Piero Fariselli

CCHMM_PROF: a HMM-based Coiled-Coil Predictor with Evolutionary Information

MOTIVATIONnThe widespread coiled-coil structural motif in proteins is known to mediate a variety of biological interactions. Recognizing a coiled-coil containing sequence and locating its coiled-coil domains are key steps towards the determination of the protein structure and function. Different tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods.nnnRESULTSnIn this article, we introduce a hidden Markov model (CCHMM_PROF) that exploits the information contained in multiple sequence alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches an accuracy of 80% at the residue level and a best per-segment and per-protein efficiency of 81% and 80%, respectively. The results indicate that CCHMM_PROF outperforms all the existing tools and can be adopted for large-scale genome annotation.nnnAVAILABILITYnThe dataset is available at http://www.biocomp.unibo.it/ approximately lisa/coiled-coils. The predictor is freely available at http://gpcr.biocomp.unibo.it/cgi/predictors/cchmmprof/pred_cchmmprof.cgi.nnnCONTACTnpiero@biocomp.unibo.it.

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.

AN AB INITIO STUDY OF THE DIOXYGEN BINDING SITE OF HEMOCYANIN : A COMPARISON BETWEEN CASSCF, CASPT2, AND DFT APPROACHES

Accurate ab initio CASSCF, CASPT2, and DFT computations have been performed on three different model systems which emulate the oxygenated active site of hemocyanin (a Cu+ − Cu+ dimer that binds oxygen as peroxide to form oxyhemocyanin). The three models differ in the number of the ammonia molecules (0, 4, and 6 molecules, respectively) which emulate the real histidine metal ligands of the protein matrix. While the CASSCF computations indicate that the ground state wave function of the oxyhemocianin active site is in all cases a singlet, the CASPT2 and the DFT approaches provide a significantly different description and suggest that the greater stability of the singlet versus the triplet state (experimentally observed) is not an intrinsic property of the oxygenated form of the hemocyanin active site but depends on the presence of ligands on copper atoms. These results indicate that the dynamic correlation contributions (included in the CASPT2 and DFT methods) are essential to obtain a proper description of these systems that cannot be correctly emutated using models where metal ligands are not included.

Analysis of hard protein corona composition on selective iron oxide nanoparticles by MALDI-TOF mass spectrometry: identification and amplification of a hidden mastitis biomarker in milk proteome

AbstractSurface active maghemite nanoparticles (SAMNs) are able to recognize and bind selected proteins in complex biological systems, forming a hard protein corona. Upon a 5-min incubation in bovine whey from mastitis-affected cows, a significant enrichment of a single peptide characterized by a molecular weight at 4338xa0Da originated from the proteolysis of aS1-casein was observed. Notably, among the large number of macromolecules in bovine milk, the detection of this specific peptide can hardly be accomplished by conventional analytical techniques. The selective formation of a stable binding between the peptide and SAMNs is due to the stability gained by adsorption-induced surface restructuration of the nanomaterial. We attributed the surface recognition properties of SAMNs to the chelation of iron(III) sites on their surface by sterically compatible carboxylic groups of the peptide. The specific peptide recognition by SAMNs allows its easy determination by MALDI-TOF mass spectrometry, and a threshold value of its normalized peak intensity was identified by a logistic regression approach and suggested for the rapid diagnosis of the pathology. Thus, the present report proposes the analysis of hard protein corona on nanomaterials as a perspective for developing fast analytical procedures for the diagnosis of mastitis in cows. Moreover, the huge simplification of proteome complexity by exploiting the selectivity derived by the peculiar SAMN surface topography, due to the iron(III) distribution pattern, could be of general interest, leading to competitive applications in food science and in biomedicine, allowing the rapid determination of hidden biomarkers by a cutting edge diagnostic strategy.n Graphical abstractThe topography of iron(III) sites on surface active maghemite nanoparticles (SAMNs) allows the recognition of sterically compatible carboxylic groups on proteins and peptides in complex biological matrixes. The analysis of hard protein corona on SAMNs led to the determination of a biomarker for cow mastitis in milk by MALDI-TOF mass spectrometry.

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.

PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants

Abstract One of the major challenges in human genetics is to identify functional effects of coding and non-coding single nucleotide variants (SNVs). In the past, several methods have been developed to identify disease-related single amino acid changes but only few tools are able to score the impact of non-coding variants. Among the most popular algorithms, CADD and FATHMM predict the effect of SNVs in non-coding regions combining sequence conservation with several functional features derived from the ENCODE project data. Thus, to run CADD or FATHMM locally, the installation process requires to download a large set of pre-calculated information. To facilitate the process of variant annotation we develop PhD-SNPg, a new easy-to-install and lightweight machine learning method that depends only on sequence-based features. Despite this, PhD-SNPg performs similarly or better than more complex methods. This makes PhD-SNPg ideal for quick SNV interpretation, and as benchmark for tool development. Availability: PhD-SNPg is accessible at http://snps.biofold.org/phd-snpg.

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.

NET-GE: a web-server for NETwork-based human gene enrichment

MOTIVATIONnGene enrichment is a requisite for the interpretation of biological complexity related to specific molecular pathways and biological processes. Furthermore, when interpreting NGS data and human variations, including those related to pathologies, gene enrichment allows the inclusion of other genes that in the human interactome space may also play important key roles in the emergency of the phenotype. Here, we describe NET-GE, a web server for associating biological processes and pathways to sets of human proteins involved in the same phenotype RESULTS: NET-GE is based on protein-protein interaction networks, following the notion that for a set of proteins, the context of their specific interactions can better define their function and the processes they can be related to in the biological complexity of the cell. Our method is suited to extract statistically validated enriched terms from Gene Ontology, KEGG and REACTOME annotation databases. Furthermore, NET-GE is effective even when the number of input proteins is small.nnnAVAILABILITY AND IMPLEMENTATIONnNET-GE web server is publicly available and accessible at http://net-ge.biocomp.unibo.it/enrich CONTACT: gigi@biocomp.unibo.itSupplementary information: Supplementary data are available at Bioinformatics online.

Blind prediction of deleterious amino acid variations with SNPs&GO

SNPs&GO is a machine learning method for predicting the association of single amino acid variations (SAVs) to disease, considering protein functional annotation. The method is a binary classifier that implements a support vector machine algorithm to discriminate between disease‐related and neutral SAVs. SNPs&GO combines information from protein sequence with functional annotation encoded by gene ontology (GO) terms. Tested in sequence mode on more than 38,000 SAVs from the SwissVar dataset, our method reached 81% overall accuracy and an area under the receiving operating characteristic curve of 0.88 with low false‐positive rate. In almost all the editions of the Critical Assessment of Genome Interpretation (CAGI) experiments, SNPs&GO ranked among the most accurate algorithms for predicting the effect of SAVs. In this paper, we summarize the best results obtained by SNPs&GO on disease‐related variations of four CAGI challenges relative to the following genes: CHEK2 (CAGI 2010), RAD50 (CAGI 2011), p16‐INK (CAGI 2013), and NAGLU (CAGI 2016). Result evaluation provides insights about the accuracy of our algorithm and the relevance of GO terms in annotating the effect of the variants. It also helps to define good practices for the detection of deleterious SAVs.

SChloro: directing Viridiplantae proteins to six chloroplastic sub-compartments

Motivation: Chloroplasts are organelles found in plants and involved in several important cell processes. Similarly to other compartments in the cell, chloroplasts have an internal structure comprising several sub‐compartments, where different proteins are targeted to perform their functions. Given the relation between protein function and localization, the availability of effective computational tools to predict protein sub‐organelle localizations is crucial for large‐scale functional studies. Results: In this paper we present SChloro, a novel machine‐learning approach to predict protein sub‐chloroplastic localization, based on targeting signal detection and membrane protein information. The proposed approach performs multi‐label predictions discriminating six chloroplastic sub‐compartments that include inner membrane, outer membrane, stroma, thylakoid lumen, plastoglobule and thylakoid membrane. In comparative benchmarks, the proposed method outperforms current state‐of‐the‐art methods in both single‐ and multi‐compartment predictions, with an overall multi‐label accuracy of 74%. The results demonstrate the relevance of the approach that is eligible as a good candidate for integration into more general large‐scale annotation pipelines of protein subcellular localization. Availability and Implementation: The method is available as web server at http://schloro.biocomp.unibo.it Contact: gigi@biocomp.unibo.it.