Calculation of helix packing angles in protein structures

Software is presented for the calculation of packing angles and geometry of helical secondary structure elements in protein structures. AVAILABILITY: C language source code and documentation is available from http://www.bioinformatics.leeds.ac.uk.     

Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites

MOTIVATION: Identifying the location of ligand binding sites on a protein is of fundamental importance for a range of applications including molecular docking, de novo drug design and structural identification and comparison of functional sites. Here, we describe a new method of ligand binding site prediction called Q-SiteFinder. It uses the interaction energy between the protein and a simple van der Waals probe to locate energetically favourable binding sites. Energetically favourable probe sites are clustered according to their spatial proximity and clusters are then ranked according to the sum of interaction energies for sites within each cluster. RESULTS: There is at least one successful prediction in the top three predicted sites in 90% of proteins tested when using Q-SiteFinder. This success rate is higher than that of a commonly used pocket detection algorithm (Pocket-Finder) which uses geometric criteria. Additionally, Q-SiteFinder is twice as effective as Pocket-Finder in generating predicted sites that map accurately onto ligand coordinates. It also generates predicted sites with the lowest average volumes of the methods examined in this study. Unlike pocket detection, the volumes of the predicted sites appear to show relatively low dependence on protein volume and are similar in volume to the ligands they contain. Restricting the size of the pocket is important for reducing the search space required for docking and de novo drug design or site comparison. The method can be applied in structural genomics studies where protein binding sites remain uncharacterized since the 86% success rate for unbound proteins appears to be only slightly lower than that of ligand-bound proteins. AVAILABILITY: Both Q-SiteFinder and Pocket-Finder have been made available online at http://www.bioinformatics.leeds.ac.uk/qsitefinder and http://www.bioinformatics.leeds.ac.uk/pocketfinder     

Protein structure topological comparison, discovery and matching service

We describe a fold level fast protein comparison and motif matching facility based on the TOPS representation of structure. This provides an update to a previous service at the EBI, with a better graph matching with faster results and visualization of both the structures being compared against and the common pattern of each with the target domain. AVAILABILITY: Web service at http://balabio.dcs.gla.ac.uk/tops or via the main TOPS site at http://www.tops.leeds.ac.uk. Software is also available for download from these sites.     

Expression cartography of human tissues using self organizing maps

Background

Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate.

Results

We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/.

Conclusions

Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners.     

iPfam: visualization of protein-protein interactions in PDB at domain and amino acid resolutions

SUMMARY: There are many resources that contain information about binary interactions between proteins. However, protein interactions are defined by only a subset of residues in any protein. We have implemented a web resource that allows the investigation of protein interactions in the Protein Data Bank structures at the level of Pfam domains and amino acid residues. This detailed knowledge relies on the fact that there are a large number of multidomain proteins and protein complexes being deposited in the structure databases. The resource called iPfam is hosted within the Pfam UK website. Most resources focus on the interactions between proteins; iPfam includes these as well as interactions between domains in a single protein. AVAILABILITY: iPfam is available on the Web for browsing at http://www.sanger.ac.uk/Software/Pfam/iPfam/; the source-data for iPfam is freely available in relational tables via the ftp site ftp://ftp.sanger.ac.uk/pub/databases/Pfam/database_files/.     

PROMISE: a database of bioinorganic motifs.

The PROMISE (prosthetic centres andmetalions in protein activesites) database aims to present comprehensive sequence, structural, functional and bibliographic information on metalloproteins and other complex proteins, with an emphasis on active site structure and function. The database is available on the WorldWide Web at http://bioinf.leeds.ac.uk/promise/     

PROMISE: a database of information on prosthetic centres and metal ions in protein active sites.

The PROMISE (Prosthetic centres andmetalions in protein activesites) database aims to gather together comprehensive sequence, structural, functional and bibliographic information on proteins which possess prosthetic centres, with an emphasis on active site structure and function. The database is available on the World Wide Web at http://bioinf.leeds.ac.uk/promise/     

Improving the accuracy of transmembrane protein topology prediction using evolutionary information

MOTIVATION: Many important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion are mediated by membrane proteins. Unfortunately, as these proteins are not water soluble, it is extremely hard to experimentally determine their structure. Therefore, improved methods for predicting the structure of these proteins are vital in biological research. In order to improve transmembrane topology prediction, we evaluate the combined use of both integrated signal peptide prediction and evolutionary information in a single algorithm. RESULTS: A new method (MEMSAT3) for predicting transmembrane protein topology from sequence profiles is described and benchmarked with full cross-validation on a standard data set of 184 transmembrane proteins. The method is found to predict both the correct topology and the locations of transmembrane segments for 80% of the test set. This compares with accuracies of 62-72% for other popular methods on the same benchmark. By using a second neural network specifically to discriminate transmembrane from globular proteins, a very low overall false positive rate (0.5%) can also be achieved in detecting transmembrane proteins. AVAILABILITY: An implementation of the described method is available both as a web server (http://www.psipred.net) and as downloadable source code from http://bioinf.cs.ucl.ac.uk/memsat. Both the server and source code files are free to non-commercial users. Benchmark and training data are also available from http://bioinf.cs.ucl.ac.uk/memsat.     

PDBsum extras: SARS‐CoV‐2 and AlphaFold models

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.     

Using feature optimization-based support vector machine method to recognize the β-hairpin motifs in enzymes

β-Hairpins in enzyme, a kind of special protein with catalytic functions, contain many binding sites which are essential for the functions of enzyme. With the increasing number of observed enzyme protein sequences, it is of especial importance to use bioinformatics techniques to quickly and accurately identify the β-hairpin in enzyme protein for further advanced annotation of structure and function of enzyme. In this work, the proposed method was trained and tested on a non-redundant enzyme β-hairpin database containing 2818 β-hairpins and 1098 non-β-hairpins. With 5-fold cross-validation on the training dataset, the overall accuracy of 90.08% and Matthew’s correlation coefficient (Mcc) of 0.74 were obtained, while on the independent test dataset, the overall accuracy of 88.93% and Mcc of 0.76 were achieved. Furthermore, the method was validated on 845 β-hairpins with ligand binding sites. With 5-fold cross-validation on the training dataset and independent test on the test dataset, the overall accuracies were 85.82% (Mcc of 0.71) and 84.78% (Mcc of 0.70), respectively. With an integration of mRMR feature selection and SVM algorithm, a reasonable high accuracy was achieved, indicating the method to be an effective tool for the further studies of β-hairpins in enzymes structure. Additionally, as a novelty for function prediction of enzymes, β-hairpins with ligand binding sites were predicted. Based on this work, a web server was constructed to predict β-hairpin motifs in enzymes (http://202.207.29.251:8080/).     

Predicting Transmembrane Helix Packing Arrangements using Residue Contacts and a Force-Directed Algorithm

Alpha-helical transmembrane proteins constitute roughly 30% of a typical genome and are involved in a wide variety of important biological processes including cell signalling, transport of membrane-impermeable molecules and cell recognition. Despite significant efforts to predict transmembrane protein topology, comparatively little attention has been directed toward developing a method to pack the helices together. Here, we present a novel approach to predict lipid exposure, residue contacts, helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins. Using molecular dynamics data, we have trained and cross-validated a support vector machine (SVM) classifier to predict per residue lipid exposure with 69% accuracy. This information is combined with additional features to train a second SVM to predict residue contacts which are then used to determine helix-helix interaction with up to 65% accuracy under stringent cross-validation on a non-redundant test set. Our method is also able to discriminate native from decoy helical packing arrangements with up to 70% accuracy. Finally, we employ a force-directed algorithm to construct the optimal helical packing arrangement which demonstrates success for proteins containing up to 13 transmembrane helices. This software is freely available as source code from http://bioinf.cs.ucl.ac.uk/memsat/mempack/.     

Identifying differentially expressed subnetworks with MMG

BACKGROUND: Mixture model on graphs (MMG) is a probabilistic model that integrates network topology with (gene, protein) expression data to predict the regulation state of genes and proteins. It is remarkably robust to missing data, a feature particularly important for its use in quantitative proteomics. A new implementation in C and interfaced with R makes MMG extremely fast and easy to use and to extend. AVAILABILITY: The original implementation (Matlab) is still available from http://www.dcs.shef.ac.uk/~guido/; the new implementation is available from http://wrightlab.group.shef.ac.uk/people_noirel.htm, from CRAN, and has been submitted to BioConductor, http://www.bioconductor.org/.     

GO‐At : in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data

Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO‐At (gene ontology prediction in A. thaliana), a method that combines five data types (co‐expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two‐step approach, GO‐At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the ‘best hit’ from the first list, and achieves success rates of up to 79%. GO‐At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO‐At’s ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi‐associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web‐based implementation of GO‐At ( http://www.bioinformatics.leeds.ac.uk/goat ) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.     

The ModFOLD server for the quality assessment of protein structural models

The reliable assessment of the quality of protein structural models is fundamental to the progress of structural bioinformatics. The ModFOLD server provides access to two accurate techniques for the global and local prediction of the quality of 3D models of proteins. Firstly ModFOLD, which is a fast Model Quality Assessment Program (MQAP) used for the global assessment of either single or multiple models. Secondly ModFOLDclust, which is a more intensive method that carries out clustering of multiple models and provides per-residue local quality assessment. AVAILABILITY: http://www.biocentre.rdg.ac.uk/bioinformatics/ModFOLD/.     

BPROMPT: A consensus server for membrane protein prediction

Protein structure prediction is a cornerstone of bioinformatics research. Membrane proteins require their own prediction methods due to their intrinsically different composition. A variety of tools exist for topology prediction of membrane proteins, many of them available on the Internet. The server described in this paper, BPROMPT (Bayesian PRediction Of Membrane Protein Topology), uses a Bayesian Belief Network to combine the results of other prediction methods, providing a more accurate consensus prediction. Topology predictions with accuracies of 70% for prokaryotes and 53% for eukaryotes were achieved. BPROMPT can be accessed at http://www.jenner.ac.uk/BPROMPT.     

A consensus algorithm to screen genomes for novel families of transmembrane beta barrel proteins

The ability to search sequence datasets for membrane spanning proteins is an important requirement for genome annotation. However, the development of algorithms to identify novel types of transmembrane beta-barrel (TMB) protein has proven substantially harder than for transmembrane helical proteins, owing to a shorter TM domain in which only alternate residues are hydrophobic. Although recent reports have described important improvements in the development of such algorithms, there is still concern over their ability to confidently screen genomes. Here we describe a new algorithm combining composition and hidden Markov model topology based classifiers (called TMB-Hunt2), which achieves a crossvalidation accuracy of >95%, with 96.7% precision and 94.2% recall. An overview is given of the algorithm design, with a thorough assessment of performance and application to a number of genomes. Of particular note is that TMB/extracellular protein discrimination is significantly more difficult than TMB/cytoplasmic protein discrimination, with the predictor correctly rejecting just 74% of extracellular proteins, in comparison to 98% of cytoplasmic proteins. Focus is given to directions for further improvements in TMB/non-TMB protein discrimination, with a call for the development of standardized tests and assessments of such algorithms. Tools and datasets are made available through a website called TMB-Web (http://www.bioinformatics.leeds.ac.uk/TMB-Web/TMB-Hunt2).     

The Bioinformatics Template Library--generic components for biocomputing

MOTIVATION: The efficiency of bioinformatics programmers can be greatly increased through the provision of ready-made software components that can be rapidly combined, with additional bespoke components where necessary, to create finished programs. The new standard for C++ includes an efficient and easy to use library of generic algorithms and data-structures, designed to facilitate low-level component programming. The extension of this library to include functionality that is specifically useful in compute-intensive tasks in bioinformatics and molecular modelling could provide an effective standard for the design of reusable software components within the biocomputing community. RESULTS: A novel application of generic programming techniques in the form of a library of C++ components called the Bioinformatics Template Library (BTL) is presented. This library will facilitate the rapid development of efficient programs by providing efficient code for many algorithms and data-structures that are commonly used in biocomputing, in a generic form that allows them to be flexibly combined with application specific object-oriented class libraries. AVAILABILITY: The BTL is available free of charge from our web site http://www.cryst.bbk.ac.uk/~classlib/ and the EMBL file server http://www.embl-ebi.ac.uk/FTP/index.html     

Prediction of protein stability changes for single-site mutations using support vector machines

Accurate prediction of protein stability changes resulting from single amino acid mutations is important for understanding protein structures and designing new proteins. We use support vector machines to predict protein stability changes for single amino acid mutations leveraging both sequence and structural information. We evaluate our approach using cross-validation methods on a large dataset of single amino acid mutations. When only the sign of the stability changes is considered, the predictive method achieves 84% accuracy-a significant improvement over previously published results. Moreover, the experimental results show that the prediction accuracy obtained using sequence alone is close to the accuracy obtained using tertiary structure information. Because our method can accurately predict protein stability changes using primary sequence information only, it is applicable to many situations where the tertiary structure is unknown, overcoming a major limitation of previous methods which require tertiary information. The web server for predictions of protein stability changes upon mutations (MUpro), software, and datasets are available at http://www.igb.uci.edu/servers/servers.html.