Ab-initio binding of barnase–barstar with DelPhiForce steered Molecular Dynamics (DFMD) approach

Author:

Koirala Mahesh1,Alexov Emil1ORCID

Affiliation:

1. Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA

Abstract

Receptor–ligand interactions are involved in various biological processes, therefore understanding the binding mechanism and ability to predict the binding mode are essential for many biological investigations. While many computational methods exist to predict the 3D structure of the corresponding complex provided the knowledge of the monomers, here we use the newly developed DelPhiForce steered Molecular Dynamics (DFMD) approach to model the binding of barstar to barnase to demonstrate that first-principles methods are also capable of modeling the binding. Essential component of DFMD approach is enhancing the role of long-range electrostatic interactions to provide guiding force of the monomers toward their correct binding orientation and position. Thus, it is demonstrated that the DFMD can successfully dock barstar to barnase even if the initial positions and orientations of both are completely different from the correct ones. Thus, the electrostatics provides orientational guidance along with pulling force to deliver the ligand in close proximity to the receptor.

Funder

National Institute of General Medical Sciences

Publisher

World Scientific Pub Co Pte Lt

Subject

Computational Theory and Mathematics,Physical and Theoretical Chemistry,Computer Science Applications

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Electrostatics in Computational Biophysics and Its Implications for Disease Effects;International Journal of Molecular Sciences;2022-09-07

2. Editorial: Special Issue on Computational Chemistry and Diseases;Journal of Theoretical and Computational Chemistry;2020-08-25

3. Editorial: Special Issue on Novel Methods in Computational Chemistry and their Applications to Biological Problems: Part 2;Journal of Theoretical and Computational Chemistry;2020-06

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