Literature DB >> 33650860

Coupling Monte Carlo, Variational Implicit Solvation, and Binary Level-Set for Simulations of Biomolecular Binding.

Zirui Zhang1, Clarisse G Ricci2, Chao Fan1, Li-Tien Cheng1, Bo Li3, J Andrew McCammon2.   

Abstract

We develop a hybrid approach that combines the Monte Carlo (MC) method, a variational implicit-solvent model (VISM), and a binary level-set method for the simulation of biomolecular binding in an aqueous solvent. The solvation free energy for the biomolecular complex is estimated by minimizing the VISM free-energy functional of all possible solute-solvent interfaces that are used as dielectric boundaries. This functional consists of the solute volumetric, solute-solvent interfacial, solute-solvent van der Waals interaction, and electrostatic free energy. A technique of shifting the dielectric boundary is used to accurately predict the electrostatic part of the solvation free energy. Minimizing such a functional in each MC move is made possible by our new and fast binary level-set method. This method is based on the approximation of surface area by the convolution of an indicator function with a compactly supported kernel and is implemented by simple flips of numerical grid cells locally around the solute-solvent interface. We apply our approach to the p53-MDM2 system for which the two molecules are approximated by rigid bodies. Our efficient approach captures some of the poses before the final bound state. All-atom molecular dynamics simulations with most of such poses quickly reach the final bound state. Our work is a new step toward realistic simulations of biomolecular interactions. With further improvement of coarse graining and MC sampling, and combined with other models, our hybrid approach can be used to study the free-energy landscape and kinetic pathways of ligand binding to proteins.

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Year:  2021        PMID: 33650860      PMCID: PMC8817676          DOI: 10.1021/acs.jctc.0c01109

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  59 in total

1.  Coupling hydrophobicity, dispersion, and electrostatics in continuum solvent models.

Authors:  J Dzubiella; J M J Swanson; J A McCammon
Journal:  Phys Rev Lett       Date:  2006-03-03       Impact factor: 9.161

2.  Dynamic energy landscape view of coupled binding and protein conformational change: induced-fit versus population-shift mechanisms.

Authors:  Kei-Ichi Okazaki; Shoji Takada
Journal:  Proc Natl Acad Sci U S A       Date:  2008-08-04       Impact factor: 11.205

3.  Multiple peptide conformations give rise to similar binding affinities: molecular simulations of p53-MDM2.

Authors:  Shubhra Ghosh Dastidar; David P Lane; Chandra S Verma
Journal:  J Am Chem Soc       Date:  2008-09-19       Impact factor: 15.419

4.  Conformational selection and induced fit mechanism underlie specificity in noncovalent interactions with ubiquitin.

Authors:  Tomasz Wlodarski; Bojan Zagrovic
Journal:  Proc Natl Acad Sci U S A       Date:  2009-11-03       Impact factor: 11.205

5.  Advances in Docking.

Authors:  Vladimir B Sulimov; Danil C Kutov; Alexey V Sulimov
Journal:  Curr Med Chem       Date:  2019       Impact factor: 4.530

Review 6.  Molecular recognition and ligand association.

Authors:  Riccardo Baron; J Andrew McCammon
Journal:  Annu Rev Phys Chem       Date:  2013-03-05       Impact factor: 12.703

7.  Charge hydration asymmetry: the basic principle and how to use it to test and improve water models.

Authors:  Abhishek Mukhopadhyay; Andrew T Fenley; Igor S Tolokh; Alexey V Onufriev
Journal:  J Phys Chem B       Date:  2012-08-07       Impact factor: 2.991

8.  Water in cavity-ligand recognition.

Authors:  Riccardo Baron; Piotr Setny; J Andrew McCammon
Journal:  J Am Chem Soc       Date:  2010-09-01       Impact factor: 15.419

Review 9.  Inhibiting the p53-MDM2 interaction: an important target for cancer therapy.

Authors:  Patrick Chène
Journal:  Nat Rev Cancer       Date:  2003-02       Impact factor: 60.716

Review 10.  Targeting p53 for the treatment of cancer.

Authors:  Michael J Duffy; Naoise C Synnott; Shane O'Grady; John Crown
Journal:  Semin Cancer Biol       Date:  2020-07-31       Impact factor: 15.707

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