Despite the great advances in structural studies of biological systems, we are frequently left without a clear structure function correlation and cannot fully describe how different systems actually work. Thus, we have a major challenge for computer modeling approaches that are aimed at a realistic simulation of biological functions. The unresolved questions range from the guiding enzyme design to the understanding of the directional motion of complex molecular motors. Here we consider the progress in studies of biological functions, starting with QM/MM approaches for simulations of enzymatic reactions (1). We provide overwhelming support to the idea that enzyme catalysis is due to electrostatic preorganization and then to demonstration of the fact that dynamical effects cannot change the rate of the chemical steps in enzymes (2). We then move to enzyme design and consider the progresses and limits of physically based simulations (3). Considering the current difficulties in obtaining accurate results with such simulations (when the structure of the new mutant is not known), we explore the use of artificial intelligence. We found (4) a very significant correlation between the energy of a maximum entropy approach and the catalytic power of mutants. This approach should help greatly in enhancing the scope of computer aided enzyme design. Next, we describe the use of our electrostatic augmented coarse grained (CG) model (2) and the renormalization method to simulate the action of different challenging complex systems. It is shown that our CG model produces, for the first time, realistic landscapes for vectorial process such as the actions of F1 ATPase (5), F0 ATPase (6) and myosin (8). It is also shown that such machines are working by exploiting free energy gradients and cannot just use Brownian motions as the vectorial driving force. Significantly, at present, to the best of our knowledge, these studies are the only studies that reproduced consistently (rather than assumed) a structure based vectorial action of molecular motors. Finally, we describe some progress in modeling the SARS-CoV‐2 system (8).
1 Electrostatic Basis for Enzyme Catalysis, A. Warshel, P. K. Sharma, M. Kato, Y. Xiang, H. Liu and M. H. M. Olsson, Chem. Rev., 106, 3210 (2006).
2 Coarse-Grained (Multiscale) Simulations in Studies of Biophysical and Chemical Systems, S. C. L. Kamerlin, S. Vicatos, A. Dryga and A. Warshel, Ann. Rev. Phys. Chem. 62,41 (2011).
3 Combinatorial Approach for Exploring Conformational Space and Activation Barriers in Computer-Aided Enzyme Design, D. Mondal, V. Kolev, and A. Warshel, ACS Catalysis (2020).
4 Enhancing Computational Enzyme Design by a Maximum Entropy Strategy, WJ. Xie, M. Asadi, A. Warshel, Proc. Natl. Acad. Sci. USA (2022).
5 The catalytic dwell in ATPases is not crucial for movement against applied torque, C. Bai, M. Asadi and A. Warshel; Nature Chemistry, 1187-92 (2020).
6 The FOF1 ATP Synthase: From Atomistic Three-Dimensional Structure to The Rotary- Chemical Function; Review, S. Mukherjee and A. Warshel, Photosynth Res. 134,1-5, (2017).
7 Reexamining the Origin of The Directionality of Myosin V, R. Alhadeff and A. Warshel, Proc. Natl. Acad. Sci. USA, 114,10426-31, (2017).
8 Predicting Mutational Effects on Receptor Binding of the SARS-CoV-2 Variants, C. Bai, J. Wang, G. Chen, H. Zhang, K. An, P. Xu, Y. Du, R. D. Ye, A. Saha, A. Zhang, and A. Warshel, J. Am. Chem. Soc. 143,17646-54 (2021).
