protein binding profiles. Results: The comparison of binding site structural similarities and small-molecule binding profiles shows that proteins with similar small-molecule binding profiles tend to have a higher degree of binding site similarity but the latter is not sufficient to predict small-molecule binding patterns, highlighting the difficulty of predicting small-molecule binding patterns from sequence or structure. Likewise, from a small-molecule perspective, small molecules with similar protein binding profiles tend to be topologically similar but topological similarity is not sufficient to predict their protein binding patterns. These observations have important consequences for function prediction and drug design. Contact: rafael.najmanovich@ebi.ac.uk

Motivation: An increasing number of protein structures are being determined for which no biochemical characterization is available. The analysis of protein structure and function assignment is becoming an unexpected challenge and a major bottleneck towards the goal of well-annotated genomes. As shape plays a crucial role in biomolecular recognition and function, the examination and development of shape description and comparison techniques is likely to be of prime importance for understanding protein structure--function relationships. Results: A novel technique is presented for the comparison of protein binding pockets. The method uses the coefficients of a real spherical harmonics expansion to describe the shape of a protein's binding pocket. Shape similarity is computed as the L2 distance in coefficient space. Such comparisons in several thousands per second can be carried out on a standard linux PC. Other properties such as the electrostatic potential fit seamlessly into the same framework. The method can also be used directly for describing the shape of proteins and other molecules. Availability: A limited version of the software for the real spherical harmonics expansion of a set of points in PDB format is freely available upon request from the authors. Binding pocket comparisons and ligand prediction will be made available through the protein structure annotation pipeline Profunc (written by Roman Laskowski) which will be accessible from the EBI website shortly. Contact: thornton@ebi.ac.uk

Motivation: Domains are the units of protein structure, function and evolution. It is therefore essential to utilize knowledge of domains when studying the evolution of function, or when assigning function to genome sequence data. For this purpose, we have developed a database of catalytic domains, SCOPEC, by combining structural domain information from SCOP, full-length sequence information from Swiss-Prot, and verified functional information from the Enzyme Classification (EC) database. Two major problems need to be overcome to create a database of domain--function relationships; (1) for sequences, EC numbers are typically assigned to whole sequences rather than the functional unit, and (2) The Protein Data Bank (PDB) structures elucidated from a larger multi-domain protein will often have EC annotation although the relevant catalytic domain may lie elsewhere. Results: SCOPEC entries have high quality enzyme assignments; having passed both computational and manual checks. SCOPEC currently contains entries for 75% of all EC annotations in the PDB. Overall, EC number is fairly well conserved within a superfamily, even when the proteins are distantly related. Initial analysis is encouraging; suggesting that there is a 50:50 chance of conserved function in distant homologues first detected by a third iteration PSI-BLAST search. Therefore, we envisage that a knowledge-based approach to function assignment using the domain--EC relationships in SCOPEC will gain a marked improvement over this base line. Availability: The SCOPEC database is a valuable resource in the analysis and prediction of protein structure and function. It can be obtained or queried at our website http://www.enzome.com

Motivation: The solubility of a protein is crucial for its function and is therefore an evolutionary constraint. As the solubility of a protein is related to the distribution of polar and hydrophobic residues on its solvent accessible surface, such a constraint should provide a valuable insight into the evolution of protein surfaces. We examine how the surfaces of proteins have evolved by considering how the average hydrophobicities of patches of surface residues vary across homologous proteins. We derive distributions for the average hydrophobicity/philicity of surface patches at a residue-based level—which we refer to as the residue hydrophobic density. This is computed for a set of 28 monomeric proteins and their homologues. The resulting distributions are compared with a set of randomized sequences, with the same residue content. Results: We find that the patches, involving typically more than 10 residues, maintain a more hydrophilic surface than one would expect from a random substitution model, indicating a cooperative behaviour for these surfaces residues in terms of this single variable. Supplementary information: Additional plots for all of the proteins examined in this paper can be found at: http://www.ebi.ac.uk/~shanahan/PCon/index.html

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/. Contact: talavera@ebi.ac.uk

An efficient means for generating mutation data matrices from large numbers of protein sequences is presented here. By means of an approximate peptide-based sequence comparison algorithm, the set sequences are clustered at the 85% identity level. The closest relating pairs of sequences are aligned, and observed amino acid exchanges tallied in a matrix. The raw mutation frequency matrix is processed in a similar way to that described by Dayhoff et al. (1978), and so the resulting matrices may be easily used in current sequence analysis applications, in place of the standard mutation data matrices, which have not been updated for 13 years. The method is fast enough to process the entire SWISS-PROT databank in 20 h on a Sun SPARCstation 1, and is fast enough to generate a matrix from a specific family or class of proteins in minutes. Differences observed between our 250 PAM mutation data matrix and the matrix calculated by Dayhoff et al. are briefly discussed.

Motivation: Organic enzyme cofactors are involved in many enzyme reactions. Therefore, the analysis of cofactors is crucial to gain a better understanding of enzyme catalysis. To aid this, we have created the CoFactor database. Results: CoFactor provides a web interface to access hand-curated data extracted from the literature on organic enzyme cofactors in biocatalysis, as well as automatically collected information. CoFactor includes information on the conformational and solvent accessibility variation of the enzyme-bound cofactors, as well as mechanistic and structural information about the hosting enzymes. Availability: The database is publicly available and can be accessed at http://www.ebi.ac.uk/thornton-srv/databases/CoFactor Contact: julia.fischer@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: The increasing number of available atomic 3D structures of transmembrane channel proteins represents a valuable resource for better understanding their structure–function relationships and to eventually predict their selectivity. Herein, we present PoreLogo, an automatic tool for analysing, visualizing and comparing the amino acid composition of transmembrane channels and its conservation across the corresponding protein family. Availability: PoreLogo is accessible as a public web server at http://www.ebi.ac.uk/thornton-srv/software/PoreLogo/. Contacts: marial@ebi.ac.uk; romina.oliva@uniparthenope.it.

Summary: Metal-MACiE is a new publicly available web-based database, held in MySQL, which aims to organize the available information on the properties and the roles of metals in the context of the catalytic mechanisms of metalloenzymes. Metal-MACiE, which currently covers 75% of metal-dependent enzyme commission (EC) sub-sub-classes and is continuously growing, exploits the existing MACiE database for the annotation of the reaction mechanisms. The two databases constitute complementary sources of information for enzymology, biochemistry and molecular pharmacology studies. Availability: http://www.ebi.ac.uk/thornton-srv/databases/Metal_MACiE/home.html Contact: andreini@cerm.unifi.it Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: MACiE (mechanism, annotation and classification in enzymes) is a publicly available web-based database, held in CMLReact (an XML application), that aims to help our understanding of the evolution of enzyme catalytic mechanisms and also to create a classification system which reflects the actual chemical mechanism (catalytic steps) of an enzyme reaction, not only the overall reaction. Availability: http://www-mitchell.ch.cam.ac.uk/macie/ Contact: [email protected]

Summary: HTHquery is a web-based service to determine if a protein structure has a helix-turn-helix structural motif which could bind to DNA. It is based on a similarity with a set of structural templates, the accessibility of a putative structural motif and a positive electrostatic potential in the neighbourhood of the putative motif. A set of scores are computed, based on each template, using a linear predictor. From the training set used, the predictor has a true positive rate of 83.5% and a false positive rate of 0.8%. The emphasis for the website is on providing a straightforward interface which can be easily used by a bench-based scientist. Availability: HTHquery is implemented using a set of Perl scripts and C program and can be accessed freely on the website http://www.ebi.ac.uk/thornton-srv/databases/HTHquery Contact: Hugh.Shanahan@physics.org

We report a discovery that, of the 226 C2H2 zinc-finger (C2H2-ZNF) genes on human chromosome 19, 12 genes each have two open reading frames (ORFs) that are in different reading frames but that can be translated into the same type of C2H2-ZNF proteins. We came to this observation after using standard tools in an original manner. First, we found that the both ORFs of such a gene contained the same type of significant C2H2-ZNF domain with e-values of e--2 or better. Second, the both ORFs had a promoter, a transcription start site, a start codon, a Kozak pattern and a poly(A) site; hence, each of them can be viewed as a gene in terms of a gene's primary structure. Third, both the ORFs matched not only human C2H2-ZNF expressed sequence tags (ESTs) but also human C2H2-ZNF proteins with e-values of e--50 or better. This indicates that the both ORFs can be transcribed and translated into the same zinc-finger proteins. More importantly, we observed that the phenomenon---a DNA can be translated into the same type of proteins after a frameshift---also occurred in a set of 160 human C2H2-ZNF ESTs and in a set of nine cDNAs of human C2H2-ZNF proteins. This observation based on the two sets of wet-experimental data much strengthened our confidence on the discovery. Our discovery is useful in the deeper understanding of a gene's regulatory mechanism to maintain its function. Supplementary information: http://zfgene.i2zr.a-star.edu.sg

High throughput technology is having an immense impact on biological research and one of the major challenges today is to use all this information to understand the complex network of interactions in the cell. Not only have we seen an exponential growth in genomic sequence data, but with the advent of transcriptome and proteome technologies—in combination with structural genomics and combinatorial chemistry—the flood of new data is currently overwhelming and will only increase. In biology the sequence and structure databases (to say nothing of the small molecule data) have traditionally been separate projects—certainly within the public sector. Databases for the transcriptome and proteome data are only just coming on line. Functional data has traditionally not been captured computationally, although pathway databases are now becoming increasingly important. In this presentation I will look towards future prospects in linking bioinformatics and chemoinformatics. I will describe in detail some current work at EBI on the different worlds of the ‘proteome’ and the metabolome. It is interesting to overview the full complement of small molecules in a cell and how this reflects the metabolic enzymes. The complement of metabolic enzymes in different species will be discussed and their evolution considered. Contact: thornton@ebi.ac.ukhttp://www.ebi.ac.uk/Information/Staff/janet.html