Missense Prediction Methods and Approaches

Mutations fall into different types:

  • 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 [9]). do prediction methods:

  • Many methods are purely sequence based (e.g. SIFT).
  • Some methods incorporate protein structure information through rule-based approaches [70] 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 [65].
  • Various servers exploit a combination of sequence, structural and evolutionary features (e.g. SNAP, PMUT and CanPredict).

Sequence and evolutionary conservation-based methods

e.g. SIFT, Align-GVGD, MutationAssessor, PANTHER, MAPP

Empirical rules

e.g. PolyPhen

Protein Structure

e.g. SNP@Domain, BONGO, SNPs3D

Protein sequence and structure-based methods

e.g. PolyPhen, PolyPhen-2, LS-SNP/PDB, SNPeffect, BONGO

Direct methods

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)

Machine-learning methods

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

Missense Prediction Tool Catalogue

A Summary of Prediction Methods and Databases

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

Program Ref St M Pre Notes
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?)
MutDB 4 Y H Y
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
TopoSNP 6 Y P Y
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.
SNPeffect 7 Y N N
assesses stability (FoldX), aggregation, amyloidosis, proximity to functional sites and cellular processing
nsSNPAnalyzer 8 Y N
Exploits SIFT and structural features to train a random forest
FIS 9 NS 'Functional Impact Score' – exploits evolutionary information from multiple sequence alignments.
MutationTaster 10 N
Uses conservation, effects on splicing, protein features and mRNA production/stability
SNAP 11, 12 (Y) N N
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.
Condel 13
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.
SAAPdap 15 Y N
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.
SAAPpred 15 Y N
A random-forest predictor based on the structural analyses from SAAPdap
MutPred 16
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.
SNPS&GO 18, 36
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
SIFT 19, 64
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.
PhD-SNP 26
Uses a support vector machine with local sequence environment and a profile derived from a multiple sequence alignment
PMut 27, 45 (Y)
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.
SDM 29, 71 Y
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.
MutationAssessor 30
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
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)
VEP 49
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.
Parepro 48
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)
transFIC N/A
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
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.
SNPdryad 75
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.
Provean 79,80,81
A fast approach to predict the effects of both amino acid substitutions and indels.

Reviews etc

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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* Reference does not appear in the table above