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Collectively, rare genetic disorders affect a substantial portion of the world’s population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

During the last years, the increasing number of DNA sequencing and protein mutagenesis studies has generated a large amount of variation data published in the biomedical literature. The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. In particular, the development of methods for predicting the effect of amino acid variants on protein stability relies on the thermodynamic data extracted from literature. In the past, such data were deposited in the ProTherm database, which however is no longer maintained since 2013. For facilitating the collection of protein thermodynamic data from literature, we developed the semi-automatic tool ThermoScan. ThermoScan is a text mining approach for the identification of relevant thermodynamic data on protein stability from full-text articles. The method relies on a regular expression searching for groups of words, including the most common conceptual words appearing in experimental studies on protein stability, several thermodynamic variables, and their units of measure. ThermoScan analyzes full-text articles from the PubMed Central Open Access subset and calculates an empiric score that allows the identification of manuscripts reporting thermodynamic data on protein stability. The method was optimized on a set of publications included in the ProTherm database, and tested on a new curated set of articles, manually selected for presence of thermodynamic data. The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts.Availability: The ThermoScan server is freely accessible online at https://folding.biofold.org/thermoscan. The ThermoScan python code and the Google Chrome extension for submitting visualized PMC web pages to the ThermoScan server are available at https://github.com/biofold/ThermoScan.

Graphical abstract, Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

Summary Identifying pathogenic variants and annotating them is a major challenge in human genetics, especially for the non-coding ones. Several tools have been developed and used to predict the functional effect of genetic variants. However, the calibration assessment of the predictions has received little attention. Calibration refers to the idea that if a model predicts a group of variants to be pathogenic with a probability P, it is expected that the same fraction P of true positive is found in the observed set. For instance, a well-calibrated classifier should label the variants such that among the ones to which it gave a probability value close to 0.7, approximately 70% actually belong to the pathogenic class. Poorly calibrated algorithms can be misleading and potentially harmful for clinical decision making. Avaliability and implementation The dataset used for testing the methods is available through the DOI:10.5281/zenodo.4448197. Supplementary information Supplementary data are available at Bioinformatics online.

Background Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (∆∆G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted ∆∆G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., ∆∆G(A → B) = −∆∆G(B → A), where A and B are amino acids. Results Here we introduce simple anti-symmetric features, based on evolutionary information, which are combined to define an untrained method, DDGun (DDG untrained). DDGun is a simple approach based on evolutionary information that predicts the ∆∆G for single and multiple variations from sequence and structure information (DDGun3D). Our method achieves remarkable performance without any training on the experimental datasets, reaching Pearson correlation coefficients between predicted and measured ∆∆G values of ~ 0.5 and ~ 0.4 for single and multiple site variations, respectively. Surprisingly, DDGun performances are comparable with those of state of the art methods. DDGun also naturally predicts multiple site variations, thereby defining a benchmark method for both single site and multiple site predictors. DDGun is anti-symmetric by construction predicting the value of the ∆∆G of a reciprocal variation as almost equal (depending on the sequence profile) to -∆∆G of the direct variation. This is a valuable property that is missing in the majority of the methods. Conclusions Evolutionary information alone combined in an untrained method can achieve remarkably high performances in the prediction of ∆∆G upon protein mutation. Non-trained approaches like DDGun represent a valid benchmark both for scoring the predictive power of the individual features and for assessing the learning capability of supervised methods. Electronic supplementary material The online version of this article (10.1186/s12859-019-2923-1) contains supplementary material, which is available to authorized users.

Motivation: The recent advance in high-throughput sequencing technologies is generating a huge amount of data that are becoming an important resource for deciphering the genotype underlying a given phenotype. Genome sequencing has been extensively applied to the study of the cancer genomes. Although a few methods have been already proposed for the detection of cancer-related genes, their automatic identification is still a challenging task. Using the genomic data made available by The Cancer Genome Atlas Consortium (TCGA), we propose a new prioritization approach based on the analysis of the distribution of putative deleterious variants in a large cohort of cancer samples. Results: In this paper, we present ContastRank, a new method for the prioritization of putative impaired genes in cancer. The method is based on the comparison of the putative defective rate of each gene in tumor versus normal and 1000 genome samples. We show that the method is able to provide a ranked list of putative impaired genes for colon, lung and prostate adenocarcinomas. The list significantly overlaps with the list of known cancer driver genes previously published. More importantly, by using our scoring approach, we can successfully discriminate between TCGA normal and tumor samples. A binary classifier based on ContrastRank score reaches an overall accuracy >90% and the area under the curve (AUC) of receiver operating characteristics (ROC) >0.95 for all the three types of adenocarcinoma analyzed in this paper. In addition, using ContrastRank score, we are able to discriminate the three tumor types with a minimum overall accuracy of 77% and AUC of 0.83. Conclusions: We describe ContrastRank, a method for prioritizing putative impaired genes in cancer. The method is based on the comparison of exome sequencing data from different cohorts and can detect putative cancer driver genes. ContrastRank can also be used to estimate a global score for an individual genome about the risk of adenocarcinoma based on the genetic variants information from a whole-exome VCF (Variant Calling Format) file. We believe that the application of ContrastRank can be an important step in genomic medicine to enable genome-based diagnosis. Availability and implementation: The lists of ContrastRank scores of all genes in each tumor type are available as supplementary materials. A webserver for evaluating the risk of the three studied adenocarcinomas starting from whole-exome VCF file is under development. Contact: emidio@uab.edu Supplementary information: Supplementary data are available at Bioinformatics online.

A computational approach is essential whenever the complexity of the process under study is such that direct theoretical or experimental approaches are not viable. This is the case for protein folding, for which a significant amount of data are being collected. This paper reports on the essential role of in silico methods and the unprecedented interplay of computational and theoretical approaches, which is a defining point of the interdisciplinary investigations of the protein folding process. Besides giving an overview of the available computational methods and tools, we argue that computation plays not merely an ancillary role but has a more constructive function in that computational work may precede theory and experiments. More precisely, computation can provide the primary conceptual clues to inspire subsequent theoretical and experimental work even in a case where no preexisting evidence or theoretical frameworks are available. This is cogently manifested in the application of machine learning methods to come to grips with the folding dynamics. These close relationships suggested complementing the review of computational methods within the appropriate theoretical context to provide a self-contained outlook of the basic concepts that have converged into a unified description of folding and have grown in a synergic relationship with their computational counterpart. Finally, the advantages and limitations of current computational methodologies are discussed to show how the smart analysis of large amounts of data and the development of more effective algorithms can improve our understanding of protein folding. © 2013 American Chemical Society.

Developmental checkpoints eliminate B cells that synthesize defective immunoglobulin (Ig) heavy (HC) and light (LC) chains. The first checkpoint tests μHCs paired with VpreB/λ5 in a pre-B cell receptor (pre-BCR) to determine whether the μHC will be able to bind conventional LCs to form membrane IgM. VpreB and λ5 also create a sensing site that interacts with the μHC antigen-binding region complementarity-determining region (CDR)–H3; however, whether this site contributes to Ig repertoire selection and function is unknown. We analyzed the amino acid content of CDR-H3s from HCs cloned from living and apoptotic pre-B cells and from IgG-antigen structures. Using a panel of D H gene–targeted mice, we showed that progressively reducing CDR-H3 tyrosine content increasingly impaired pre-BCR checkpoint passage. Counting from cysteine at framework 3 position 96, we found that VpreB particularly selected for tyrosine at CDR-H3 position 101 and that Y101 also bound antigen in IgG-antigen structures. Therefore, in addition to its stabilization role in the pre-BCR, VpreB also acts as an early invariant antigen by selecting for particular CDR-H3 amino acids. These interactions shape the specificity of the IgG humoral response and may thus impose limitations on development of certain neutralizing antibodies.

Motivation: Human single nucleotide polymorphisms (SNPs) are the most frequent type of genetic variation in human population. One of the most important goals of SNP projects is to understand which human genotype variations are related to Mendelian and complex diseases. Great interest is focused on non-synonymous coding SNPs (nsSNPs) that are responsible of protein single point mutation. nsSNPs can be neutral or disease associated. It is known that the mutation of only one residue in a protein sequence can be related to a number of pathological conditions of dramatic social impact such as Altzheimer's, Parkinson's and Creutzfeldt-Jakob's diseases. The quality and completeness of presently available SNPs databases allows the application of machine learning techniques to predict the insurgence of human diseases due to single point protein mutation starting from the protein sequence. Results: In this paper, we develop a method based on support vector machines (SVMs) that starting from the protein sequence information can predict whether a new phenotype derived from a nsSNP can be related to a genetic disease in humans. Using a dataset of 21 185 single point mutations, 61% of which are disease-related, out of 3587 proteins, we show that our predictor can reach more than 74% accuracy in the specific task of predicting whether a single point mutation can be disease related or not. Our method, although based on less information, outperforms other web-available predictors implementing different approaches. Availability: A beta version of the web tool is available at Contact: casadio@alma.unibo.it

Motivation: One important requirement for protein design is to be able to predict changes of protein stability upon mutation. Different methods addressing this task have been described and their performance tested considering global linear correlation between predicted and experimental data. Neither is direct statistical evaluation of their prediction performance available, nor is a direct comparison among different approaches possible. Recently, a significant database of thermodynamic data on protein stability changes upon single point mutation has been generated (ProTherm). This allows the application of machine learning techniques to predicting free energy stability changes upon mutation starting from the protein sequence. Results: In this paper, we present a neural-network-based method to predict if a given mutation increases or decreases the protein thermodynamic stability with respect to the native structure. Using a dataset consisting of 1615 mutations, our predictor correctly classifies >80% of the mutations in the database. On the same task and using the same data, our predictor performs better than other methods available on the Web. Moreover, when our system is coupled with energy-based methods, the joint prediction accuracy increases up to 90%, suggesting that it can be used to increase also the performance of pre-existing methods, and generally to improve protein design strategies. Availability: The server is under construction and will be available at http://www.biocomp.unibo.it

The success of SNP-SIG 2013 (Berlin, Germany), as confirmed by the number of participants and the interesting discussions, indicated the great interest of the community in the automatic annotation of genomic variants. This year the sessions focused on the annotation and prediction of structural/functional impacts of SNPs (morning session) and on the disease and evolution -related SNP perspectives (afternoon session). In the morning session, chaired by Yana Bromberg, the two keynotes were from Ruth Nussinov, National Cancer Institute (Frederick, MD) and Manolis Kellis, Massachusetts Institute of Technology (Cambridge, MA). Dr. Nussinov discussed her investigation of nonsynonymous variants in cancer pathways (here, networks proteins of known 3D structure) to understand the relationship among linked phenomena, e.g. inflammation and cancer. Dr. Kellis addressed the effects of genomic and epigenomic changes on gene regulation. Particularly, he talked about the making predictions of regulatory activity using epigenomic maps of human tissues and specific cell types to expand the annotation of noncoding regions and to provide mechanistic hypotheses of complex disease. This session also hosted four original work presentations by: Christopher Yates, Imperial College of London (London, UK), Lukas Folkman, Griffith University (Brisbane, Australia), Martin Kircher, University of Washington (Seattle, WA), and Bjoern Stade, Christian-Albrechts-University (Kiel, Germany). All presentations described different methods for the annotation and prioritization of single nucleotide variants. After the company presentation (Frank Schacherer, BIOBASE GmbH) and the poster session, Emidio Capriotti chaired the second session to discuss SNPs as effectors of change in disease and evolution. Paul Flicek, European Bioinformatics Institute (Hixton, UK) and Alon Keinan, Cornell University (Ithaca, NY) presented highlight talks in this session. Dr. Flicek focused on the use of comparative genomics analysis to reduce the search space of regulatory variants associated with rare diseases. Particularly, he presented a newly developed method for identifying the thousands of variants in regulatory regions associated with genetic disorders. The major novelty of this method is in the use of functional conservation of regions rather than sequence conservation, which is difficult to detect in non-coding stretched of DNA. Dr. Keinan’s talk presented the results of a population genetics study describing the abundance of rare variants as an effect of the explosive growth of the human population since the Neolithic. The developed theoretical model based on the spectrum of allele frequencies is able to recapitulate the human demographic history. In addition the model has been used to estimate the effects of the load of individual genetic variants in terms of complex disease risk. In the second session there were also three selected presentations by: Graham Ritchie, European Bioinformatics Institute (Cambridge, UK), Andrey Grigoriev, Rutgers University (Camden, NJ) and John Moult, University of Maryland (Rockville, MD). These talks addressed different topics, including the annotation and visualization of non-coding variants and the identification of potential drug targets using GWAS. In the last part of the meeting Steven Brenner, UC Berkeley (Berkeley, CA) summarized the results of the last edition of the Critical Assessment of Genomic Interpretation (CAGI), This was an in-depth report on the discussions taking place in the community of scientists, involved in the development of new computational methods for genomic interpretation. The report mostly addressed issues of proper testing datasets selection in the assessment of the available techniques. Finally, all speakers and participants were invited to discuss the “hot” open topics in the field as part of a round-table session chaired by Sean Mooney, Buck Institute (Novato, CA). The participation of leading scientists in this discussion was an opportunity to define most important challenges for the field. This session also addressed the role of computational biology and bioinformatics in delivering algorithms into clinical settings – no small task for the near future of personalized medicine. In the next few months, a SNP-SIG 2013 BMC Genomics Special Issue, edited by Yana Bromberg and Emidio Capriotti, will be published. This collection of selected works presented in Berlin will be the third issue in the series of proceedings highlighting the lessons learned at the SNP-SIG meeting. After three years of meetings we believe that our SIG has become a well-established venue for the discussion of major advances in the field of genomic interpretation. This is evident from the enthusiastic participation of colleagues and young scientists who strongly contribute to the success of this meeting. In the near future we expect that our SIG could play a central role in the organization of an interdisciplinary Community Of Special Interest (COSI) to understand the relationship between genomic variation and disease.

Summary: Accurate prediction of amyloid-forming amino acid sequences remains an important challenge. We here present an online database that provides open access to the largest set of experimentally characterized amyloid forming hexapeptides. To this end, we expanded our previous set of 280 hexapeptides used to develop the Waltz algorithm with 89 peptides from literature review and by systematic experimental characterisation of the aggregation of 720 hexapeptides by transmission electron microscopy, dye binding and Fourier transform infrared spectroscopy. This brings the total number of experimentally characterized hexapeptides in the WALTZ-DB database to 1089, of which 244 are annotated as positive for amyloid formation. Availability and implementation: The WALTZ-DB database is freely available without any registration requirement at http://waltzdb.switchlab.org. Contact: frederic.rousseau@switch.vib-kuleuven.be or joost.schymkowitz@switch.vib-kuleuven.be

Summary: The understanding of the biological role of RNA molecules has changed. Although it is widely accepted that RNAs play important regulatory roles without necessarily coding for proteins, the functions of many of these non-coding RNAs are unknown. Thus, determining or modeling the 3D structure of RNA molecules as well as assessing their accuracy and stability has become of great importance for characterizing their functional activity. Here, we introduce a new web application, WebRASP, that uses knowledge-based potentials for scoring RNA structures based on distance-dependent pairwise atomic interactions. This web server allows the users to upload a structure in PDB format, select several options to visualize the structure and calculate the energy profile. The server contains online help, tutorials and links to other related resources. We believe this server will be a useful tool for predicting and assessing the quality of RNA 3D structures. Availability and implementation: The web server is available at http://melolab.org/webrasp. It has been tested on the most popular web browsers and requires Java plugin for Jmol visualization. Contact: fmelo@bio.puc.cl

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk-benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein-drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants. © 2012 The Royal Society.

Motivation: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics. Results: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice. Contact: russ.altman@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Over the recent years, the vision that RNA simply serves as information transfer molecule has dramatically changed. The study of the sequence/structure/function relationships in RNA is becoming more important. As a direct consequence, the total number of experimentally solved RNA structures has dramatically increased and new computer tools for predicting RNA structure from sequence are rapidly emerging. Therefore, new and accurate methods for assessing the accuracy of RNA structure models are clearly needed. Results: Here, we introduce an all-atom knowledge-based potential for the assessment of RNA three-dimensional (3D) structures. We have benchmarked our new potential, called Ribonucleic Acids Statistical Potential (RASP), with two different decoy datasets composed of near-native RNA structures. In one of the benchmark sets, RASP was able to rank the closest model to the X-ray structure as the best and within the top 10 models for ~93 and ~95% of decoys, respectively. The average correlation coefficient between model accuracy, calculated as the root mean square deviation and global distance test-total score (GDT-TS) measures of C3′ atoms, and the RASP score was 0.85 and 0.89, respectively. Based on a recently released benchmark dataset that contains hundreds of 3D models for 32 RNA motifs with non-canonical base pairs, RASP scoring function compared favorably to ROSETTA FARFAR force field in the selection of accurate models. Finally, using the self-splicing group I intron and the stem-loop IIIc from hepatitis C virus internal ribosome entry site as test cases, we show that RASP is able to discriminate between known structure-destabilizing mutations and compensatory mutations. Availability: RASP can be readily applied to assess all-atom or coarse-grained RNA structures and thus should be of interest to both developers and end-users of RNA structure prediction methods. The computer software and knowledge-based potentials are freely available at http://melolab.org/supmat.html. Contact:fmelo@bio.puc.cl; mmarti@cipf.es Supplementary information:Supplementary data are available at Bioinformatics online.

Background: Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.Results: In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.Conclusion: This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available. © 2011 Capriotti; licensee BioMed Central Ltd.

Background: In recent years, the number of available RNA structures has rapidly grown reflecting the increased interest on RNA biology. Similarly to the studies carried out two decades ago for proteins, which gave the fundamental grounds for developing comparative protein structure prediction methods, we are now able to quantify the relationship between sequence and structure conservation in RNA.Results: Here we introduce an all-against-all sequence- and three-dimensional (3D) structure-based comparison of a representative set of RNA structures, which have allowed us to quantitatively confirm that: (i) there is a measurable relationship between sequence and structure conservation that weakens for alignments resulting in below 60% sequence identity, (ii) evolution tends to conserve more RNA structure than sequence, and (iii) there is a twilight zone for RNA homology detection.Discussion: The computational analysis here presented quantitatively describes the relationship between sequence and structure for RNA molecules and defines a twilight zone region for detecting RNA homology. Our work could represent the theoretical basis and limitations for future developments in comparative RNA 3D structure prediction. © 2010 Capriotti and Marti-Renom; licensee BioMed Central Ltd.

Motivation: The recent discovery of tiny RNA molecules such as µRNAs and small interfering RNA are transforming the view of RNA as a simple information transfer molecule. Similar to proteins, the native three-dimensional structure of RNA determines its biological activity. Therefore, classifying the current structural space is paramount for functionally annotating RNA molecules. The increasing numbers of RNA structures deposited in the PDB requires more accurate, automatic and benchmarked methods for RNA structure comparison. In this article, we introduce a new algorithm for RNA structure alignment based on a unit-vector approach. The algorithm has been implemented in the SARA program, which results in RNA structure pairwise alignments and their statistical significance. Results: The SARA program has been implemented to be of general applicability even when no secondary structure can be calculated from the RNA structures. A benchmark against the ARTS program using a set of 1275 non-redundant pairwise structure alignments results in ¡«6% extra alignments with at least 50% structurally superposed nucleotides and base pairs. A first attempt to perform RNA automatic functional annotation based on structure alignments indicates that SARA can correctly assign the deepest SCOR classification to >60% of the query structures. Availability: The SARA program is freely available through a World Wide Web server http://sgu.bioinfo.cipf.es/services/SARA/ Contact: mmarti@cipf.es

A basic question of protein structural studies is to which extent mutations affect the stability. This question may be addressed starting from sequence and/or from structure. In proteomics and genomics studies prediction of protein stability free energy change (DDG) upon single point mutation may also help the annotation process. The experimental SSG values are affected by uncertainty as measured by standard deviations. Most of the DDG values are nearly zero (about 32% of the DDG data set ranges from -0.5 to 0.5 Kcal/mol) and both the value and sign of DDG may be either positive or negative for the same mutation blurring the relationship among mutations and expected DDG value. In order to overcome this problem we describe a new predictor that discriminates between 3 mutation classes: destabilizing mutations (DDG0.5 Kcal/mol) and neutral mutations (-0.5, Comment: Text: 9 pages, Figures: 9 pages, Tables: 1 page, Supplemetary Material: 1 page

Summary: K-Fold is a tool for the automatic prediction of the protein folding kinetic order and rate. The tool is based on a support vector machine (SVM) that was trained on a data set of 63 proteins, whose 3D structure and folding mechanism are known from experiments already described in the literature. The method predicts whether a protein of known atomic structure folds according to a two-state or a multi-state kinetics and correctly classifies 81% of the folding mechanisms when tested over the training set of the 63 proteins. It also predicts as a further option the logarithm of the folding rate. To the best of our knowledge, the tool discriminates for the first time whether a protein is characterized by a two state or a multiple state kinetics, during the folding process, and concomitantly estimates also the value of the constant rate of the process. When used to predict the logarithm of the folding rate, K-Fold scores with a correlation value to the experimental data of 0.74 (with a SE of 1.2). Availability: Contact: emidio@biocomp.unibo.it Supplementary information

We wish to propose a novel mechanism by which the triggering of a biochemical signal can be controlled by the hierarchical coupling between a protein redox equilibrium and an external mechanical force. We have characterized this mechanochemical mechanism in angiostatin, and we have evidence that it can switch the access to partially unfolded structures of this protein. We have identified a metastable intermediate that is specifically accessible under thioredoxin-rich reducing conditions, like those met by angiostatin on the surface of a tumor cell. The structure of the same intermediate accounts for the unexplained antiangiogenic activity of angiostatin. These findings demonstrate a new link between redox biology and mechanically regulated processes.

In this article we use mutation studies as a benchmark for a minimal model of the folding process of helical proteins. The model as- cribes a pivotal role to the collisional dynamics of a few crucial residues (foldons) and predicts the fold- ing rates by exploiting information drawn from the protein sequence. We show that our model rational- izes the effects of point mutations on the kinetics of folding. The folding times of two proteins and their mutants are predicted. Stability and location of foldons have a critical role as the determinants of protein folding. This allows us to elucidate two main mechanisms for the kinetic effects of mutations. First, it turns out that the mutations eliciting the most notable effects alter protein stability through stabilization or destabilization of the foldons. Sec- ondly, the folding rate is affected via a modification of the foldon topology by those mutations that lead to the birth or death of foldons. The few mispre- dicted folding rates of some mutants hint at the limits of the current version of the folding model proposed in the present article. The performance of our folding model declines in case the mutated residues are subject to strong long-range forces. That foldons are the critical targets of mutation studies has notable implications for design strate- gies and is of particular interest to address the issue of the kinetic regulation of single proteins in the general context of the overall dynamics of the inter- actome. Proteins 2006;64:198 -209.

Motivation: The prediction of protein stability change upon mutations is key to understanding protein folding and misfolding. At present, methods are available to predict stability changes only when the atomic structure of the protein is available. Methods addressing the same task starting from the protein sequence are, however, necessary in order to complete genome annotation, especially in relation to single nucleotide polymorphisms (SNPs) and related diseases. Results: We develop a method based on support vector machines that, starting from the protein sequence, predicts the sign and the value of free energy stability change upon single point mutation. We show that the accuracy of our predictor is as high as 77% in the specific task of predicting the ΔΔG sign related to the corresponding protein stability. When predicting the ΔΔG values, a satisfactory correlation agreement with the experimental data is also found. As a final blind benchmark, the predictor is applied to proteins with a set of disease-related SNPs, for which thermodynamic data are also known. We found that our predictions corroborate the view that disease-related mutations correspond to a decrease in protein stability. Availability: http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant2.0/I-Mutant2.0.cgi Contact: casadio@alma.unibo.it

Detection of homologous proteins with low-sequence identity to a given target (remote homologues) is routinely performed with alignment algorithms that take advantage of sequence profile. In this article, we investigate the efficacy of differ- ent alignment procedures for the task at hand on a set of 185 protein pairs with similar structures but low-sequence similarity. Criteria based on the SCOP label detection and MaxSub scores are adopted to score the results. We investigate the efficacy of alignments based on sequence-sequence, sequence- profile, and profile-profile information. We confirm that with profile-profile alignments the results are better than with other procedures. In addition, we report, and this is novel, that the selection of the results of the profile-profile alignments can be im- proved by using Shannon entropy, indicating that this parameter is important to recognize good pro- file-profile alignments among a plethora of meaning- less pairs. By this, we enhance the global search accuracy without losing sensitivity and filter out most of the erroneous alignments. We also show that when the entropy filtering is adopted, the quality of the resulting alignments is comparable to that com- puted for the target and template structures with CE, a structural alignment program. Proteins 2004

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

The view of RNA as simple information transfer molecule has been continuously challenged since the discovery of ribozymes, a class of RNA molecules with enzyme-like function. Moreover, the recent discovery of tiny RNA molecules such as μRNAs and small interfering RNA, is transforming our thinking about how gene expression is regulated. Thus, RNA molecules are now known to carry a large repertory of biological functions within cells includift information transfer, enzymatic catalysis and regulation of cellular processes. Similar to proteins, functional RNA molecules fold into their native three-dimensional (3D) conformation, which is essential for performing their biological activity. Despite advances in understanding the folding and unfolding of RNA, our knowledge of the atomic mechanism by which RNA molecules adopt their biological active structure is still limited. In this review, we outline the general principles that govern RNA structure and describe the databases and algorithm for aralyzing and predicting RNA secondary and tertiary structure. Finally, we assess the impact of the current coverage of the RNA structural space on comparative modeling RNA structures. The view of RNA as simple information transfer molecule has been continuously challenged since the discovery of ribozymes, a class of RNA molecules with enzyme-like function. Moreover, the recent discovery of tiny RNA molecules such as μRNAs and small interfering RNA, is transforming our thinking about how gene expression is regulated. Thus, RNA molecules are now known to carry a large repertory of biological functions within cells includift information transfer, enzymatic catalysis and regulation of cellular processes. Similar to proteins, functional RNA molecules fold into their native three-dimensional (3D) conformation, which is essential for performing their biological activity. Despite advances in understanding the folding and unfolding of RNA, our knowledge of the atomic mechanism by which RNA molecules adopt their biological active structure is still limited. In this review, we outline the general principles that govern RNA structure and describe the databases and algorithm for aralyzing and predicting RNA secondary and tertiary structure. Finally, we assess the impact of the current coverage of the RNA structural space on comparative modeling RNA structures. © 2008 Bentham Science Publishers Ltd.

Motivation: Aligning RNAs is useful to search for homologous genes, study evolutionary relationships, detect conserved regions and identify any patterns that may be of biological relevance. Poor levels of conservation among homologs, however, make it difficult to compare RNA sequences, even when considering closely evolutionary related sequences. Results: We describe SARA-Coffee, a tertiary structure-based multiple RNA aligner, which has been validated using BRAliDARTS, a new benchmark framework designed for evaluating tertiary structure–based multiple RNA aligners. We provide two methods to measure the capacity of alignments to match corresponding secondary and tertiary structure features. On this benchmark, SARA-Coffee outperforms both regular aligners and those using secondary structure information. Furthermore, we show that on sequences in which Availability and implementation: The package and the datasets are available from http://www.tcoffee.org/Projects/saracoffee and http://structure.biofold.org/sara/. Contact: cedric.notredame@crg.es Supplementary information: Supplementary data are available at Bioinformatics online