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Genome3D (https://www.genome3d.eu) is a freely available resource that provides consensus structural annotations for representative protein sequences taken from a selection of model organisms. Since the last NAR update in 2015, the method of data submission has been overhauled, with annotations now being 'pushed' to the database via an API. As a result, contributing groups are now able to manage their own structural annotations, making the resource more flexible and maintainable. The new submission protocol brings a number of additional benefits including: providing instant validation of data and avoiding the requirement to synchronise releases between resources. It also makes it possible to implement the submission of these structural annotations as an automated part of existing internal workflows. In turn, these improvements facilitate Genome3D being opened up to new prediction algorithms and groups. For the latest release of Genome3D (v2.1), the underlying dataset of sequences used as prediction targets has been updated using the latest reference proteomes available in UniProtKB. A number of new reference proteomes have also been added of particular interest to the wider scientific community: cow, pig, wheat and mycobacterium tuberculosis. These additions, along with improvements to the underlying predictions from contributing resources, has ensured that the number of annotations in Genome3D has nearly doubled since the last NAR update article. The new API has also been used to facilitate the dissemination of Genome3D data into InterPro, thereby widening the visibility of both the annotation data and annotation algorithms., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

PhyreRisk is an open-access, publicly accessible web application for interactively bridging genomic, proteomic and structural data facilitating the mapping of human variants onto protein structures. A major advance over other tools for sequence-structure variant mapping is that PhyreRisk provides information on 20,214 human canonical proteins and an additional 22,271 alternative protein sequences (isoforms). Specifically, PhyreRisk provides structural coverage (partial or complete) for 70% (14,035 of 20,214 canonical proteins) of the human proteome, by storing 18,874 experimental structures and 84,818 pre-built models of canonical proteins and their isoforms generated using our in house Phyre2. PhyreRisk reports 55,732 experimentally, multi-validated protein interactions from IntAct and 24,260 experimental structures of protein complexes. Another major feature of PhyreRisk is that, rather than presenting a limited set of precomputed variant-structure mapping of known genetic variants, it allows the user to explore novel variants using, as input, genomic coordinates formats (Ensembl, VCF, reference SNP ID and HGVS notations) and Human Build GRCh37 and GRCh38. PhyreRisk also supports mapping variants using amino acid coordinates and searching for genes or proteins of interest. PhyreRisk is designed to empower researchers to translate genetic data into protein structural information, thereby providing a more comprehensive appreciation of the functional impact of variants. PhyreRisk is freely available at http://phyrerisk.bc.ic.ac.uk., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The identification of structurally similar proteins can provide a range of biological insights, and accordingly, the alignment of a query protein to a database of experimentally determined protein structures is a technique commonly used in the fields of structural and evolutionary biology. The PhyreStorm Web server has been designed to provide comprehensive, up-to-date and rapid structural comparisons against the Protein Data Bank (PDB) combined with a rich and intuitive user interface. It is intended that this facility will enable biologists inexpert in bioinformatics access to a powerful tool for exploring protein structure relationships beyond what can be achieved by sequence analysis alone. By partitioning the PDB into similar structures, PhyreStorm is able to quickly discard the majority of structures that cannot possibly align well to a query protein, reducing the number of alignments required by an order of magnitude. PhyreStorm is capable of finding 93±2% of all highly similar (TM-score>0.7) structures in the PDB for each query structure, usually in less than 60s. PhyreStorm is available at http://www.sbg.bio.ic.ac.uk/phyrestorm/., (Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Protein domains are generally thought to correspond to units of evolution. New research raises questions about how such domains are defined with bioinformatics tools and sheds light on how evolution has enabled partial domains to be viable.

Whole-genome and exome sequencing studies reveal many genetic variants between individuals, some of which are linked to disease. Many of these variants lead to single amino acid variants (SAVs), and accurate prediction of their phenotypic impact is important. Incorporating sequence conservation and network-level features, we have developed a method, SuSPect (Disease-Susceptibility-based SAV Phenotype Prediction), for predicting how likely SAVs are to be associated with disease. SuSPect performs significantly better than other available batch methods on the VariBench benchmarking dataset, with a balanced accuracy of 82%. SuSPect is available at www.sbg.bio.ic.ac.uk/suspect. The Web site has been implemented in Perl and SQLite and is compatible with modern browsers. An SQLite database of possible missense variants in the human proteome is available to download at www.sbg.bio.ic.ac.uk/suspect/download.html., (Copyright © 2014. Published by Elsevier Ltd.)

Coarse-grained protein structure models offer increased efficiency in structural modeling, but these must be coupled with fast and accurate methods to revert to a full-atom structure. Here, we present a novel algorithm to reconstruct mainchain models from C traces. This has been parameterized by fitting Gaussian mixture models (GMMs) to short backbone fragments centered on idealized peptide bonds. The method we have developed is statistically significantly more accurate than several competing methods, both in terms of RMSD values and dihedral angle differences. The method produced Ramachandran dihedral angle distributions that are closer to that observed in real proteins and better Phaser molecular replacement log-likelihood gains. Amino acid residue sidechain reconstruction accuracy using SCWRL4 was found to be statistically significantly correlated to backbone reconstruction accuracy. Finally, the PD2 method was found to produce significantly lower energy full-atom models using Rosetta which has implications for multiscale protein modeling using coarse-grained models. A webserver and C++ source code is freely available for noncommercial use from: http://www.sbg.bio.ic.ac.uk/phyre2/PD2_ca2main/., (Copyright © 2013 Wiley Periodicals, Inc.)

Coarse-grained (CG) methods for sampling protein conformational space have the potential to increase computational efficiency by reducing the degrees of freedom. The gain in computational efficiency of CG methods often comes at the expense of non-protein like local conformational features. This could cause problems when transitioning to full atom models in a hierarchical framework. Here, a CG potential energy function was validated by applying it to the problem of loop prediction. A novel method to sample the conformational space of backbone atoms was benchmarked using a standard test set consisting of 351 distinct loops. This method used a sequence-independent CG potential energy function representing the protein using [Formula: see text]-carbon positions only and sampling conformations with a Monte Carlo simulated annealing based protocol. Backbone atoms were added using a method previously described and then gradient minimised in the Rosetta force field. Despite the CG potential energy function being sequence-independent, the method performed similarly to methods that explicitly use either fragments of known protein backbones with similar sequences or residue-specific [Formula: see text]/[Formula: see text]-maps to restrict the search space. The method was also able to predict with sub-Angstrom accuracy two out of seven loops from recently solved crystal structures of proteins with low sequence and structure similarity to previously deposited structures in the PDB. The ability to sample realistic loop conformations directly from a potential energy function enables the incorporation of additional geometric restraints and the use of more advanced sampling methods in a way that is not possible to do easily with fragment replacement methods and also enable multi-scale simulations for protein design and protein structure prediction. These restraints could be derived from experimental data or could be design restraints in the case of computational protein design. C++ source code is available for download from http://www.sbg.bio.ic.ac.uk/phyre2/PD2/.

Motivation: Databases of sequenced genomes are widely used to characterize the structure, function and evolutionary relationships of proteins. The ability to discern such relationships is widely expected to grow as sequencing projects provide novel information, bridging gaps in our map of the protein universe., Results: We have plotted our progress in protein sequencing over the last two decades and found that the rate of novel sequence discovery is in a sustained period of decline. Consequently, PSI-BLAST, the most widely used method to detect remote evolutionary relationships, which relies upon the accumulation of novel sequence data, is now showing a plateau in performance. We interpret this trend as signalling our approach to a representative map of the protein universe and discuss its implications.

Structural genomics initiatives are rapidly generating vast numbers of protein structures. Comparative modelling is also capable of producing accurate structural models for many protein sequences. However, for many of the known structures, functions are not yet determined, and in many modelling tasks, an accurate structural model does not necessarily tell us about function. Thus, there is a pressing need for high-throughput methods for determining function from structure. The spatial arrangement of key amino acids in a folded protein, on the surface or buried in clefts, is often the determinants of its biological function. A central aim of molecular biology is to understand the relationship between such substructures or surfaces and biological function, leading both to function prediction and to function design. We present a new general method for discovering the features of binding pockets that confer specificity for particular ligands. Using a recently developed machine-learning technique which couples the rule-discovery approach of inductive logic programming with the statistical learning power of support vector machines, we are able to discriminate, with high precision (90%) and recall (86%) between pockets that bind FAD and those that bind NAD on a large benchmark set given only the geometry and composition of the backbone of the binding pocket without the use of docking. In addition, we learn rules governing this specificity which can feed into protein functional design protocols. An analysis of the rules found suggests that key features of the binding pocket may be tied to conformational freedom in the ligand. The representation is sufficiently general to be applicable to any discriminatory binding problem. All programs and data sets are freely available to non-commercial users at http://www.sbg.bio.ic.ac.uk/svilp_ligand/.

Motivation: The output of a bioinformatic tool such as BLAST must usually be interpreted by an expert before reliable conclusions can be drawn. This may be based upon the expert's experience, additional data and statistical analysis. Often the process is laborious, goes unrecorded and may be biased. Argumentation is an established technique for reasoning about situations where absolute truth or precise probability is impossible to determine., Results: We demonstrate the application of argumentation to 3D-PSSM, a protein structure prediction tool. The expert's interpretation of results is represented as an argumentation framework. Given a 3D-PSSM result, an automated procedure constructs arguments for and against the conclusion that the result is a good predictor of protein structure. In addition to capturing the unique expertise of the author of 3D-PSSM for distribution to users, an improvement in recall of 5-10 percentage points is achieved. This technique can be applied to a wide range of bioinformatic tools., Availability: Example public server and benchmarking data are available at http://www.sbg.bio.ic.ac.uk/~brj03/argumentation/paper/. Source code available on request.

A method (three-dimensional position-specific scoring matrix, 3D-PSSM) to recognise remote protein sequence homologues is described. The method combines the power of multiple sequence profiles with knowledge of protein structure to provide enhanced recognition and thus functional assignment of newly sequenced genomes. The method uses structural alignments of homologous proteins of similar three-dimensional structure in the structural classification of proteins (SCOP) database to obtain a structural equivalence of residues. These equivalences are used to extend multiply aligned sequences obtained by standard sequence searches. The resulting large superfamily-based multiple alignment is converted into a PSSM. Combined with secondary structure matching and solvation potentials, 3D-PSSM can recognise structural and functional relationships beyond state-of-the-art sequence methods. In a cross-validated benchmark on 136 homologous relationships unambiguously undetectable by position-specific iterated basic local alignment search tool (PSI-Blast), 3D-PSSM can confidently assign 18 %. The method was applied to the remaining unassigned regions of the Mycoplasma genitalium genome and an additional 13 regions were assigned with 95 % confidence. 3D-PSSM is available to the community as a web server: http://www.bmm.icnet.uk/servers/3dpssm, (Copyright 2000 Academic Press.)

Motivation: Sequence database search methods often identify putative sub-threshold hits of known function or structure for a given query sequence. It is widespread practice to filter these hits by hand using knowledge of function and other factors; to the expert, some hits may appear more sensible than others. SAWTED (Structure Assignment With Text Description) is an automated solution to this post-filtering problem which will be applicable to large scale genome assignments., Results: A standard document comparison algorithm is applied to text descriptions extracted from SWISS-PROT annotations. The added value of SAWTED in combination with PSI-BLAST has been shown with a benchmark of difficult remote homologues taken from the SCOP structure database., Availability: A WAWTED PSI-BLAST Web server is available to perform sensitive searches against the protein structure database (http://www.bmm.icnet.uk/servers/sawted)., Contact: R.MacCallum@icrf.icnet.uk

The results of the first Critical Assessment of Fully Automated Structure Prediction (CAFASP-1) are presented. The objective was to evaluate the success rates of fully automatic web servers for fold recognition which are available to the community. This study was based on the targets used in the third meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP-3). However, unlike CASP-3, the study was not a blind trial, as it was held after the structures of the targets were known. The aim was to assess the performance of methods without the user intervention that several groups used in their CASP-3 submissions. Although it is clear that "human plus machine" predictions are superior to automated ones, this CAFASP-1 experiment is extremely valuable for users of our methods; it provides an indication of the performance of the methods alone, and not of the "human plus machine" performance assessed in CASP. This information may aid users in choosing which programs they wish to use and in evaluating the reliability of the programs when applied to their specific prediction targets. In addition, evaluation of fully automated methods is particularly important to assess their applicability at genomic scales. For each target, groups submitted the top-ranking folds generated from their servers. In CAFASP-1 we concentrated on fold-recognition web servers only and evaluated only recognition of the correct fold, and not, as in CASP-3, alignment accuracy. Although some performance differences appeared within each of the four target categories used here, overall, no single server has proved markedly superior to the others. The results showed that current fully automated fold recognition servers can often identify remote similarities when pairwise sequence search methods fail. Nevertheless, in only a few cases outside the family-level targets has the score of the top-ranking fold been significant enough to allow for a confident fully automated prediction. Because the goals, rules, and procedures of CAFASP-1 were different from those used at CASP-3, the results reported here are not comparable with those reported in CASP-3. Nevertheless, it is clear that current automated fold recognition methods can not yet compete with "human-expert plus machine" predictions. Finally, CAFASP-1 has been useful in identifying the requirements for a future blind trial of automated served-based protein structure prediction.

In cases where the structure of a single protein is represented by an ensemble of conformations, there is often a need to determine the common features and to choose a "representative" conformation. This occurs, for example, with structures determined by NMR spectroscopy, analysis of the trajectory from a molecular dynamics simulation, or an ensemble of structures produced by comparative modeling. We reported previously automatic methods for (1) defining the atoms with low spatial variance across an ensemble (i.e., the "core" atoms) and the domains in which these atoms lie, and (2) clustering an ensemble into conformationally related subfamilies. To extend the utility of these methods, we have developed a freely available server on the World Wide Web at http:/(/)neon.chem.le.ac.uk/olderado/. This (1) contains an automatically generated database of representative structures, core atoms, and domains determined for 449 ensembles of NMR-derived protein structures in the Protein Data Bank (PDB) in May 1997, and (2) allows the user to upload a PDB-formatted file containing the coordinates of an ensemble of structures. The server returns in real time: (1) information on the residues constituting domains: (2) the structures that constitute each conformational subfamily; and (3) an interactive java-based three-dimensional viewer to visualise the domains and clusters. Such information is useful, for example, when selecting conformations to be used in comparative modeling and when choosing parts of structures to be used in molecular replacement. Here we describe the OLDERADO server.