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Over the past twenty years, multiple in silico approaches to predict how mutations affect protein stability have been developed based on various evolutionary and physicochemical hypotheses.These include methods that seek to understand the effects of amino acid substitutions from the protein sequence alone, and those that exploit the extensive structural information now available for many proteins.The effects of mutations can be complex and multifactorial.
The advent of high-throughput techniques including sequencing and saturation mutagenesis has provided large amounts of phenotypic data linked to mutations.The figure depicts the proposed methodology workflow which can be divided in the four main steps: data collection and structural analysis, in silico (quantitative) prediction of effects of mutations, filtering mutations by their predicted effect, building regression and classification models to link prediction with observed phenotype.Findlay and colleagues reported in Nature a CRISPR/Cas9 cleavage system coupled with multiplex homology-directed repair to perform saturation editing of a conserved 25 amino acid region of the RNA lariat debranching enzyme DBR1, an essential gene.These properties are much more difficult to predict from the amino acid sequence alone.
Several methods have been recently proposed in an attempt to understand how mutations on protein interfaces affect binding affinity.
The sequence-based approaches include, amongst others, the well established and widely used methods SIFT, which uses environment-specific substitution tables of protein families to derive a statistical potential energy function.