Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing. PLoS One 2017;12(2):e0170822
Date
02/10/2017Pubmed ID
28182693Pubmed Central ID
PMC5300139DOI
10.1371/journal.pone.0170822Scopus ID
2-s2.0-85012093055 (requires institutional sign-in at Scopus site) 21 CitationsAbstract
Variants in the TGFBR2 kinase domain cause several human diseases and can increase propensity for cancer. The widespread application of next generation sequencing within the setting of Individualized Medicine (IM) is increasing the rate at which TGFBR2 kinase domain variants are being identified. However, their clinical relevance is often uncertain. Consequently, we sought to evaluate the use of molecular modeling and molecular dynamics (MD) simulations for assessing the potential impact of variants within this domain. We documented the structural differences revealed by these models across 57 variants using independent MD simulations for each. Our simulations revealed various mechanisms by which variants may lead to functional alteration; some are revealed energetically, while others structurally or dynamically. We found that the ATP binding site and activation loop dynamics may be affected by variants at positions throughout the structure. This prediction cannot be made from the linear sequence alone. We present our structure-based analyses alongside those obtained using several commonly used genomics-based predictive algorithms. We believe the further mechanistic information revealed by molecular modeling will be useful in guiding the examination of clinically observed variants throughout the exome, as well as those likely to be discovered in the near future by clinical tests leveraging next-generation sequencing through IM efforts.
Author List
Zimmermann MT, Urrutia R, Oliver GR, Blackburn PR, Cousin MA, Bozeck NJ, Klee EWAuthors
Raul A. Urrutia MD Center Director, Professor in the Surgery department at Medical College of WisconsinMichael T. Zimmermann PhD Director, Associate Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
High-Throughput Nucleotide SequencingHumans
Molecular Dynamics Simulation
Protein Domains
Protein Structure, Secondary
Receptors, Transforming Growth Factor beta
