Seminar


Department of Chemistry
Indian Institute of Technology Delhi

Computational Approaches to Protein Dynamics and Binding Kinetics for Drug Discovery



Prof. Rebecca C. Wade

Molecular and Cellular Modeling Group

Heidelberg Institute for Theoretical Studies, Heidelberg, Germany

Date: December 2nd 2019 (Monday)
Time: 4:00 PM
Venue: Committee Room (Room No. 101), Kusuma School of Biological Sciences (KSBS)

Abstract: The dynamic nature of protein structures and the diversity of protein binding pocket dynamics provide challenges and opportunities for ligand design [1]. We have developed TRAPP, a toolbox of computational approaches to identify TRAnsient Pockets in Proteins for ligand design. I will present recent developments in TRAPP to identify pocket conformations with high druggability. Protein binding site flexibility is one of the factors that can affect drug-target binding kinetics. Growing evidence that the efficacy of a drug can be correlated to target binding kinetics has led to the development of many new methods aimed at computing rate constants for receptor-ligand binding processes [2], see also: kbbox.h-its.org. Here, I will describe our studies to explore the determinants of structure-kinetic relationships and to develop computationally efficient methods to estimate drug-target binding kinetic parameters. I will introduce our τ-random acceleration molecular dynamics simulation (τRAMD) method to compute relative residence times [3] and discuss how machine learning analysis of τRAMD trajectories [4] and the application of Comparative Binding Energy (COMBINE) Analysis [5] can be used to decipher the determinants of drug-target residence times.

    References:
  1. Stank A, Kokh DB, Fuller JC, Wade RC. Protein binding pocket dynamics. Acc. Chem Res., 2016, 49:809-815.
  2. Bruce NJ, Ganotra GK, Kokh DB, Sadiq SK, Wade RC. New approaches for computing ligand-receptor binding kinetics. Curr Opin Struct Biol. 2018, 49: 1-10.
  3. Kokh DB, Amaral M,……Wade RC. Estimation of drug-target residence times by τ-random acceleration molecular dynamics simulations, J. Chem. Theory Comput. 2018, 14: 3859–3869.
  4. Kokh DB, Kaufmann T, Kister B, Wade RC. Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times, Frontiers. Mol. Biosci. 2019, 6: 36.
  5. Ganotra GK, Wade RC. Prediction of Drug–Target Binding Kinetics by Comparative Binding Energy Analysis. ACS Medicinal Chemistry Letters 2018, 9: 1134–1139


    All are cordially invited to attend.
    Convener (Seminar)