Pier Luigi Martelli

INPS: predicting the impact of non-synonymous variations on protein stability from sequence.

MOTIVATIONnA tool for reliably predicting the impact of variations on protein stability is extremely important for both protein engineering and for understanding the effects of Mendelian and somatic mutations in the genome. Next Generation Sequencing studies are constantly increasing the number of protein sequences. Given the huge disproportion between protein sequences and structures, there is a need for tools suited to annotate the effect of mutations starting from protein sequence without relying on the structure. Here, we describe INPS, a novel approach for annotating the effect of non-synonymous mutations on the protein stability from its sequence. INPS is based on SVM regression and it is trained to predict the thermodynamic free energy change upon single-point variations in protein sequences.nnnRESULTSnWe show that INPS performs similarly to the state-of-the-art methods based on protein structure when tested in cross-validation on a non-redundant dataset. INPS performs very well also on a newly generated dataset consisting of a number of variations occurring in the tumor suppressor protein p53. Our results suggest that INPS is a tool suited for computing the effect of non-synonymous polymorphisms on protein stability when the protein structure is not available. We also show that INPS predictions are complementary to those of the state-of-the-art, structure-based method mCSM. When the two methods are combined, the overall prediction on the p53 set scores significantly higher than those of the single methods.nnnAVAILABILITY AND IMPLEMENTATIONnThe presented method is available as web server at http://inps.biocomp.unibo.it.nnnCONTACTnpiero.fariselli@unibo.itnnnSUPPLEMENTARY INFORMATIONnSupplementary Materials are available at Bioinformatics online.

TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs.

SUMMARYnTargeting peptides are N-terminal sorting signals in proteins that promote their translocation to mitochondria through the interaction with different protein machineries. We recently developed TPpred, a machine learning-based method scoring among the best ones available to predict the presence of a targeting peptide into a protein sequence and its cleavage site. Here we introduce TPpred2 that improves TPpred performances in the task of identifying the cleavage site of the targeting peptides. TPpred2 is now available as a web interface and as a stand-alone version for users who can freely download and adopt it for processing large volumes of sequences. Availability and implementaion: TPpred2 is available both as web server and stand-alone version at http://tppred2.biocomp.unibo.it.nnnCONTACTngigi@biocomp.unibo.itnnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

INPS-MD: a web server to predict stability of protein variants from sequence and structure

MOTIVATIONnProtein function depends on its structural stability. The effects of single point variations on protein stability can elucidate the molecular mechanisms of human diseases and help in developing new drugs. Recently, we introduced INPS, a method suited to predict the effect of variations on protein stability from protein sequence and whose performance is competitive with the available state-of-the-art tools.nnnRESULTSnIn this article, we describe INPS-MD (Impact of Non synonymous variations on Protein Stability-Multi-Dimension), a web server for the prediction of protein stability changes upon single point variation from protein sequence and/or structure. Here, we complement INPS with a new predictor (INPS3D) that exploits features derived from protein 3D structure. INPS3D scores with Pearsons correlation to experimental ΔΔG values of 0.58 in cross validation and of 0.72 on a blind test set. The sequence-based INPS scores slightly lower than the structure-based INPS3D and both on the same blind test sets well compare with the state-of-the-art methods.nnnAVAILABILITY AND IMPLEMENTATIONnINPS and INPS3D are available at the same web server: http://inpsmd.biocomp.unibo.itnnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.nnnCONTACTngigi@biocomp.unibo.it.

TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins

MOTIVATIONnMolecular recognition of N-terminal targeting peptides is the most common mechanism controlling the import of nuclear-encoded proteins into mitochondria and chloroplasts. When experimental information is lacking, computational methods can annotate targeting peptides, and determine their cleavage sites for characterizing protein localization, function, and mature protein sequences. The problem of discriminating mitochondrial from chloroplastic propeptides is particularly relevant when annotating proteomes of photosynthetic Eukaryotes, endowed with both types of sequences.nnnRESULTSnHere, we introduce TPpred3, a computational method that given any Eukaryotic protein sequence performs three different tasks: (i) the detection of targeting peptides; (ii) their classification as mitochondrial or chloroplastic and (iii) the precise localization of the cleavage sites in an organelle-specific framework. Our implementation is based on our TPpred previously introduced. Here, we integrate a new N-to-1 Extreme Learning Machine specifically designed for the classification task (ii). For the last task, we introduce an organelle-specific Support Vector Machine that exploits sequence motifs retrieved with an extensive motif-discovery analysis of a large set of mitochondrial and chloroplastic proteins. We show that TPpred3 outperforms the state-of-the-art methods in all the three tasks.nnnAVAILABILITY AND IMPLEMENTATIONnThe method server and datasets are available at http://tppred3.biocomp.unibo.it.nnnCONTACTngigi@biocomp.unibo.itnnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

Working toward precision medicine : Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patients disease risk and best therapeutic options by using that individuals genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome‐sequencing data: Crohns disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohns disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships.

Large scale analysis of protein stability in OMIM disease related human protein variants

BackgroundModern genomic techniques allow to associate several Mendelian human diseases to single residue variations in different proteins. Molecular mechanisms explaining the relationship among genotype and phenotype are still under debate. Change of protein stability upon variation appears to assume a particular relevance in annotating whether a single residue substitution can or cannot be associated to a given disease. Thermodynamic properties of human proteins and of their disease related variants are lacking. In the present work, we take advantage of the available three dimensional structure of human proteins for predicting the role of disease related variations on the perturbation of protein stability.ResultsWe develop INPS3D, a new predictor based on protein structure for computing the effect of single residue variations on protein stability (ΔΔG), scoring at the state-of-the-art (Pearson’s correlation value of the regression is equal to 0.72 with mean standard error of 1.15xa0kcal/mol on a blind test set comprising 351 variations in 60 proteins). We then filter 368 OMIM disease related proteins known with atomic resolution (where the three dimensional structure covers at least 70xa0% of the sequence) with 4717 disease related single residue variations and 685 polymorphisms without clinical consequence. We find that the effect on protein stability of disease related variations is larger than the effect of polymorphisms: in particular, by setting to |1xa0kcal/mol| the threshold between perturbing and not perturbing variations of the protein stability, about 44xa0% of disease related variations and 20xa0% of polymorphisms are predicted with |ΔΔG|u2009>u20091xa0kcal/mol, respectively. A consistent fraction of OMIM disease related variations is however predicted to promote |ΔΔG|u2009≤u20091xa0kcal/mol and we focus here on detecting features that can be associated to the thermodynamic property of the protein variant. Our analysis reveals that some 47xa0% of disease related variations promoting |ΔΔG|u2009≤u20091 are located in solvent exposed sites of the protein structure. We also find that the increase of the fraction of variations that in proteins are predicted with |ΔΔG|u2009≤u20091xa0kcal/mol, partially relates with the increasing number of the protein interacting partners, corroborating the notion that disease related, non-perturbing variations are likely to impair protein-protein interaction (70xa0% of the disease causing variations, with high accessible surface are indeed predicted in interacting sites). The set of OMIM surface accessible variations with |ΔΔG|u2009≤u20091xa0kcal/mol and located in interaction sites are 23xa0% of the total in 161 proteins. Among these, 43 proteins with some 327 disease causing variations are involved in signalling, structural biological processes, development and differentiation.ConclusionsWe compute the effect of disease causing variations on protein stability with INPS3D, a new state-of-the-art tool for predicting the change in ΔΔG value associated to single residue substitution in protein structures.xa0 The analysis indicates that OMIM disease related variations in proteins promote a much larger effect on protein stability than polymorphisms non-associated to diseases. Disease related variations with a slight effect on protein stability (|ΔΔG|u2009<u20091 kcal/mol) frequently occur at the protein accessible surface suggesting that they are located in protein-protein interactions patches in putative human biological functional networks. The hypothesis is corroborated by proving that proteins with many disease related variations that slightly perturb protein stability are on average more connected in the human physical interactome (IntAct) than proteins with variations predicted with |ΔΔG|u2009>u20091 kcal/mol.xa0

DeepSig: deep learning improves signal peptide detection in proteins

Motivation: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results: Here, we present DeepSig, an improved approach for signal peptide detection and cleavage‐site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state‐of‐the‐art approaches on both signal peptide detection and precise cleavage‐site identification. Availability and implementation: DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. Contact: pierluigi.martelli@unibo.it Supplementary information: Supplementary data are available at Bioinformatics online.

Benchmarking predictions of allostery in liver pyruvate kinase in CAGI4

The Critical Assessment of Genome Interpretation (CAGI) is a global community experiment to objectively assess computational methods for predicting phenotypic impacts of genomic variation. One of the 2015–2016 competitions focused on predicting the influence of mutations on the allosteric regulation of human liver pyruvate kinase. More than 30 different researchers accessed the challenge data. However, only four groups accepted the challenge. Features used for predictions ranged from evolutionary constraints, mutant site locations relative to active and effector binding sites, and computational docking outputs. Despite the range of expertise and strategies used by predictors, the best predictions were marginally greater than random for modified allostery resulting from mutations. In contrast, several groups successfully predicted which mutations severely reduced enzymatic activity. Nonetheless, poor predictions of allostery stands in stark contrast to the impression left by more than 700 PubMed entries identified using the identifiers “computational + allosteric.” This contrast highlights a specialized need for new computational tools and utilization of benchmarks that focus on allosteric regulation.

ISPRED4: interaction sites PREDiction in protein structures with a refining grammar model

Motivation: The identification of protein‐protein interaction (PPI) sites is an important step towards the characterization of protein functional integration in the cell complexity. Experimental methods are costly and time‐consuming and computational tools for predicting PPI sites can fill the gaps of PPI present knowledge. Results: We present ISPRED4, an improved structure‐based predictor of PPI sites on unbound monomer surfaces. ISPRED4 relies on machine‐learning methods and it incorporates features extracted from protein sequence and structure. Cross‐validation experiments are carried out on a new dataset that includes 151 high‐resolution protein complexes and indicate that ISPRED4 achieves a per‐residue Matthew Correlation Coefficient of 0.48 and an overall accuracy of 0.85. Benchmarking results show that ISPRED4 is one of the top‐performing PPI site predictors developed so far. Contact: gigi@biocomp.unibo.it Availability and Implementation: ISPRED4 and datasets used in this study are available at http://ispred4.biocomp.unibo.it.

eDGAR: a database of Disease-Gene Associations with annotated Relationships among genes

BackgroundGenetic investigations, boosted by modern sequencing techniques, allow dissecting the genetic component of different phenotypic traits. These efforts result in the compilation of lists of genes related to diseases and show that an increasing number of diseases is associated with multiple genes. Investigating functional relations among genes associated with the same disease contributes to highlighting molecular mechanisms of the pathogenesis.ResultsWe present eDGAR, a database collecting and organizing the data on gene/disease associations as derived from OMIM, Humsavar and ClinVar. For each disease-associated gene, eDGAR collects information on its annotation. Specifically, for lists of genes, eDGAR provides information on: i) interactions retrieved from PDB, BIOGRID and STRING; ii) co-occurrence in stable and functional structural complexes; iii) shared Gene Ontology annotations; iv) shared KEGG and REACTOME pathways; v) enriched functional annotations computed with NET-GE; vi) regulatory interactions derived from TRRUST; vii) localization on chromosomes and/or co-localisation in neighboring loci. The present release of eDGAR includes 2672 diseases, related to 3658 different genes, for a total number of 5729 gene-disease associations. 71% of the genes are linked to 621 multigenic diseases and eDGAR highlights their common GO terms, KEGG/REACTOME pathways, physical and regulatory interactions. eDGAR includes a network based enrichment method for detecting statistically significant functional terms associated to groups of genes.ConclusionseDGAR offers a resource to analyze disease-gene associations. In multigenic diseases genes can share physical interactions and/or co-occurrence in the same functional processes. eDGAR is freely available at: edgar.biocomp.unibo.it