Professor Mahmoud Moradi
Chemistry and Biochemistry
U. of Arkansas
Fayetteville, AR

Monday, September 9, 2019
3:00 pm (CT)
3269 Beckman Institute

Abstract

With recent advances in structural biology, supercomputer technology, and computational modeling techniques, molecular dynamics has emerged as a standard technique to provide a detailed picture of protein dynamics at the atomic level. Unfortunately, many biomolecular processes such as large-scale protein conformational changes are associated with timescales inaccessible to brute-force molecular dynamics. Various enhanced sampling techniques have been developed over the past few decades to address this “timescale gap”; however, the application of these methods to biologically relevant systems remains challenging due to both their computational costs and their methodological flaws. We have recently developed a mathematical framework for improving the theoretical foundations of enhanced sampling techniques, path-finding algorithms, and free energy calculation methods for the computational study of conformational transitions of proteins. The Riemannian framework allows for developing theories and algorithms that generate protein conformational pathways and free energy profiles that are invariant under coordinate transformation. The Riemannian formalism thus provides a robust framework for the study of functionally important conformational changes of proteins at the molecular level that is less dependent on the choice of parameters and variables used in calculations. The developed algorithms have been particularly used to study several classes of bacterial membrane proteins including mechanosensitive channel of large conductance, membrane insertase YidC, and proton-coupled oligopeptide transporter GkPOT.