About the Speaker:-About the Speaker:- Prof. Major completed his undergraduate studies in chemistry and computer sciences at Bar-Ilan University (BIU) in 1997. He received his Ph.D. from BIU in 2003 under Prof. Bilha Fischer, where he worked on molecular properties of nucleotide derivatives, theoretical modeling of G-protein coupled receptors, as well as molecular recognition.
He did a post-doctorate at the University of Minnesota under Prof. Jiali Gao during the years 2003-2006, where he was involved in development and application of theoretical methods for enzyme catalysis.
Since 2007, he has been a Faculty member in the Chemistry Department at BIU. His main research interests are in the fields of computational chemistry, biochemistry, and renewable energy.

Abstract:- Predicting the correct, mechanistically appropriate binding modes in enzymes for substrate, product, as well as all reaction intermediates, and transition states along a reaction pathway is immensely challenging and remains an unsolved problem. We recently developed EnzyDock, a CHARMM-based multiscale, multistate consensus docking program that can predict the chemically relevant pose of substrate, reaction intermediates, transition states, products, and inhibitors [1]. Herein, we will present EnzyDock and show its application to several different kinds of problems. First, we study the ability of several docking programs, including EnzyDock, to reproduce observed binding modes in the Mpro SARS-CoV-2 enzyme [2]. Second, we will see how to use EnzyDock to study highly complex, multistep reactions, like the terpene synthase reaction in the diterpene synthase CotB2 [3]. Finally, we will show how one can gain insight into evolutionary-based differences in substrate binding modes in terpene synthases using EnzyDock [4].

1. Das, S.; Shimshi, M.; Raz, K.; Nitoker, N.; Mhashal, A.; Ansbacher, T.; Major, D. T. J. Chem. Theory Comput. 2019, 15, 5116-5134.
2. Raz, K.; Driller, R.; Dimos, N.; Ringel, M.; Bruck, T.; Loll, B.; Major, D. T. J. Am. Chem. Soc. 2020, 142, 21562-21574.
3. Zev, S.; Raz, K.; Schwartz, R.; Tarabeh, R.; Gupta, P. K.; Major, D. T. Benchmarking the ability of common docking programs to correctly reproduce and score binding modes in SARS-CoV-2 protease Mpro. J. Chem. Inf. Model. 2021, 61, 2957-2966.
4. Schwartz, R.; Zev, S.; Major, D. T. Differential Substrate Sensing in Terpene Synthases from Plants and Microorganisms. Insights from Structural, Bioinformatic, and EnzyDock Analyses. Angew. Chem. Int. Ed. 2024, e202400743.