The evaluation of protein folding rate constant is improved by predicting the folding kinetic order with a SVM-based method

Protein folding is a problem of large interest since it concerns the mechanism by which the genetic information is translated into proteins with well defined three-dimensional (3D) structures and functions. Recently theoretical models have been developed to predict the protein folding rate considering the relationships of the process with tolopological parameters derived from the native (atomic-solved) protein structures. Previous works classified proteins in two different groups exhibiting either a single-exponential or a multi-exponential folding kinetics. It is well known that these two classes of proteins are related to different protein structural features. The increasing number of available experimental kinetic data allows the application to the problem of a machine learning approach, in order to predict the kinetic order of the folding process starting from the experimental data so far collected. This information can be used to improve the prediction of the folding rate. In this work first we describe a support vector machine-based method (SVM-KO) to predict for a given protein the kinetic order of the folding process. Using this method we can classify correctly 78% of the folding mechanisms over a set of 63 experimental data. Secondly we focus on the prediction of the logarithm of the folding rate. This value can be obtained as a linear regression task with a SVM-based method. In this paper we show that linear correlation of the predicted with experimental data can improve when the regression task is computed over two different sets, instead of one, each of them composed by the proteins with a correctly predicted two state or multistate kinetic order.Comment: The paper will be published on WSEAS Transaction on Biology and Biomedicin

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

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

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

One of the primary challenges in human genetics is determining the functional impact of single nucleotide variants (SNVs) and insertion and deletions (InDels), whether coding or noncoding. In the past, methods have been created to detect disease-related single amino acid changes, but only some can assess the influence of noncoding variations. CADD is the most commonly used and advanced algorithm for predicting the diverse effects of genome variations. It employs a combination of sequence conservation and functional features derived from the ENCODE project data. To use CADD, a large set of pre-calculated information must be downloaded during the installation process. To streamline the variant annotation process, we developed PhD-SNPg, a machine-learning tool that is easy to install and lightweight, relying solely on sequence-based features. Here we present an updated version, trained on a larger dataset, that can also predict the impact of the InDel variations. Despite its simplicity, PhD-SNPg performs similarly to CADD, making it ideal for rapid genome interpretation and as a benchmark for tool development

I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure

I-Mutant2.0 is a support vector machine (SVM)-based tool for the automatic prediction of protein stability changes upon single point mutations. I-Mutant2.0 predictions are performed starting either from the protein structure or, more importantly, from the protein sequence. This latter task, to the best of our knowledge, is exploited for the first time. The method was trained and tested on a data set derived from ProTherm, which is presently the most comprehensive available database of thermodynamic experimental data of free energy changes of protein stability upon mutation under different conditions. I-Mutant2.0 can be used both as a classifier for predicting the sign of the protein stability change upon mutation and as a regression estimator for predicting the related ΔΔG values. Acting as a classifier, I-Mutant2.0 correctly predicts (with a cross-validation procedure) 80% or 77% of the data set, depending on the usage of structural or sequence information, respectively. When predicting ΔΔG values associated with mutations, the correlation of predicted with expected/experimental values is 0.71 (with a standard error of 1.30 kcal/mol) and 0.62 (with a standard error of 1.45 kcal/mol) when structural or sequence information are respectively adopted. Our web interface allows the selection of a predictive mode that depends on the availability of the protein structure and/or sequence. In this latter case, the web server requires only pasting of a protein sequence in a raw format. We therefore introduce I-Mutant2.0 as a unique and valuable helper for protein design, even when the protein structure is not yet known with atomic resolution. Availability:

Challenges in predicting stabilizing variations: An exploration

An open challenge of computational and experimental biology is understanding the impact of non-synonymous DNA variations on protein function and, subsequently, human health. The effects of these variants on protein stability can be measured as the difference in the free energy of unfolding (ΔΔG) between the mutated structure of the protein and its wild-type form. Throughout the years, bioinformaticians have developed a wide variety of tools and approaches to predict the ΔΔG. Although the performance of these tools is highly variable, overall they are less accurate in predicting ΔΔG stabilizing variations rather than the destabilizing ones. Here, we analyze the possible reasons for this difference by focusing on the relationship between experimentally-measured ΔΔG and seven protein properties on three widely-used datasets (S2648, VariBench, Ssym) and a recently introduced one (S669). These properties include protein structural information, different physical properties and statistical potentials. We found that two highly used input features, i.e., hydrophobicity and the Blosum62 substitution matrix, show a performance close to random choice when trying to separate stabilizing variants from either neutral or destabilizing ones. We then speculate that, since destabilizing variations are the most abundant class in the available datasets, the overall performance of the methods is higher when including features that improve the prediction for the destabilizing variants at the expense of the stabilizing ones. These findings highlight the need of designing predictive methods able to exploit also input features highly correlated with the stabilizing variants. New tools should also be tested on a not-artificially balanced dataset, reporting the performance on all the three classes (i.e., stabilizing, neutral and destabilizing variants) and not only the overall results

STRUCTURE COMPARISON AND ALIGNMENT

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Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the \u201cperturbation\u201d of the protein stability upon a missense variant (in terms of absolute \u394\u394G value, i.e., |\u394\u394G|) 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

DDGun: an untrained predictor of protein stability changes upon amino acid variants

Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction

Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve