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

From: Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework

Method type Prediction Limitations
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
  1. 3D three-dimensional