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mCSM / mutation Cutoff Scanning Matrix
Predicts the impact of single-point mutations on protein stability and protein–protein and protein–nucleic acid affinity. mCSM is an approach, which relies on graph-based signatures, for studying the impact of missense mutations in proteins. The software perceives residue environment density and depth implicitly, without relying on direct calculations or thresholds. It was applied to predict stability changes of mutations occurring in p53, demonstrating its applicability in a challenging disease scenario.
AUTO–MUTE / AUTOmated server for predicting functional consequences of amino acid MUTations in protEins
A collection of programs for predicting functional changes to proteins upon single residue substitutions, developed by combining structure-based features with trained statistical learning models. For each type of function prediction, a variety of classification and regression models have been developed and are available for researchers. These include Random Forest, Support Vector Machine (SVM), AdaBoostM1 combined with the C4.5 Decision Tree algorithm, as well as Tree and SVM regression. The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and/or evolutionary information.
Offers a platform for determining protein structural features and tertiary structures. SCRATCH is a web application including ten modules for determining three and eight class: (1) secondary structure, (2) relative solvent accessibility, (3) domain boundaries, (4) disordered regions, (5) disulfide bridges, (6) the effect of single amino acid mutation on stability, (7) residue-residue contact maps, and (8) tertiary structures as well as contacts with other residues compared to average.
A novel ensemble machine learning approach that predicts the effects of mutations on protein folding and protein-protein interactions. The ELASPIC webserver makes the ELASPIC pipeline available through a fast and intuitive interface. The webserver can be used to evaluate the effect of mutations on any protein in the Uniprot database, and allows all predicted results, including modeled wild-type and mutated structures, to be managed and viewed online and downloaded if needed. It is backed by a database which contains improved structural domain definitions, and a list of curated domain-domain interactions for all known proteins, as well as homology models of domains and domain-domain interactions for the human proteome.
Automates design of multiple-point thermostable mutant proteins which combines structural and evolutionary information in its calculation core. FireProt uses sixteen tools and three protein engineering strategies for making reliable protein designs. It allows user to analyze and modify the design of thermostable mutants. The graphical user interface (GUI) provides user to interactively analyze individual mutations selected as a part of energy, or evolution-based approach together with the ability to design their own multiple-point.
A method for predicting changes in stability upon point mutation in proteins. MAESTRO is structure based and distinguishes itself from similar approaches in the following points: (i) MAESTRO implements a multi-agent machine learning system. (ii) It also provides predicted free energy change (ΔΔG) values and a corresponding prediction confidence estimation. (iii) It provides high throughput scanning for multi-point mutations where sites and types of mutation can be comprehensively controlled. (iv) Finally, the software provides a specific mode for the prediction of stabilizing disulfide bonds.
STRUM / STRUcture Modeling
A method for predicting the fold stability change (ΔΔG) of protein molecules upon single-point nsSNP mutations. STRUM adopts a gradient boosting regression approch to train the Gibbs free-energy changes on a variety of features at different levels of sequence and structure properties. The unique characteristics of STRUM is the combination of sequence profiles with low-resolution structure models from protein structure prediction, which helps enhance the robustness and accuracy of the method and make it applicable to various protein sequences, including those without experimental structures. The results showed that STRUM based on predicted structural protein models are comparable with or outperform most of the methods that are built on the experimental structures.
A practical computational pipeline to readily perform data analyses of protein-protein interaction networks (PPINs) by using genetic and functional information mapped onto protein structures. We provide a 3D representation of the available protein structure and its regions (surface, interface, core, and disordered) for the selected genetic variants and/or SNPs, and a prediction of the mutants’ impact on the protein as measured by a range of methods. We have mapped in total 2587 genetic disorder-related SNPs from OMIM, 587 873 cancer-related variants from COSMIC, and 1 484 045 SNPs from dbSNP. All result data can be downloaded by the user together with an R-script to compute the enrichment of SNPs/variants in selected structural regions.
Predicts in silico structural variation (SV) impact. SVScore is a Variant Call Format (VCF) annotation tool which scores structural variants by predicted pathogenicity based on single nucleotide polymorphism (SNP)-based CADD scores. For each variant, SVScore first defines important genomic intervals based on the variant type, breakend confidence intervals, and overlapping exon/intron annotations. It then applies an operation to each interval to aggregate the CADD scores in that interval into an interval score. A score for a given operation defined as the maximum of all interval scores calculated using that operation. SVScore is based on hg19/GRCh37.
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 Pearson's correlation to experimental ∆∆G 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.
Enables the assessment of multiple non-synonymous single nucleotide polymorphisms (nsSNPs) in a single protein by visualizing the mutated residues within the wild type structure, collecting available pathogenicity information from different databases and predicting binding pockets as well as protein stability changes. Based on the generated information and the three-dimensional visualization, a user can assume whether the amino acid substitutions can have a quantitative effect due to mutual interaction or have an influence on binding and stability.
EASE-MM / Evolutionary Amino acid and Structural Encodings with Multiple Models
Comprises five specialised support vector machine (SVM) models and makes the final prediction from a consensus of two models selected based on the predicted secondary structure and accessible surface area of the mutated residue. EASE-MM is applicable to single-domain monomeric proteins and can predict protein stability changes (DeltaDeltaGu) with a protein sequence and mutation as the only inputs. EASE-MM yielded a Pearson correlation coefficient of 0.53-0.59 in 10-fold cross-validation and independent testing and was able to outperform other sequence-based methods. When compared to structure-based energy functions, EASE-MM achieved a comparable or better performance.
Allows to predict binding affinity and protein stability change upon mutation. TopologyNet is a multi-task multichannel topological convolutional neural network (MM-TCNN) framework. The software utilizes element-specific persistent homology to efficiently characterize 3D biomolecular structures in terms of multichannel topological invariants. TopologyNet methods have been shown to outperform other existing methods in protein-ligand binding affinity predictions and mutation induced protein stability change predictions. They can be easily extended to other applications in the structural prediction of biomolecular properties.
Predicts the single amino acid folding free energy changes based on a knowledge-modified molecular mechanics Poisson-Boltzmann (MM/PBSA) approach. SAAFEC is comprised of two main components: a MM/PBSA component and a set of knowledge based terms delivered from a statistical study of the biophysical characteristics of proteins. The predictor utilizes a multiple linear regression model with weighted coefficients of various terms optimized against a set of experimental data. The afore mentioned approach yields a correlation coefficient of 0.65 when benchmarked against 983 cases from 42 proteins in the ProTherm database.
PROTS-RF / PROtein Thermostability Random Forest model
Predicts mutation-induced protein stability changes. PROTS-RF is based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It shows high levels of robustness in the tests using hypothetical reverse mutations.
iSEE / interface Structure, Evolution and Energy-based
Allows quantitative prediction of the effects of single point mutations at the interface of a protein-protein complex. iSEE is a machine learning based ∆∆G predictor that combines HADDOCK, structure and energy terms of wildtype and mutant complexes and position specific scoring matrix (PSSM) conservation profiles before and after mutations. The software permits computational mutation scanning of protein-protein interfaces, as well as the identification of important binding sites.
Based on the sequence information of wild-type, mutant and three neighboring residues, a weighted decision table method (WET) have been presented for predicting the stability changes of 180 double mutants obtained from thermal (DeltaDeltaG) denaturation. Using 10-fold cross-validation test, this method showed a correlation of 0.75 between experimental and predicted values of stability changes, and an accuracy of 82.2% for discriminating the stabilizing and destabilizing mutants.
iStability / in silico Analysis of Stability Change in Protein Structures
Aims to implement specific pre-defined protein engineering strategies like disulphide bond insertion. iStability is a module of the iRDP Web Server that aids in identification of stabilizing mutation sites through the application of protein design strategies described above for improvement of thermal stability but also assesses the stability of any mutant. It also offers in silico implementation of four experimentally established protein engineering strategies for identification of possible stabilizing mutation sites.
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