Generative AI is generating much enthusiasm on potentially advancing biological design in computational biology. In this paper we take a somewhat contrarian view, arguing that a broader and deeper understanding of existing biological sequences is essential before undertaking the design of novel ones. We draw attention, for instance, to current protein function prediction methods which currently face significant limitations due to incomplete data and inherent challenges in defining and measuring function. We propose a “blue sky” vision centered on both comprehensive and precise annotation of existing protein and DNA sequences, aiming to develop a more complete and precise understanding of biological function. By contrasting recent studies that leverage generative AI for biological design with the pressing need for enhanced data annotation, we underscore the importance of prioritizing robust predictive models over premature generative efforts. We advocate for a strategic shift toward thorough sequence annotation and predictive understanding, laying a solid foundation for future advances in biological design.

MOTIVATION: In silico functional annotation of proteins is crucial to narrowing the sequencing-accelerated gap in our understanding of protein activities. Numerous function annotation methods exist, and their ranks have been growing, particularly so with the recent deep learning-based developments. However, it is unclear if these tools are truly predictive. As we are not aware of any methods that can identify new terms in functional ontologies, we ask if they can, at least, identify molecular functions of proteins that are non-homologous to or far-removed from known protein families. RESULTS: Here, we explore the potential and limitations of the existing methods in predicting the molecular functions of thousands of such proteins. Lacking the "ground truth" functional annotations, we transformed the assessment of function prediction into evaluation of functional similarity of protein pairs that likely share function but are unlike any of the currently functionally annotated sequences. Notably, our approach transcends the limitations of functional annotation vocabularies, providing a means to assess different-ontology annotation methods. We find that most existing methods are limited to identifying functional similarity of homologous sequences and fail to predict the function of proteins lacking reference. Curiously, despite their seemingly unlimited by-homology scope, deep learning methods also have trouble capturing the functional signal encoded in protein sequence. We believe that our work will inspire the development of a new generation of methods that push boundaries and promote exploration and discovery in the molecular function domain. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available at https://doi.org/10.6084/m9.figshare.c.6737127.v3. The code used to compute siblings is available openly at https://bitbucket.org/bromberglab/siblings-detector/.

MOTIVATION: metal-binding proteins have a central role in maintaining life processes. Nearly one-third of known protein structures contain metal ions that are used for a variety of needs, such as catalysis, DNA/RNA binding, protein structure stability, etc. Identifying metal-binding proteins is thus crucial for understanding the mechanisms of cellular activity. However, experimental annotation of protein metal-binding potential is severely lacking, while computational techniques are often imprecise and of limited applicability. RESULTS: we developed a novel machine learning-based method, mebipred, for identifying metal-binding proteins from sequence-derived features. This method is over 80% accurate in recognizing proteins that bind metal ion-containing ligands; the specific identity of 11 ubiquitously present metal ions can also be annotated. mebipred is reference-free, i.e. no sequence alignments are involved, and is thus faster than alignment-based methods; it is also more accurate than other sequence-based prediction methods. Additionally, mebipred can identify protein metal-binding capabilities from short sequence stretches, e.g. translated sequencing reads, and, thus, may be useful for the annotation of metal requirements of metagenomic samples. We performed an analysis of available microbiome data and found that ocean, hot spring sediments and soil microbiomes use a more diverse set of metals than human host-related ones. For human microbiomes, physiological conditions explain the observed metal preferences. Similarly, subtle changes in ocean sample ion concentration affect the abundance of relevant metal-binding proteins. These results highlight mebipred's utility in analyzing microbiome metal requirements. AVAILABILITY AND IMPLEMENTATION: mebipred is available as a web server at services.bromberglab.org/mebipred and as a standalone package at https://pypi.org/project/mymetal/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction - a process called 'end-to-end learning' - has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. RESULTS: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. CONCLUSION: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

The biennial joint meeting of ISMB (27th Annual Conference on Intelligent Systems for Molecular Biology) and ECCB (18th European Conference on Computational Biology) was held in Basel, Switzerland, July 21–25, 2019. ISMB is the flagship conference of the International Society for Computational Biology and the world’s premier forum for dissemination of scientific research in computational biology and its intersection with other areas. ECCB is similarly a top venue in the field, with a long tradition of publishing and presenting world-class research. This special issue serves as the Proceedings of ISMB/ECCB 2019. Following a successful model with a centralized manuscript review and acceptance process, this year’s conference organization provided the community with a unified submission interface for high-quality papers in the field of computational biology. The review process across 10 scientific areas was supervised by the Senior Program Committee (SPC), consisting of the Proceedings Chairs and Area Chairs (AC). About a third of the ACs were nominated by the Communities of Special Interests (COSIs), reflecting the desire of the ISMB/ECCB 2019 Steering Committee to involve COSIs in conference organization and the review process. Overall, the SPC consisted of 21 individuals; see Table 1. Thematic areas of ISMB/ECCB 2019 Note: The table lists the ACs for each theme, the number of reviewed papers, the number of accepted papers, and the acceptance rate for each area. A special area of General Computational Biology was created for papers not fitting in any of the predefined areas. Thematic areas of ISMB/ECCB 2019 Note: The table lists the ACs for each theme, the number of reviewed papers, the number of accepted papers, and the acceptance rate for each area. A special area of General Computational Biology was created for papers not fitting in any of the predefined areas. The scope of the conference includes theoretical papers, algorithms and statistical methods that allow for important novel biological insights and broadly defined intellectual contributions. We invited submission of papers in nine general scientific areas, organized by the relevant biological problems (Table 1). The 10th area of General Computational Biology was created to accommodate innovation outside of specified fields. The papers submitted to this area were largely in Text Mining, Mass Spectrometry and Visualization subfields, indicating community interest in these areas. All papers submitted to all areas were expected to present methodological and scientific contributions to the specific areas of submission. Abstracts, previously published papers, position papers, perspectives and reviews are not eligible for submission to the ISMB/ECCB Proceedings track. In total, 366 papers were submitted—a 10.6% increase over ISMB 2018. Of these, 363 papers were sent to review, receiving 1303 reviews from 388 Program Committee members. This constitutes an average of 3.6 reviews per submission. Three submissions had two completed reviews, 175 had three, 157 had four, 24 had five and four submissions had six completed reviews. The conditional acceptance information was provided to the authors a month after the submission deadline and final acceptance conveyed in another month. Overall, 69 manuscripts were accepted for a final acceptance rate of 18.9%. The distribution of papers reviewed in different areas is shown in Table 1. As was the case in 2018, the authors had the opportunity to request that their accepted manuscripts be presented in one of the COSI sessions. The most requests were made for MLCSB (64), followed by NetBio (39), Evolution (37), RegSys (37), TransMed (34), HiTSeq (33), Function (30), Microbiome (18), VarI (16), RNA (15), Text Mining (11), BioVis (10), Bio-Ontologies (7), CAMDA (4), Education (2) and CompMS (2). A further 35% of the submitted manuscripts made no specific COSI session request. Final assignments to COSI sessions were made by the Proceedings Chairs on the basis of the author and COSI requests. Three papers were assigned to the General Computational Biology session for presentation. The distribution of accepted papers to COSIs is shown in Table 2. Distribution of ISMB/ECCB 2019 Proceedings papers to COSIs Distribution of ISMB/ECCB 2019 Proceedings papers to COSIs We thank the ISMB/ECCB 2019 Steering Committee for their support and guidance. Special thanks go to the SPC and reviewers for their fantastic work dedicated to maintaining the high-quality standards of ISMB/ECCB in a compressed time frame. We are particularly grateful to Steven Leard, Diane Kovats and Pat Rodenburg for world-class organizational support. We would also like to thank the COSIs for nominating the ACs and the COSI contacts for their help in identifying reviewers and incorporating accepted papers into their programs. Finally, we thank the community for their interest and engagement in this conference—ISMB/ECCB 2019 belongs to you! With this, we invite you to read the Proceedings of ISMB/ECCB 2019. See you next year in Montreal, Canada, for ISMB 2020. Conflict of Interest: none declared.

The 26th Annual Conference on Intelligent Systems for Molecular Biology (ISMB) was held in Chicago, Illinois, USA, July 6–10, 2018. ISMB is the flagship conference of the International Society for Computational Biology (ISCB) and the world’s premier forum for dissemination of scientific research in computational biology and its intersection with other fields. This special issue serves as the Proceedings of ISMB 2018. One of the primary goals of this year’s conference organization was to provide the community with a unified interface for submission/acceptance of high-quality papers in the field of computational biology. That is, we explicitly aimed to ensure homogeneity in quality of accepted papers across different scientific themes. Moreover, we looked to simultaneously acquaint researchers outside the immediate ISCB community with the areas of interest represented by our Community of Special Interests (COSIs; COSI stands for the Community of Special Interests. There are 20 active COSIs currently operating under ISCB. Of those, 16 COSIs participated in the ISMB 2018 Proceedings.) and to invite submissions beyond these pre-determined interests. The scope of ISMB includes theoretical papers, algorithms, or statistical methods, while allowing for important novel biological insights and broadly defined intellectual contributions. We invited submission of papers in 10 general scientific areas (Table 1), organized by relevant biological problems. The area of General Computational Biology was created to accommodate under-represented innovation in the field. Upon inspection of the papers submitted to this area, three methodology-specific scientific sub-areas were identified (Text Mining, Mass Spectrometry and Visualization) in addition to a number of unclassifiable outliers. All papers submitted to all areas were expected to present methodological and scientific contributions to the specific areas of submission. Thematic areas of ISMB 2018 Notes: The table lists the ACs for each theme, the number of reviewed papers, the number of accepted papers and the acceptance rate for each area. A special area of General Computational Biology was created for papers not fitting in any of the pre-defined areas. This area contained 6 Text Mining papers (1 accepted), 7 Mass Spectrometry papers (2 accepted), 5 Visualization papers (2 accepted) and 14 additional diverse papers (1 accepted). The list of CCs includes: Rolf Backhofen, Jan Baumbach, Emidio Capriotti, Ana Conesa, Christophe Dessimoz, Jeremy Goecks, Pavel Labaj, Florian Markowetz, Nicola Mulder, Alice McHardy, Natasa Przulj, Venkata Satagopam, Saurabh Sinha, Karin Verspoor and Olga Vitek. Thematic areas of ISMB 2018 Notes: The table lists the ACs for each theme, the number of reviewed papers, the number of accepted papers and the acceptance rate for each area. A special area of General Computational Biology was created for papers not fitting in any of the pre-defined areas. This area contained 6 Text Mining papers (1 accepted), 7 Mass Spectrometry papers (2 accepted), 5 Visualization papers (2 accepted) and 14 additional diverse papers (1 accepted). The list of CCs includes: Rolf Backhofen, Jan Baumbach, Emidio Capriotti, Ana Conesa, Christophe Dessimoz, Jeremy Goecks, Pavel Labaj, Florian Markowetz, Nicola Mulder, Alice McHardy, Natasa Przulj, Venkata Satagopam, Saurabh Sinha, Karin Verspoor and Olga Vitek. Prominent researchers were selected to the Senior Program Committee (SPC). SPC consisted of the Proceedings Chairs, Area Chairs (ACs) and COSI-selected Chairs (CCs). This structure reflected the desire of the ISMB 2018 Steering Committee to involve COSIs early on and throughout the review process, in part because the Proceedings papers were to be ultimately presented in COSI sessions at the main meeting. Overall, the SPC consisted of 26 people (Table 1). In total, 331 papers were submitted for an 18.6% increase over ISMB/ECCB 2017. Of those, 324 papers were sent to review for which 1040 reviews were written by 337 Program Committee (PC) members (average of 3.2 reviews per submission). Twenty-nine submissions had 2 completed reviews, 209 had 3, 64 had 4, 15 had 5 and 4 submissions had 6 completed reviews. The conditional acceptance information was provided to the authors a month later and final acceptance conveyed 2 months after initial submission. Overall, 65 manuscripts were accepted for a final acceptance rate of 19.6%. The distribution of papers reviewed in different areas is shown in Table 1. A new feature of this year’s ISMB submission was that the authors had an opportunity to request that their accepted manuscript be presented in one of the COSI sessions. The most requests were made for MLCSB (73), followed by NetBio (47), HiTSeq (29), Function (29), RegSys (27), TransMed (25), RNA (23), Evolution (20), Bio-Ontologies (17), Microbiome (15), BioVis (13), VarI (5), CAMDA (5) and Education (1). Roughly 37% of the submitted manuscripts did not provide this information. Final assignments to COSI sessions were made by the Proceedings Chairs on the basis of the author and COSI requests. One of the papers did not fit any of the COSIs and was assigned to the General Computational Biology session. The distribution of accepted papers to COSIs is shown in Table 2. Distribution of ISMB 2018 proceedings papers to COSIs Distribution of ISMB 2018 proceedings papers to COSIs We thank the ISMB 2018 Steering Committee for their guidance. Special thanks go to the SPC and reviewers for all their fantastic work dedicated to maintaining the high quality standards of ISMB in a very short timeframe. We are particularly grateful to Steven Leard, Diane Kovats and Pat Rodenburg for world-class organizational support. Finally, we thank the community for their interest and engagement in this conference––ISMB 2018 belongs to you! With this, we invite you to read the Proceedings of ISMB 2018. See you next year in Basel, Switzerland, for ISMB/ECCB 2019. Conflict of Interest: none declared.

Motivation: The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. The increase in the number of available sequences, however, has drastically increased the search space, thus significantly slowing down alignment methods. Results: Here we describe homology-derived functional similarity of proteins (HFSP), a novel computational method that uses results of a high-speed alignment algorithm, MMseqs2, to infer functional similarity of proteins on the basis of their alignment length and sequence identity. We show that our method is accurate (85% precision) and fast (more than 40-fold speed increase over state-of-the-art). HFSP can help correct at least a 16% error in legacy curations, even for a resource of as high quality as Swiss-Prot. These findings suggest HFSP as an ideal resource for large-scale functional annotation efforts. Supplementary information: Supplementary data are available at Bioinformatics online.

Institutions around the world are fighting to improve science communication all the time. From calls for journal papers to be simplified to encouraging scientists to take more of an active role through community engagement, there is an impetus to demystify and improve public understanding and engagement with science. Technology has greatly helped expand the range of learning styles that a lecturer can call on to reach people in new ways. Social media outlets like Twitter, Facebook, Instagram, and Tumblr have expanded the reach of science communication within and across scientific disciplines and to the lay public. Here, with all the videos, interactive quizzes, and instant feedback it can be easy to overlook one of the simplest methods for communicating complex ideas: comics.

The 25th Annual Conference Intelligent Systems for Molecular Biology (ISMB), held jointly with the 16th annual European Conference on Computational Biology (ECCB), took place in Prague, Czech Republic, July 21–25, 2017 (http://www.iscb.org/ismbeccb2017). This special issue of Bioinformatics serves as the proceedings for this official conference of the International Society for Computational Biology (ISCB, http://www.iscb.org/). The year 2017 was inaugural for a new program format introducing the ISCB Communities of Special Interest (COSIs) as scientific themes within the conference. The 12 COSIs presenting at ISMB/ECCB 2017 (Table 1) had previously organized the pre-conference Special Interest Group meetings. This year, the program incorporated COSI sessions including proceedings, highlights (previously published research), and late-breaking research talks in addition to special sessions, workshop, posters and other scientific talks submitted directly to ISMB/ECCB. Thematic areas of ISMB/ECCB 2017. Areas have been defined as Communities of Special Interest (COSIs), except ‘Other Topics’ Thematic areas of ISMB/ECCB 2017. Areas have been defined as Communities of Special Interest (COSIs), except ‘Other Topics’ ISMB/ECCB is the leading international forum in the field for presenting new methods and research results and for facilitating discussions at all levels of expertise. The 45 papers in this volume were selected from 279 original submissions, resulting in an acceptance rate of 16%. Submitted papers accumulated 3.2 reviews on average. The review process included 208 expert reviewers (with some reviewers working for more than one COSI) led by 42 Area Chairs (Table 1) to produce 899 reviews. The Area Chairs were appointed by the individual COSIs, and included a mix of experienced individuals intimately familiar with the field as well as with the review process. Manuscripts submitted to the proceedings track were assigned to COSIs based on authors’ suggestions and on the judgement of Area and Proceedings Chairs. An additional track was created to house all submissions unrelated to any of the existing COSI topics. The submissions in this ‘Other Topics’ area mostly addressed questions in microbiome analysis and evolutionary modeling. With the COSI-based structure, proceedings areas have become more community-driven and dynamic. We believe that going forward in this manner new emerging scientific themes can be detected quicker and represented better. After initial screening, submissions were sent out to expert reviewers selected by the Area Chairs from the program committee. Each paper and its reviews were then discussed online among reviewers and Area Chairs. Finally, Area and Proceedings Chairs discussed the ranked papers to arrive at a subset of 46 papers, which were conditionally accepted. In continuation of the two-tier review process employed at ISMB 2016, Area Chairs, Proceedings Chairs and sometimes the corresponding reviewers inspected the revised papers again. All but one paper were re-submitted. These 45 revised papers were judged to have satisfactorily addressed the reviewers’ comments and were finally accepted into the proceedings. We would like to thank all authors of the 279 submissions for submitting their work. The excellent scientific quality of these papers is pivotal for the impact and the success of the conference. We believe that we have identified 45 outstanding pieces of bioinformatics research for the proceedings track. We note, however, that the review process was subject to some re-organization largely due to the new COSI-based conference format. It is, therefore, possible that excellent papers have not been fully recognized as such. Nevertheless, we know that submissions were evaluated fairly and diligently and hope that all authors have received useful feedback. Like the conference itself, the review and selection process of the proceedings papers is a community effort. We would like to express our deep gratitude to all Area Chairs, members of the program committee, and external subreviewers for their outstanding efforts and rigorous focus on scientific quality. We would like to express deep gratitude to Steven Leard for all help with the review process—without his organizational support our work would not be possible. We would also like to thank the team at Oxford University Press for consistently handling this ISMB/ECCB special proceedings issue. Last, but certainly not least, we thank all members of the ISMB Steering Committee for their expert advice and support. Conflict of Interest: none declared.

Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease.

Abstract Contact: [email protected] or [email protected] .

Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.

Disease-causing aberrations in the normal function of a gene define that gene as a disease gene. Proving a causal link between a gene and a disease experimentally is expensive and time-consuming. Comprehensive prioritization of candidate genes prior to experimental testing drastically reduces the associated costs. Computational gene prioritization is based on various pieces of correlative evidence that associate each gene with the given disease and suggest possible causal links. A fair amount of this evidence comes from high-throughput experimentation. Thus, well-developed methods are necessary to reliably deal with the quantity of information at hand. Existing gene prioritization techniques already significantly improve the outcomes of targeted experimental studies. Faster and more reliable techniques that account for novel data types are necessary for the development of new diagnostics, treatments, and cure for many diseases.

peer reviewed

An international consortium released the first draft sequence of the human genome 10 years ago. Although the analysis of this data has suggested the genetic underpinnings of many diseases, we have not yet been able to fully quantify the relationship between genotype and phenotype. Thus, a major current effort of the scientific community focuses on evaluating individual predispositions to specific phenotypic traits given their genetic backgrounds. Many resources aim to identify and annotate the specific genes responsible for the observed phenotypes. Some of these use intra-species genetic variability as a means for better understanding this relationship. In addition, several online resources are now dedicated to collecting single nucleotide variants and other types of variants, and annotating their functional effects and associations with phenotypic traits. This information has enabled researchers to develop bioinformatics tools to analyze the rapidly increasing amount of newly extracted variation data and to predict the effect of uncharacterized variants. In this work, we review the most important developments in the field--the databases and bioinformatics tools that will be of utmost importance in our concerted effort to interpret the human variome.

UNLABELLED: Many existing databases annotate experimentally characterized single nucleotide polymorphisms (SNPs). Each non-synonymous SNP (nsSNP) changes one amino acid in the gene product (single amino acid substitution;SAAS). This change can either affect protein function or be neutral in that respect. Most polymorphisms lack experimental annotation of their functional impact. Here, we introduce SNPdbe-SNP database of effects, with predictions of computationally annotated functional impacts of SNPs. Database entries represent nsSNPs in dbSNP and 1000 Genomes collection, as well as variants from UniProt and PMD. SAASs come from >2600 organisms; 'human' being the most prevalent. The impact of each SAAS on protein function is predicted using the SNAP and SIFT algorithms and augmented with experimentally derived function/structure information and disease associations from PMD, OMIM and UniProt. SNPdbe is consistently updated and easily augmented with new sources of information. The database is available as an MySQL dump and via a web front end that allows searches with any combination of organism names, sequences and mutation IDs. AVAILABILITY: http://www.rostlab.org/services/snpdbe.

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.

Abstract Summary: Many non-synonymous single nucleotide polymor-phisms (nsSNPs) in humans are suspected to impact protein function. Here, we present a publicly available server implementation of the method SNAP (screening for non-acceptable polymorphisms) that predicts the functional effects of single amino acid substitutions. SNAP identifies over 80% of the non-neutral mutations at 77% accuracy and over 76% of the neutral mutations at 80% accuracy at its default threshold. Each prediction is associated with a reliability index that correlates with accuracy and thereby enables experimentalists to zoom into the most promising predictions. Availability: Web-server: http://www.rostlab.org/services/SNAP; downloadable program available upon request. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.