How to efficiently characterize the interaction pathways of protein–ligand recognition? A comparative analysis on enhanced sampling approaches

It is evidenced that many elaborately designed molecules that can interact well with the binding pocket of their target fail to exhibit activity in wet-lab experiments. This may associate with the interacting process of drug-target recognition. To efficiently characterize the drug-target interacting process, various enhanced sampling technologies have been proposed; yet, very few studies have systemically investigated whether the settings of these simulations are favorable to characterize the purposed tasks. Here, by comparing two popular enhanced sampling technologies, namely, the well-temped metadynamics and random acceleration molecular dynamics (RAMD), we systemically investigate the strategies to efficiently characterize the dissociating process of protein-ligand interactions. Two target families are employed for the analysis, including the kinase family (represented by TRK1) that represents the interaction-pathway obvious systems and the nuclear receptor family (represented by THRβ) that represents the interaction-pathway unobvious systems. Our results suggest that (1) in terms of maintaining stability of the protein structure, MetaD at various simulation conditions and RAMD with a large random force are good choice; (2) drug residence time derived from both MetaD and RAMD based on various parameters shows reasonable correlation to the experimental binding strength of the ligands, but RAMD usually runs with much less simulation time; and (3) both enhanced sampling methods result in reasonably consistent pathway preference for the two target families. Taken together, it will be much time-saving to utilize RAMD with high random force for interaction pathway exploration for both the pathway obvious and unobvious systems if the protein keeps stable in the simulation; otherwise, MetaD with a high bias factor is proposed to balance the computational accuracy and efficiency for the exploration.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2026-01-09
DOI
https://doi.org/10.1063/5.0311694
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
45.17
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article

How to efficiently characterize the interaction pathways of protein–ligand recognition? A comparative analysis on enhanced sampling approaches

Chen Yin, Kexin Xu, Zihao Wang, Kaimo Yang et al.
1 citations
The Journal of Chemical Physics
Computational Drug Discovery Methods
45.17
article

How to efficiently characterize the interaction pathways of protein–ligand recognition? A comparative analysis on enhanced sampling approaches

Chen Yin, Kexin Xu, Zihao Wang, Kaimo Yang, Tingjun Hou, Huiyong Sun, Mingyun Shen, Zhe Wang, Zhiliang Jiang, Sutong Xiang, Qirui Deng
article en
1 citations

Abstract

It is evidenced that many elaborately designed molecules that can interact well with the binding pocket of their target fail to exhibit activity in wet-lab experiments. This may associate with the interacting process of drug-target recognition. To efficiently characterize the drug-target interacting process, various enhanced sampling technologies have been proposed; yet, very few studies have systemically investigated whether the settings of these simulations are favorable to characterize the purposed tasks. Here, by comparing two popular enhanced sampling technologies, namely, the well-temped metadynamics and random acceleration molecular dynamics (RAMD), we systemically investigate the strategies to efficiently characterize the dissociating process of protein-ligand interactions. Two target families are employed for the analysis, including the kinase family (represented by TRK1) that represents the interaction-pathway obvious systems and the nuclear receptor family (represented by THRβ) that represents the interaction-pathway unobvious systems. Our results suggest that (1) in terms of maintaining stability of the protein structure, MetaD at various simulation conditions and RAMD with a large random force are good choice; (2) drug residence time derived from both MetaD and RAMD based on various parameters shows reasonable correlation to the experimental binding strength of the ligands, but RAMD usually runs with much less simulation time; and (3) both enhanced sampling methods result in reasonably consistent pathway preference for the two target families. Taken together, it will be much time-saving to utilize RAMD with high random force for interaction pathway exploration for both the pathway obvious and unobvious systems if the protein keeps stable in the simulation; otherwise, MetaD with a high bias factor is proposed to balance the computational accuracy and efficiency for the exploration.

The Journal of Chemical PhysicsVol. 164(2)
China Pharmaceutical University (CN), Hangzhou Normal University (CN), Zhejiang Pharmaceutical College (CN), Zhejiang University of Technology (CN), Zhejiang University (CN)
Openalex Percentile: Top 1%
Computational Drug Discovery Methods
45.17
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