Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design

Abstract

Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.

Structure guided prediction of Pyrazinamide resistance mutations in pncA

“…We have developed a comprehensive in silico mutational analysis platform that uses graph-based signatures to represent the 3D structure of a protein and quantitatively predict the molecular consequences of point mutations on protein structure, function and interactions 30,[33][34][35][36]45 . This has been used to characterize and preemptively identify likely resistance mutations in drug targets 23,37,[46][47][48][49][50][51][52][53][54] . Using these tools, we assessed the molecular consequences of each mutation on the structure of PncA and drug activation.…”

DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

“…Proteins are dynamic macromolecules, whose function is intricately linked to their biological motions ( 1 , 2 ). We have shown previously that drug resistant and genetic disease mutations can both act through changes in protein conformational equilibria and dynamics ( 3–7 ). In order to fully understand the molecular consequences of a mutation it is, therefore, important to consider changes in protein dynamics.…”

Kinact: a computational approach for predicting activating missense mutations in protein kinases

“…As an alternative, the use of graph-based structural signatures have been shown to be a scalable and effective approach for modeling the residue environment, which was successfully employed to train machine learning-based methods to predict and elucidate effects of mutations on protein stability and interactions with their partner ( 18–26 ). Moreover, these have also been used to provide insights into the molecular mechanisms of mutations and how they lead to disease and disease predisposition ( 27–33 ) and drug resistance ( 34–41 ). These graph-based signatures are predominantly composed of distance patterns extracted from the wildtype residue environment, which together with a pharmacophore modelling of its components, has been shown to be an effective way to model both geometry and physicochemical composition of protein regions.…”