Table 1 Classification of methods to predict the effect of missense mutations

Protein stability

Predicts the difference in unfolding free energy between wild-type and mutant protein

Considers only one possible mechanism that may affect the phenotype

Protein–protein/protein–nucleic acid affinity

Predicts the difference in the binding affinity between binding partners upon mutation

Small training datasets limit the scope of these methods

Protein–ligand affinity

Predicts the difference in ligand-binding affinity upon mutation

Small training datasets limit the scope of these methods

Phenotypic effect

Predicts the likelihood that a mutation is deleterious without considering a specific molecular mechanism

Except for Mendelian disease phenotypes, the phenotype may only be observed in a subset of the population (partial penetrance). Databases use different annotation practices and contain contradictory information for some mutations

Mapping and 3D visualization

Provides a 3D context of the site of mutation and may give atomic-level insight into mechanism of action

Visual approach is not suitable for automated whole-exome predictions

3D mutation hotspots

Clusters mutations by spatial proximity that are not necessarily close in protein sequence

Clustering may not explain the effect of specific mutations in a hotspot