Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model

Ji, Xiaofan; Cui, Xiaochen; Li, Zhengxin; Choi, Taeyoung; Wang, Ying; Xiao, Wen; Zhao, Yunshuo; Zha, Jinyin; Zhang, Jian; Chen, Hai-Feng; Yu, Zhengtian

doi:10.1021/acs.jcim.2c01369

Cited by 6 publications

(6 citation statements)

References 83 publications

(120 reference statements)

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“…Thus, a potential strategy could involve employing deep-learning methods to generate sequence-specific water model parameters for different IDPs. In fact, some works have already been done regarding deep learning-based energy functions or force fields. , We believe that the accuracy could be substantially improved through similar methodologies.…”

Section: Discussionmentioning

confidence: 98%

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Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Pan

Chen

2023

J. Chem. Theory Comput.

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Intrinsically disordered proteins (IDPs) play a critical role in many biological processes. Due to the inherent structural flexibility of IDPs, experimental methods present significant challenges for sampling their conformational information at the atomic level. Therefore, molecular dynamics (MD) simulations have emerged as the primary tools for modeling IDPs whose accuracy depend on force field and water model. To enhance the accuracy of physical modeling of IDPs, several force fields have been developed. However, current water models lack precision and underestimate the interaction between water molecules and proteins. Here, we used Monte-Carlo re-weighting method to re-parameterize a three-point water model based on OPC3 for IDPs (named OPC3-B). We benchmarked the performance of OPC3-B compared with nine different water models for 10 IDPs and three ordered proteins. The results indicate that the performance of OPC3-B is better than other water models for both IDPs and ordered proteins. At the same time, OPC3-B possess the power of transferability with other force field to simulate IDPs. This newly developed water model can be used to insight into the research of sequence-disordered-function paradigm for IDPs.

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Section: Discussionmentioning

confidence: 98%

“…In fact, some works have already been done regarding deep learning-based energy functions 76 or force fields. 23,77 We believe that the accuracy could be substantially improved through similar methodologies.…”

Section: ■ Conclusionmentioning

confidence: 95%

Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Pan

Chen

2023

J. Chem. Theory Comput.

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“…The grid-based energy correction map (CMAP) term was widely used in force fields of proteins and RNA. ,,, To improve the concordance between simulation and experimental results, we previously introduced the zeta-alpha CMAP in the BSFF1 for surpassing the intercalated conformations. Here, we added the base-specific gamma-delta CMAP parameter, which was respectively obtained with reweighting in AAAA, CCCC, GGGG, and UUUU, to improve the dominating A-form experimental conformations in simulations.…”

Section: Methodsmentioning

confidence: 99%

Excited–Ground-State Transition of the RNA Strand Slippage Mechanism Captured by the Base-Specific Force Field

Li,

Song,

Zhu

et al. 2024

J. Chem. Theory Comput.

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Excited-ground-state transition and strand slippage of RNA play key roles in transcription and translation of central dogma. Due to limitation of current experimental techniques, the dynamic structure ensembles of RNA remain inadequately understood. Molecular dynamics simulations offer a promising complementary approach, whose accuracy depends on the force field. Here, we develop the new version of RNA base-specific force field (BSFF2) to address underestimation of base pairing stability and artificial backbone conformations. Extensive evaluations on typical RNA systems have comprehensively confirmed the accuracy of BSFF2. Furthermore, BSFF2 demonstrates exceptional efficiency in de novo folding of tetraloops and reproducing base pair reshuffling transition between RNA excited and ground states. Then, we explored the RNA strand slippage mechanism with BSFF2. We conducted a comprehensive three-dimensional structural investigation into the strand slippage of the most complex r(G4C2) 9 repeat element and presented the molecular details in the dynamic transition along with the underlying mechanism. Our results of capturing the strand slippage, excited-ground transition, de novo folding, and simulations for various typical RNA motifs indicate that BSFF2 should be one of valuable tools for dynamic conformation research and structure prediction of RNA, and a future contribution to RNA-targeted drug design as well as RNA therapy development.

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“…By training models on large datasets of experimental and simulated data, it is also possible to identify patterns and relationships that can inform the development of more accurate and predictive force fields. 270 Furthermore, ML and AI can also be used to accelerate simulations and optimize parameters, allowing for more efficient and accurate simulations of biological systems. [271][272][273][274][275] As computational power and data availability continue to increase, we can expect to see even more exciting developments in this area.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Application of computational approaches in biomembranes: From structure to function

Guo

Bao

et al. 2023

WIREs Comput Mol Sci

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Biological membranes (biomembranes) are one of the most complicated structures that allow life to exist. Investigating their structure, dynamics, and function is crucial for advancing our knowledge of cellular mechanisms and developing novel therapeutic strategies. However, experimental investigation of many biomembrane phenomena is challenging due to their compositional and structural complexity, as well as the inherently multi‐scalar features. Computational approaches, particularly molecular dynamics (MD) simulations, have emerged as powerful tools for addressing the atomic details of biomembrane systems, driving breakthroughs in our understanding of biomembranes and their roles in cellular function. This review presents an overview of the latest advancements in related computational approaches, from force fields and model construction to MD simulations and trajectory analysis. We also discussed current hot research topics and challenges. Finally, we outline future directions, emphasizing the integration of force field development, enhanced sampling techniques, and data‐driven approaches to accelerate the growth of this field in the years to come. We aim to equip readers with an understanding of the promise and limitations of emerging computational technologies in biomembrane systems and offer valuable recommendations for future research endeavors.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

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Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model

Cited by 6 publications

References 83 publications

Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Balanced Three-Point Water Model OPC3-B for Intrinsically Disordered and Ordered Proteins

Excited–Ground-State Transition of the RNA Strand Slippage Mechanism Captured by the Base-Specific Force Field

Application of computational approaches in biomembranes: From structure to function

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