Interaction-based discovery of functionally important genes in cancers

Abstract

A major challenge in cancer genomics is uncovering genes with an active role in tumorigenesis from a potentially large pool of mutated genes across patient samples. Here we focus on the interactions that proteins make with nucleic acids, small molecules, ions and peptides, and show that residues within proteins that are involved in these interactions are more frequently affected by mutations observed in large-scale cancer genomic data than are other residues. We leverage this observation to predict genes that play a functionally important role in cancers by introducing a computational pipeline (http://canbind.princeton.edu) for mapping large-scale cancer exome data across patients onto protein structures, and automatically extracting proteins with an enriched number of mutations affecting their nucleic acid, small molecule, ion or peptide binding sites. Using this computational approach, we show that many previously known genes implicated in cancers are enriched in mutations within the binding sites of their encoded proteins. By focusing on functionally relevant portions of proteins—specifically those known to be involved in molecular interactions—our approach is particularly well suited to detect infrequent mutations that may nonetheless be important in cancer, and should aid in expanding our functional understanding of the genomic landscape of cancer.

Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols

“…A missense mutation that affects protein interactions [38–40] may cause significant perturbations or complete abolishment of protein function, potentially leading to diseases. Typically, the change in binding free energy (ΔΔ G bind ) is used to quantify the magnitude of mutational effects on protein–protein interactions (Fig.…”

Functional variomics and network perturbation: connecting genotype to phenotype in cancer

“…In addition, some algorithms integrate multiple evolutionary and structural features to evaluate the disease-causing potential of mutations, such as CanDrA 76 and MutationTaster 77 . Last but not least, other methods prioritize driver mutations based on their location at the structural binding sites for small molecules (for example, CanBind 78 and SGDriver 79 ). Notably, most of these approaches predict mutational effects on the function of coding genes.…”

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

“…CanBind is a computational approach to prioritize SMGs that harbor enriched mutations by altering their nucleic acid, small molecules and ion or peptide binding sites [64]. iPAC, namely Identification of Protein Amino acid Clustering, is an algorithm that characterizes nonrandom somatic mutations in protein by using its 3D structure information [62].…”