Literature DB >> 26598172

Gating and Intermolecular Interactions in Ligand-Protein Association: Coarse-Grained Modeling of HIV-1 Protease.

Myungshim Kang1, Christopher Roberts1, Yuhui Cheng2, Chia-En A Chang1.   

Abstract

Most biological processes are initiated or mediated by the association of ligands and proteins. This work studies multistep, ligand-protein association processes by Brownian dynamics simulations with coarse-grained models for HIV-1 protease (HIVp) and its neutral ligands. We report the average association times when the ligand concentration is 100 μM. The influence of crowding on the simulated binding time was also studied. HIVp has flexible loops that serve as a gate during the ligand binding processes. It is believed that the flaps are partially closed most of the time in its free state. To accelerate our simulations, we fixed a part of the HIVp and reparameterized our coarse-grained model, using atomistic molecular dynamics simulations, to reproduce the "gating" motions of HIVp. HIVp-ligand interactions changed the gating behavior of HIVp and helped ligands diffuse on HIVp surface to accelerate binding. The structural adjustment of the ligand toward its final stable state was the limiting step in the binding processes, which is highly system dependent. The intermolecular attraction between the ligands and crowder proteins contributes the most to the crowding effects. The results highlight broader implications in recognition pathways under more complex environment that considers molecular dynamics and conformational changes. This work brings insights into ligand-protein associations and is helpful in the design of targeted ligands.

Entities:  

Year:  2011        PMID: 26598172     DOI: 10.1021/ct2004885

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


  16 in total

Review 1.  Modeling protein association mechanisms and kinetics.

Authors:  Huan-Xiang Zhou; Paul A Bates
Journal:  Curr Opin Struct Biol       Date:  2013-07-12       Impact factor: 6.809

2.  Molecular Dynamics Simulations of Supramolecular Anticancer Nanotubes.

Authors:  Myungshim Kang; Kaushik Chakraborty; Sharon M Loverde
Journal:  J Chem Inf Model       Date:  2018-06-06       Impact factor: 4.956

3.  BDflex: a method for efficient treatment of molecular flexibility in calculating protein-ligand binding rate constants from brownian dynamics simulations.

Authors:  Nicholas Greives; Huan-Xiang Zhou
Journal:  J Chem Phys       Date:  2012-10-07       Impact factor: 3.488

4.  Mechanism of the Association Pathways for a Pair of Fast and Slow Binding Ligands of HIV-1 Protease.

Authors:  Yu-Ming M Huang; Mark Anthony V Raymundo; Wei Chen; Chia-En A Chang
Journal:  Biochemistry       Date:  2017-02-21       Impact factor: 3.162

5.  Conformational frustration in calmodulin-target recognition.

Authors:  Swarnendu Tripathi; Qian Wang; Pengzhi Zhang; Laurel Hoffman; M Neal Waxham; Margaret S Cheung
Journal:  J Mol Recognit       Date:  2015-01-20       Impact factor: 2.137

6.  Escape of a Small Molecule from Inside T4 Lysozyme by Multiple Pathways.

Authors:  Ariane Nunes-Alves; Daniel M Zuckerman; Guilherme Menegon Arantes
Journal:  Biophys J       Date:  2018-03-13       Impact factor: 4.033

7.  Ligand Binding Pathways and Conformational Transitions of the HIV Protease.

Authors:  Yinglong Miao; Yu-Ming M Huang; Ross C Walker; J Andrew McCammon; Chia-En A Chang
Journal:  Biochemistry       Date:  2018-02-15       Impact factor: 3.162

8.  Effects of Macromolecular Crowding on the Conformational Ensembles of Disordered Proteins.

Authors:  Sanbo Qin; Huan-Xiang Zhou
Journal:  J Phys Chem Lett       Date:  2013-10-17       Impact factor: 6.475

9.  An FFT-based method for modeling protein folding and binding under crowding: benchmarking on ellipsoidal and all-atom crowders.

Authors:  Sanbo Qin; Huan-Xiang Zhou
Journal:  J Chem Theory Comput       Date:  2013-10-01       Impact factor: 6.006

Review 10.  Understanding ligand-receptor non-covalent binding kinetics using molecular modeling.

Authors:  Zhiye Tang; Christopher C Roberts; Chia-En A Chang
Journal:  Front Biosci (Landmark Ed)       Date:  2017-01-01
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