Bi-Directional SIFT Predicts a Subset of Activating Mutations

Abstract

Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but none that specifically attempt to identify mutations that result in hyperactivation or gain-of-function. Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness. We show that this bi-directional SIFT (B-SIFT) is capable of identifying experimentally verified activating mutants from multiple datasets. B-SIFT analysis of large-scale cancer genotyping data identified potential activating mutations, some of which we have provided detailed structural evidence to support. B-SIFT could prove to be a valuable tool for efforts in protein engineering as well as in identification of functional mutations in cancer.

Germline Fitness-Based Scoring of Cancer Mutations

“…This clear contrast to mutations in candidate tumor suppressor genes may point to activating mutations being more heterogenous in conferring their cancerous effect, or to the germline fitness playing no (or a smaller) role for them, or to our simple classification criterion being a definition not accurate enough for onco genes. However, we note that L ee et al (2009b) put forward a proposition that a subset of activating mutations may have positive scores, i.e. , that they are germline beneficial, and they provide structural evidence for predictions that such a criterion produces.…”

Computational approaches to identify functional genetic variants in cancer genomes

“…Because mutations do not occur in isolation, but coexist with other somatic alterations that work together to alter cellular processes, separate gene-by-gene analyses are error-prone. A promising direction is the integration of multiple sources of biological information 60 , and the use of pathway and network analyses in the interpretation of cancer genomes 22,61,62 .…”

“…The second challenge is to develop reliable computational methods for the classification of mutations by functional impact type: loss of function, gain of function or switch of function 22,61,62 . The computational classification of mutations by type as well as strength of impact will contribute to the more complete elucidation of functional alterations in a cancer genome.…”

Identification of Genomic Features in the Classification of Loss- and Gain-of-Function Mutation

“…Because proteins are generated and regulated based on the genome sequence, alterations of the genome can lead to changes of protein functions [1]. Through these genetic mutations, a protein can loss its native function (loss-of-function), or it can confer a new function (gainof-function) [2][3][4][5]. For example, a mutated fumarate hydratase (FH) loses its native catalytic activity [6], and heterozygous point mutations in isocitrate dehydrogenase (IDH1, IDH2) confer a new metabolic enzymatic activity that produces 2-hydroxyglutarate [7,8].…”

“…Lee et al discussed the bi-directional SIFT (B-SIFT), which is a modified form of SIFT. In addition, the B-SIFT algorithm calculates scores of mutation alleles based on evolutionary conservation information [3]. They used the scores to identify mutations which cause hyperactivation or gain-of-function outcomes, but our work uses not only the functional effects of mutations but also several other properties.…”