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Summary: Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. Availability: http://cubic.bioc.columbia.edu/eva. Contact: eva@cubic.bioc.columbia.edu * To whom correspondence should be addressed.

The discrimination between functionally neutral amino acid substitutions and non-neutral mutations, affecting protein function, is very important for our understanding of diseases. The rapidly growing amounts of experimental data enable the development of computational tools to facilitate the annotation of these substitutions. Here, we describe a Random Forests-based classifier, named Mutation Detector (MuD) that utilizes structural and sequence-derived features to assess the impact of a given substitution on the protein function. In its automatic mode, MuD is comparable to alternative tools in performance. However, the uniqueness of MuD is that user-reported protein-specific structural and functional information can be added at run-time, thereby enhancing the prediction accuracy further. The MuD server, available at http://mud.tau.ac.il, assigns a reliability score to every prediction, thus offering a useful tool for the prioritization of substitutions in proteins with an available 3D structure.

Natively unstructured or disordered protein regions may increase the functional complexity of an organism; they are particularly abundant in eukaryotes and often evade structure determination. Many computational methods predict unstructured regions by training on outliers in otherwise well-ordered structures. Here, we introduce an approach that uses a neural network in a very different and novel way. We hypothesize that very long contiguous segments with nonregular secondary structure (NORS regions) differ significantly from regular, well-structured loops, and that a method detecting such features could predict natively unstructured regions. Training our new method, NORSnet, on predicted information rather than on experimental data yielded three major advantages: it removed the overlap between testing and training, it systematically covered entire proteomes, and it explicitly focused on one particular aspect of unstructured regions with a simple structural interpretation, namely that they are loops. Our hypothesis was correct: well-structured and unstructured loops differ so substantially that NORSnet succeeded in their distinction. Benchmarks on previously used and new experimental data of unstructured regions revealed that NORSnet performed very well. Although it was not the best single prediction method, NORSnet was sufficiently accurate to flag unstructured regions in proteins that were previously not annotated. In one application, NORSnet revealed previously undetected unstructured regions in putative targets for structural genomics and may thereby contribute to increasing structural coverage of large eukaryotic families. NORSnet found unstructured regions more often in domain boundaries than expected at random. In another application, we estimated that 50%–70% of all worm proteins observed to have more than seven protein–protein interaction partners have unstructured regions. The comparative analysis between NORSnet and DISOPRED2 suggested that long unstructured loops are a major part of unstructured regions in molecular networks., Author Summary The details of protein structures are important for function. Regions that do not adopt any regular structure in isolation (natively unstructured or disordered regions) initially appeared as a curious exception to this structure–function paradigm. It has become increasingly clear that unstructured regions are fundamental to many roles and that they are particularly important for multicellular organisms. Structural biology is just beginning to apprehend the stunning diversity of these roles. Here, we focused on unstructured regions dominated by a particular type of loop, namely the natively unstructured one. We developed a method that succeeded in the distinction between well-structured and natively unstructured loops. For the development, we did not use any experimental data for unstructured regions; when tested on experimental data, the method performed surprisingly well. Due to its different premises, the method captured very different aspects of unstructured regions than other methods that we tested. We applied the new method to two different problems. The first was the identification of proteins that may be difficult targets for structure determination. The second was the identification of worm proteins that have many interaction partners (more than seven) and unstructured regions. Surprisingly, we found unstructured regions of the loopy type in more than 50% of all the promiscuous worm proteins.

Experimental high-throughput studies of protein–protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein–protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein–protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein–protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/, Synopsis The IntAct database contains about ten large-scale data sets of protein–protein interactions. Each set contains thousands of experimentally observed pair interactions. Most pairs were observed in yeast (Saccharomyces cerevisiae), fly (Drosophila melanogaster), and worm (Caenorhabditis elegans). These interactions are often perceived as model organisms in the sense that one can infer that two mouse proteins interact if one experimentally observes the two corresponding proteins in worm to interact. Here, the authors analyzed in detail how the sequence signals of physical protein–protein interactions are conserved. It is a common assumption that protein–protein interactions can easily be inferred through homology transfer from one model organism to another organism of interest. Here, the authors demonstrated that such homology transfers are only accurate at unexpectedly high levels of sequence identity. Even more surprisingly, homology transfers of protein–protein interactions are significantly more reliable for protein pairs from the same species than for two protein pairs from different organisms. The observation that interactions were much more conserved within than across species was valid for all levels of sequence similarity, i.e. for very similar as well as for more diverged interologs.

RIKEN's FANTOM project has revealed many previously unknown coding sequences, as well as an unexpected degree of variation in transcripts resulting from alternative promoter usage and splicing. Ever more transcripts that do not code for proteins have been identified by transcriptome studies, in general. Increasing evidence points to the important cellular roles of such non-coding RNAs (ncRNAs). The distinction of protein-coding RNA transcripts from ncRNA transcripts is therefore an important problem in understanding the transcriptome and carrying out its annotation. Very few in silico methods have specifically addressed this problem. Here, we introduce CONC (for "coding or non-coding"), a novel method based on support vector machines that classifies transcripts according to features they would have if they were coding for proteins. These features include peptide length, amino acid composition, predicted secondary structure content, predicted percentage of exposed residues, compositional entropy, number of homologs from database searches, and alignment entropy. Nucleotide frequencies are also incorporated into the method. Confirmed coding cDNAs for eukaryotic proteins from the Swiss-Prot database constituted the set of true positives, ncRNAs from RNAdb and NONCODE the true negatives. Ten-fold cross-validation suggested that CONC distinguished coding RNAs from ncRNAs at about 97% specificity and 98% sensitivity. Applied to 102,801 mouse cDNAs from the FANTOM3 dataset, our method reliably identified over 14,000 ncRNAs and estimated the total number of ncRNAs to be about 28,000. RIKEN's FANTOM project has revealed many previously unknown coding sequences, as well as an unexpected degree of variation in transcripts resulting from alternative promoter usage and splicing. Ever more transcripts that do not code for proteins have been identified by transcriptome studies, in general. Increasing evidence points to the important cellular roles of such non-coding RNAs (ncRNAs). The distinction of protein-coding RNA transcripts from ncRNA transcripts is therefore an important problem in understanding the transcriptome and carrying out its annotation. Very few in silico methods have specifically addressed this problem. Here, we introduce CONC (for "coding or non-coding"), a novel method based on support vector machines that classifies transcripts according to features they would have if they were coding for proteins. These features include peptide length, amino acid composition, predicted secondary structure content, predicted percentage of exposed residues, compositional entropy, number of homologs from database searches, and alignment entropy. Nucleotide frequencies are also incorporated into the method. Confirmed coding cDNAs for eukaryotic proteins from the Swiss-Prot database constituted the set of true positives, ncRNAs from RNAdb and NONCODE the true negatives. Ten-fold cross-validation suggested that CONC distinguished coding RNAs from ncRNAs at about 97% specificity and 98% sensitivity. Applied to 102,801 mouse cDNAs from the FANTOM3 dataset, our method reliably identified over 14,000 ncRNAs and estimated the total number of ncRNAs to be about 28,000.

The DSSP program assigns protein secondary structure to one of eight states. This discrete assignment cannot describe the continuum of thermal fluctuations. Hence, a continuous assignment is proposed. Technically, the continuum results from averaging over ten discrete DSSP assignments with different hydrogen bond thresholds. The final continuous assignment for a single NMR model successfully reflected the structural variations observed between all NMR models in the ensemble. The structural variations between NMR models were verified to correlate with thermal motion; these variations were captured by the continuous assignments. Because the continuous assignment reproduces the structural variation between many NMR models from one single model, functionally important variation can be extracted from a single X-ray structure. Thus, continuous assignments of secondary structure may affect future protein structure analysis, comparison, and prediction.

Protein–protein interactions are facilitated by a myriad of residue–residue contacts on the interacting proteins. Identifying the site of interaction in the protein is a key for deciphering its functional mechanisms, and is crucial for drug development. Many studies indicate that the compositions of contacting residues are unique. Here, we describe a neural network that identifies protein–protein interfaces from sequence. For the most strongly predicted sites (in 34 of 333 proteins), 94% of the predictions were confirmed experimentally. When 70% of our predictions were right, we correctly predicted at least one interaction site in 20% of the complexes (66/333). These results indicate that the prediction of some interaction sites from sequence alone is possible. Incorporating evolutionary and predicted structural information may improve our method. However, even at this early stage, our tool might already assist wet-lab biology.

Contains fulltext : 171819.pdf (Publisher’s version ) (Open Access) Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand.Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners.As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.

In 2009 the International Society for Computational Biology (ISCB) started to roll out regional bioinformatics conferences in Africa, Latin America and Asia. The open and competitive bid for the first meeting in Asia (ISCB-Asia) was awarded to Asia-Pacific Bioinformatics Network (APBioNet) which has been running the International Conference on Bioinformatics (InCoB) in the Asia-Pacific region since 2002. InCoB/ISCB-Asia 2011 is held from November 30 to December 2, 2011 in Kuala Lumpur, Malaysia. Of 104 manuscripts submitted to BMC Genomics and BMC Bioinformatics conference supplements, 49 (47.1%) were accepted. The strong showing of Asia among submissions (82.7%) and acceptances (81.6%) signals the success of this tenth InCoB anniversary meeting, and bodes well for the future of ISCB-Asia.

Background Mutations resulting in the disruption of protein function are the underlying causes of many genetic diseases. Some mutations affect the number of expressed proteins while others alter the activity on a per-molecule basis. Single amino acid substitutions as caused by non-synonymous Single Nucleotide Polymorphisms (nsSNPs) often disrupt function by altering protein structure and/or stability, but can also wreak havoc by directly impacting functional binding sites. Given the experimental three-dimensional (3D) structure of a protein, we can try to differentiate between the "effect on structure/stability" and the "effect on binding". However, experimental 3D structures are available for only 1% of all known proteins; the magnitude of stability change caused by a given mutation is more widely available. Results Here, we analyze to which extent the functional effect of a mutation can be predicted from the effect on protein stability. We find that simple sequence-based methods succeed in predicting functional effects of nsSNPs. In fact, such methods consistently outperform approaches that predict functional change through the application of binary thresholds to stability change. We also observed that if stability is affected, functional change is easier to predict than when stability is not affected. Conclusion Our results confirmed that stability change is somehow related to function change. However, we also show that the knowledge of stability changes in no way suffices to predict functional changes and that many function changing mutations have no effect on stability.

Background Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease. Results Most disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for disease-causing mutations were higher than the scores for the function-altering data set originally used for developing the prediction method (here SNAP). We might expect that diseases are caused by change-of-function mutations. However, it is surprising how well prediction methods developed for different purposes identify this link. Conversely, our predictions suggest that the set of nsSNPs not currently linked to diseases contains very few strong disease associations to be discovered. Conclusions Firstly, annotations of disease-causing nsSNPs are on average so reliable that they can be used as proxies for functional impact. Secondly, disease-causing nsSNPs can be identified very well by methods that predict the impact of mutations on protein function. This implies that the existing prediction methods provide a very good means of choosing a set of suspect SNPs relevant for disease.

We have built on deep residual neural networks and coevolutionary features to develop two novel deep-learning-based systems, DeepHelicon and DeepTMInter, for sequence-based prediction of residue contacts and interaction sites in TM proteins, respectively. Both systems have undergone systematic supervised-learning processes followed by performance refinement on the currently largest datasets of TM proteins at the

International audience; Abstract A refined prediction of the nicotinic acetylcholine receptor (nAChR) subunits' secondary structure was computed with third-generation algorithms. The four selected programs, PHD, Predator, DSC, and NNSSP, based on different prediction approaches, were applied to each sequence of an alignment of nAChR and 5-HT3 receptor subunits, as well as a larger alignment with related subunit sequences from glycine and GABA receptors. A consensus prediction was computed for the nAChR subunits through a "winner takes all" method. By integrating the probabilities obtained with PHD, DSC, and NNSSP, this prediction was filtered in order to eliminate the singletons and to more precisely establish the structure limits (only 4% of the residues were modified). The final consensus secondary structure includes nine alpha-helices (24.2% of the residues, with an average length of 13.9 residues) and 17 beta-strands (22.5% of the residues, with an average length of 6.6 residues). The large extracellular domain is predicted to be mainly composed of beta-strands, with only two helices at the amino-terminal end. The transmembrane segments are predicted to be in a mixed alpha/beta topology (with a predominance of alpha-helices), with no known equivalent in the current protein database. The cytoplasmic domain is predicted to consist of two well-conserved amphipathic helices joined together by an unfolded stretch of variable length and sequence. In general, the segments predicted to occur in a periodic structure correspond to the more conserved regions, as defined by an analysis of sequence conservation per position performed on 152 superfamily members. The solvent accessibility of each residue was predicted from the multiple alignments with PHDacc. Each segment with more than three exposed residues was assumed to be external to the core protein. Overall, these data constitute an envelope of structural constraints. In a subsequent step, experimental data relative to the extracellular portion of the complete receptor were incorporated into the model. This led to a proposed two-dimensional representation of the secondary structure in which the peptide chain of the extracellular domain winds alternatively between the two interfaces of the subunit. Although this representation is not a tertiary structure and does not lead to predictions of specific beta-beta interaction, it should provide a basic framework for further mutagenesis investigations and for fold recognition (threading) searches.