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MSV3d / Database of human MisSense Variants mapped to 3D protein structure

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Provides full annotation of missense variants of all human proteins with multi-level characterization including details of the physico-chemical changes induced by the amino acid modification, information related to the conservation of the mutated residue and its position relative to functional features in available or calculated 3D model. The major release of the MSV3d database is generated and updated regularly according to the dbSNP 137 (database of Single Nucleotide Polymorphism) and SwissVar releases.

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.

mutLBSgeneDB / mutated Ligand Binding Site gene DataBase

A database encompassing comprehensive annotations for all genes having ligand binding site mutations. mutLBSgeneDB may contribute to systematic analysis for functional annotations of genes with ligand binding site mutations for cancer and drug research in the precision medicine era. mutLBSgeneDB collected and curated over 2300 genes (mutLBSgenes) having around 12 000 somatic mutations at around 10 000 LBSs across 16 cancer types and selected 744 drug targetable genes (targetable_mutLBSgenes) by incorporating kinases, transcription factors, pharmacological genes, and cancer driver genes.

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.


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.


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.


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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.


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.


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.

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.

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.

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.


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.


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.


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.

SPROUTS / Structural Prediction for pRotein fOlding UTility System

A database that provides access to various structural data sets and integrated functionalities not yet available to the community. The originality of the SPROUTS database is the ability to gain access to a variety of structural analyses at one place and with a strong interaction between them. SPROUTS combines data pertaining to 429 structures that capture representative folds and results related to the prediction of critical residues expected to belong to the folding nucleus: the MIR (Most Interacting Residues), the description of the structures in terms of modular fragments: the TEF (Tightened End Fragments), and the calculation at each position of the free energy change gradient upon mutation by one of the 19 amino acids. All database results can be displayed and downloaded in textual files and Excel spreadsheets and visualized on the protein structure. SPROUTS is a unique resource to access as well as visualize state-of-the-art characteristics of protein folding and analyse the effect of point mutations on protein structure.

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.

ASEdb / Alanine Scanning Energetics database

Allows to search single alanine mutations in protein–protein, protein–nucleic acid, and protein–small molecule interactions for which binding affinities have been experimentally determined. ASEdb contains surface areas of the mutated side chain and links to the PDB entries. It is useful for studying the contribution of single amino acids to the energetics of protein interactions, and can be updated by researchers as new data are generated.


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.

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.


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.


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Provides unbiased training and validation datasets for the development of algorithms to predict binding affinity changes due to missense mutations. The PROXiMATE database helps about the study of disease-causing mutations in the progression, diagnosis and treatment of various diseases. It gives possible drug targets and novel therapy options. More, it supplies experimental data for the identification of mutants which show increased affinity to their interacting partners.

ePRoS / energy PRofile Suite

Allows users to detect similar energy profiles, relevant structural and functional relationships. ePRoS is a database for energy profile-based studying and comparing sequence–structure–function relationships and protein stability. It stores more than 74 000 pre-calculated energy profiles derived from experimental globular protein structures and about 1300 pre-calculated profiles of a-helical membrane protein structures. It offers users different way to visualize, download, and access energy profile data.