Molecular dynamics simulations for genetic interpretation in protein coding regions: where we are, where to go and when

Abstract

The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical–chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80–85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore’s law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.

Protein haploinsufficiency drivers identifyMYBPC3mutations that cause hypertrophic cardiomyopathy

“…For each WT and variant structure, three 1-μs long trajectories were obtained. Trajectories were analyzed using the model described in 74 to obtain estimations of ΔΔG upon mutation at 298 K.…”

Protein haploinsufficiency drivers identify MYBPC3 variants that cause hypertrophic cardiomyopathy

“…For each WT and variant structure, three 1-μs long trajectories were obtained. Trajectories were analyzed using the model described in ( 78 ) to obtain estimates of ΔΔ G upon mutation at 298 K.…”

In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

“…Interpretation of the effect of genetic variants at the protein level can be assisted by computational approaches. Over the last two decades, many in silico tools were developed for that end and were used to investigate the effects of gene variants in human disease 7 – 11 . Despite the multitude of computational tools available, only a few newer methods exploit the 3D protein structure to include dynamic aspects of proteins in the prediction of genetic variants impact 7 .…”

“…Over the last two decades, many in silico tools were developed for that end and were used to investigate the effects of gene variants in human disease 7 – 11 . Despite the multitude of computational tools available, only a few newer methods exploit the 3D protein structure to include dynamic aspects of proteins in the prediction of genetic variants impact 7 . Normal mode analysis (NMA) is one of the main approaches that enable the incorporation of dynamic parameters of protein structure into the investigation of genetic variants 7 .…”

“…Despite the multitude of computational tools available, only a few newer methods exploit the 3D protein structure to include dynamic aspects of proteins in the prediction of genetic variants impact 7 . Normal mode analysis (NMA) is one of the main approaches that enable the incorporation of dynamic parameters of protein structure into the investigation of genetic variants 7 . In this study, we utilize an in-silico approach using NMA to investigate the effect of disease-associated TUBB4A missense variants on the encoded protein and their associated phenotype.…”