Computational Advances in Structural Biology

Phosphate catalytic enzymes are essential and ubiquitous to all forms of life. While structures of these proteins are typically readily available, prediction and design of their function and activity is a key current challenge. Here we present computing intensive free energy calculation data and machine learning applications to predict catalytic activity for prototype examples including Ras [1]. Our work highlights the important role of coupled proton transfer steps in the catalytic mechanism using the finite-temperature string method. This allows us to use multiple collective variables that govern the reaction path. Identification of these collective variables in complex processes presents a major problem. We offer promising AI-driven algorithms to help identify essential reaction coordinates in biomolecular processes [2,3].

References:

[1] Berta, D.; Gehrke, S.; Nyíri, K.; Vértessy, B. G.; Rosta, E. Redesigned GAP to Activate Oncogenic GAP. In preparation (ChemRxiv), 2023, 10.26434/chemrxiv-2022-ttt09-v2.

[2] Badaoui, M.; Buigues, P. J.; Berta, D.; Mandana, G. M.; Gu, H.; Földes, T.; Dickson, C. J.; Hornak, V.; Kato, M.; Molteni, C.; Parsons, S.; Rosta, E. Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics. J. Chem. Theory Comput. 2022, 10.1021/acs.jctc.1c00924.

[3] Buigues, PJ; Gehrke, S; Badaoui, M; Mandana, G. M.; Qi, T; Bottegoni, G; Rosta, E. Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor. bioRxiv; 2023, 10.1101/2023.01.03.522558.