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

ABSTRACT

Introduction: 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.

KEYWORDS:

• Mutational analysis
• genetic diseases
• drug resistance
• cancer
• drug design
• molecular mechanism
• genotype-phenotype association

Article highlights

• Scalable and reliable structural based computational approaches are providing detailed insight into the molecular consequences of coding mutations.

• These have been used to guide patient treatment strategies for renal cell carcinoma and genetic diseases.

• Using these methods, drug resistance mutations can be identified and predicted.

• Used in a preemptive fashion, these can help guide drug development in the search for new therapeutics less likely to develop resistance.

• Mutations can give rise to a phenotype through different molecular mechanisms which can be assessed via integration of computational methods.

This box summarizes key points contained in the article.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

This work was funded by the Jack Brockhoff Foundation (JBF 4186, 2016) and a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1). This research was supported by the Victorian Life Sciences Computation Initiative (VLSCI), an initiative of the Victorian Government, Australia, on its Facility hosted at the University of Melbourne (UOM0017). DEV Pires receives support from the René Rachou Research Center (CPqRR/FIOCRUZ Minas), Brazil. DB Ascher is supported by a C. J. Martin Research Fellowship from the National Health and Medical Research Council of Australia (APP1072476), and the Department of Biochemistry, University of Melbourne.