Most mutations are 'Loss of Function' – some are 'Gain of Function' (generally through loss of regulation). A small number are actually 'change of function' – e.g. of specificity (estimated at 5% of cancer mutations ).
Many methods are purely sequence based (e.g. SIFT). Protein structure information has been incorporated through rule-based approaches  or machine learning. More sophisticated alignment information has been used exploiting hidden Markov models (e.g. subPSEC and PANTHER).When a structure is not available, comparative modelling may be exploited (e.g. LS-SNP) and application of ab initio structural models has also been explored . Various servers exploit a combination of sequence, structural and evolutionary features (e.g. SNAP, PMUT and CanPredict).
e.g. SIFT, Align-GVGD, MutationAssessor, PANTHER, MAPP
e.g. SNP@Domain, BONGO, SNPs3D
e.g. PolyPhen, PolyPhen-2, LS-SNP/PDB, SNPeffect, BONGO
These employ some sort of score based on some type of theoretical model of what happens when a mutation occurs (e.g. SIFT, PANTHER, etc)
These use machine-learning (such as neural nets, SVMs, random forests, etc.) and can combine different properties of the native and mutant residue such as size and polarity, together with other information such as structural environment (e.g. accessibility, H-bonding), evolutionary conservation
e.g. PMut, SNAP, PhD-SNP, SNPs&GO, Parepro, CanPredict, nsSNPAnalyzer, MutPred, Hansa, MutationTaster
Ref – reference (see below)
St – use of structural data: Y = required; (Y) = used if available (predicts structural information otherwise)
M – generates models: Y = yes; P = precomputed only; H = highlights where the mutation is but doesn't model it
Pre – Are data pre-calculated: Y = yes (novel mutations cannot be uploaded); NS = No web server
|SNPs3D||1, 70, 72, 73||Y||P||Y||
Uses structures, sequence profiles, pathways together with conservation scores from MutDB to train SVMs to make destabilization predictions
Pre-calculated analysis uses pathways
|ModSNP||3||Y||Y||Simply provides models and SIFT results (No longer available?)|
Precalculated set of mutant models
|LS-SNP||5, 57, 58||Y||P||Y||
Uses an SVM trained with rule-based annotation of structure, sequence and evolution to look for destabilization, proximity to ligands and interfaces and exploits information from OMIM on similar known PDs
Classifies residues based on location (surface pocket or interior void; convex or depressed surface; internal) and combines this with a conservation score from derived from Pfam.
assesses stability (FoldX), aggregation, amyloidosis, proximity to functional sites and cellular processing
Exploits SIFT and structural features to train a random forest
|FIS||9||NS||'Functional Impact Score' – exploits evolutionary information from multiple sequence alignments.|
Uses conservation, effects on splicing, protein features and mRNA production/stability
Uses neural networks with data from the sequence and PolyPhen and SIFT predictions. In addition it uses predicted structural features (solvent accessibility, secondary structure and flexibility), but can exploit actual structural data if available.
Uses a weighted average score from a number of predictors. The original paper uses LogRE, MAPP, MutationAssessor, PolyPhen-2 and SIFT, but the latest version just MutationAssessor and FATHMM.
Exploits HMMs to represent a protein family and exploits species-specific weights.
A pre-calculated database of the structural effects of mutations. Used a number of rule-based analyses of strctural effects together with a conservation score.
A pipeline for calculating the structural effects of mutations (replaces SAAPdb). Uses a number of rule-based analyses of strctural effects together with a conservation score.
A random-forest predictor based on the structural analyses from SAAPdap
Uses a Random Forest predictor with data based on predicted protein structure and dynamics, predicted functional properties and sequence and evolutionary information.
A meta-predictor that uses support vector machines with results from SIFT, PolyPhen, conservation, predicted effects on regulation, the 'Grantham' score for amino acid differences. Designed to be expandable.
Uses results from PANTHER together with functional information from GO and sequence information – both from the local environment and from profiles from multiple sequence alignments.
As SNPS&GO, but also uses structural data
An evolutionary method which calculates a sophisticated residue conservation score from multiple alignment
|PolyPhen/PolyPhen-2||20, 21, 22, 67||(Y)||N||
Uses machine learning on a set of eight sequence- and three structure-based features. If no structure is available, the structural features are predicted.
|Panther/subPSEC||23, 24, 25||
PSEC is a position-specific evolutionary conservation score and subPSEC is a difference in PSEC scores for a substitution. Panther exploits these scores derived from HMMs (PANTHER/lib) together with an ontology of protein function (PANTHER/X – a simplified form of GO) to make predictions.
Uses a support vector machine with local sequence environment and a profile derived from a multiple sequence alignment
Uses PHD secondary structure and accessibility prediction (or observed if a structure is available), together with statistical potentials from Prosa-II to evaluate stability, mutation matrix scores, changes in amino acid properties, a sequence potential, PSSM, a conservation score and SwissProt annotations to train a neural network.
Assess stability using environment-specific substitution tables and local structural environment (secondary structure, solvent accessibility, Hbonds), functional information from the catalytic site atlas and UniProt.
Uses 'combinatorial entropy optimization' (CEO) to look at sets of evolutionarily related proteins and find key functional residues to which it applies a conservation score.
|LogRE / CanPredict||31, 39, 56||
[SEEMS NOT TO BE AVAILABLE]
LogRE is a score calculated from a Hidden Markov Model for a substitution that is exploited by CanPredict
|MAPP / ProPhylER||32, 33||
www.prophyler.org [SEEMS NOT TO BE AVAILABLE]
Prophyler uses the MAPP score which takes data from a multiple alignment and converts a position in the alignment to a vector describing the importance of 6 physicochemical properties (hydropathy, polarity, charge, volume and free-energy in alpha helices and beta-strands)
|ProSPect||34, 35, 77||
Concentrates on stability and interfaces and protein network information
FOLD-X is an online force-field for calculating energy – it has been widely used for calculating stability changes on mutation.
|PoPMuSiC||40, 41, 42||(Y)||
Links ENSEMBL to variant effect predictors (currently SIFT and PolyPhen-2)
A protein structure is converted to a graph, based on its amino acid interactions. Those residues of key importance for structural stability are determined by these interactions. The substituted amino acids are modelled and the impact of the change determined based on the changes in the network.
Combines 10 different properties of these substitutions to partition disease and neutral mutations: 6 features related to the specific position of the mutation and probabilities of the amino acids; 2 features of protein structural environment; 2 features based on likelihood of the amino acid substitutions.
Three attributes are characterised from homologues collected using PSI-BLAST: (i) property differences between the ‘new’ amino acid and those in the alignment; (ii) the distribution of amino acids at the position; (iii) the sequence environment (upstream and downstream amino acids)
Exploits Functional Impact Scores with SIFT, PolyPhen-2 and MutationAssessor to score cancer mutations
|[Westhead]||59||NS||Evaluates two machine learning methods in prediction from sequence|
[NO LONGER AVAILABLE]
Evaluate two machine learning methods and uses structural information from homologues and sequence profiles from multiple alignment
|[Kohane]||51||NS||Uses Bayesian methods using frequency data and hydrophobicity on some specific datasets|
|CHASM||52||NS||Cancer-specific High-throughput Annotation of Somatic Mutations. Uses a random forest to identify driver mutations in cancer.|
|B-SIFT||62||NS||a modified version of SIFT which is able to identify both deleterious and a subset of activating mutations given a protein sequence and a query mutation within that sequence|
|[Baker]||65||Y||NS||Uses classification tree and logistic regression machine learning method with solvent-accessibility, Cβ density and SIFT scores.|
Uses only protein orthologs in building a multiple sequence alignment to derive a novel conservation scoring scheme with a Random Forest classifier.
Predicts stability changes caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed using I-TASSER and physics- and knowledge-based energy functions derived from the I-TASSER models are used for machine learning.
Ref 2 has a useful comparison of some of the resources in Table 1
Refs 43, 44, 63, 66, 68 are extensive reviews
Refs 55 and 61 are review of methods used for cancer mutations
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