4.5 Review

Molecular dynamics simulations and novel drug discovery

期刊

EXPERT OPINION ON DRUG DISCOVERY
卷 13, 期 1, 页码 23-37

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/17460441.2018.1403419

关键词

Molecular dynamics simulation; amyloidosis; drug discovery; virtual screening; drug resistance

资金

  1. National Natural Science Foundation of China [21375054, 21675070]
  2. Fundamental Research Funds for the Central Universities [lzujbky-2016-146, lzujbky-2017-k24]

向作者/读者索取更多资源

Introduction: Molecular dynamics (MD) simulations can provide not only plentiful dynamical structural information on biomacromolecules but also a wealth of energetic information about protein and ligand interactions. Such information is very important to understanding the structure-function relationship of the target and the essence of protein-ligand interactions and to guiding the drug discovery and design process. Thus, MD simulations have been applied widely and successfully in each step of modern drug discovery. Areas covered: In this review, the authors review the applications of MD simulations in novel drug discovery, including the pathogenic mechanisms of amyloidosis diseases, virtual screening and the interaction mechanisms between drugs and targets. Expert opinion: MD simulations have been used widely in investigating the pathogenic mechanisms of diseases caused by protein misfolding, in virtual screening, and in investigating drug resistance mechanisms caused by mutations of the target. These issues are very difficult to solve by experimental methods alone. Thus, in the future, MD simulations will have wider application with the further improvement of computational capacity and the development of better sampling methods and more accurate force fields together with more efficient analysis methods.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Chemistry, Medicinal

Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation

Yuwei Yang, Zhenxing Wu, Xiaojun Yao, Yu Kang, Tingjun Hou, Chang-Yu Hsieh, Huanxiang Liu

Summary: A conditional generative model combining a semisupervised variational autoencoder and a toxicity predictor was developed in this study to generate molecules with low toxicity, good drug-like properties, and structural diversity. The model efficiently generates low-toxicity molecules while maintaining structural diversity during the generation process.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2022)

Article Materials Science, Multidisciplinary

Novel RGD-containing peptides exhibited improved abilities to integrin receptor binding and cultures of human induced pluripotent stem cells

Ping Zhou, Fang Feng, Yameng Song, Jing Li, Qin Li, Zerong Xu, Jiamin Shi, Liying Qin, Fei He, Hongjiao Li, Yu Han, Rongzhi Zhang, Huanxiang Liu, Feng Lan

Summary: The design of novel peptide sequences and investigation of cell adhesion mechanisms are crucial for hiPSCs culture on peptide displaying surfaces. This study reveals the importance of near-Asp sequences in RGD-containing peptides and presents a novel peptide, Ac-KGGVFTMPRGDTYRAY, with excellent ability to sustain hiPSCs cultures. Structural modeling and molecular docking confirm the strong affinity between the peptide and ocV133 integrin protein, contributing to its superior performance. These findings shed light on the mechanisms underlying RGD-containing peptides' support of adhesion and offer a better peptide option for hiPSCs culture.

MATERIALS & DESIGN (2022)

Article Chemistry, Physical

Structural and Dynamics Studies of the Spcas9 Variant Provide Insights into the Regulatory Role of the REC1 Domain

Huayi Liu, Yi Zhou, Yingjie Song, Qianqian Zhang, Yeyi Kan, Xinyue Tang, Qingjie Xiao, Qianyin Xiang, Huanxiang Liu, Yunzi Luo, Rui Bao

Summary: CRISPR-Cas9 from Streptococcus pyogenes is a powerful biotechnological tool for DNA sequence modification. Understanding the dynamic mechanism of the REC lobe is crucial for designing and engineering better Cas9 enzymes. The structure analysis of the xCas9 P411T variant and molecular dynamics simulations revealed the central role of REC1 in the activation and target site binding process of Cas9.

ACS CATALYSIS (2022)

Article Biochemistry & Molecular Biology

Binding Thermodynamics and Dissociation Kinetics Analysis Uncover the Key Structural Motifs of Phenoxyphenol Derivatives as the Direct InhA Inhibitors and the Hotspot Residues of InhA

Qianqian Zhang, Jianting Han, Yongchang Zhu, Shuoyan Tan, Huanxiang Liu

Summary: In this study, the binding thermodynamic and kinetic information of direct inhibitors of InhA to phenoxyphenol derivatives were explored using multiple computer-aided drug design strategies. The results showed that van der Waals interactions were the main driving force for protein-ligand binding, and key amino acid residues contributed significantly to the binding energy.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2022)

Article Biochemical Research Methods

Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism

Yanan Tian, Xiaorui Wang, Xiaojun Yao, Huanxiang Liu, Ying Yang

Summary: This paper proposes a novel graph neural network, IFGN, which gradually identifies the key atoms/groups in the molecule related to predicted properties by a multistep focus mechanism. It also generates multistep interpretations to provide a deeper understanding of the model's predictive behaviors.

BRIEFINGS IN BIOINFORMATICS (2023)

Review Biochemical Research Methods

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Yuan-Qin Huang, Ping Sun, Yi Chen, Huan-Xiang Liu, Ge-Fei Hao, Bao-An Song

Summary: Drug resistance is a major issue impacting human health and agriculture. Developing approaches to address target mutation-induced drug resistance is crucial in biological research. Bioinformatics tools have been developed over the past decade to explore this type of drug resistance, offering a cost-effective and efficient means of analysis. However, these tools are underutilized and their strengths and limitations have not been thoroughly evaluated. This study systematically surveyed 59 freely available bioinformatics tools and analyzed their functionality, data volume, source, operating principle, and performance. The study also discussed the strengths, limitations, and application examples of these tools, providing a valuable toolbox for researchers in biomedical, pesticide, bioinformatics, and pharmaceutical engineering fields, as well as a platform for non-specialists to understand drug resistance prediction.

BRIEFINGS IN BIOINFORMATICS (2023)

Article Biochemical Research Methods

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?

Shukai Gu, Chao Shen, Jiahui Yu, Hong Zhao, Huanxiang Liu, Liwei Liu, Rong Sheng, Lei Xu, Zhe Wang, Tingjun Hou, Yu Kang

Summary: This study evaluated the impact of structural dynamic information on binding affinity prediction and found that the optimized molecular dynamics protocol improved the predictive performance for the TAF1-BD2 target with high structural flexibility, but not for the less flexible JAK1 and DDR1 targets.

BRIEFINGS IN BIOINFORMATICS (2023)

Article Biochemistry & Molecular Biology

Identification of CBPA as a New Inhibitor of PD-1/PD-L1 Interaction

Fengling Wang, Wenling Ye, Yongxing He, Haiyang Zhong, Yongchang Zhu, Jianting Han, Xiaoqing Gong, Yanan Tian, Yuwei Wang, Shuang Wang, Shaoping Ji, Huanxiang Liu, Xiaojun Yao

Summary: Targeting the PD-1/PD-L1 immunologic checkpoint has provided a breakthrough in cancer therapy. A small molecule inhibitor called CBPA was identified as an effective PD-L1 inhibitor with blocking activity and T-cell reinvigoration capacity. CBPA also showed significant antitumor efficacy in mouse tumor models without observable organ toxicity.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2023)

Article Biochemistry & Molecular Biology

Identification of Aggregation Mechanism of Acetylated PHF6*and PHF6 Tau Peptides Based on Molecular Dynamics Simulations and Markov State Modeling

Syed Jawad Ali Shah, Qianqian Zhang, Jingjing Guo, Hongli Liu, Huanxiang Liu, Jordi Villa-Freixa

Summary: The microtubule-associated protein tau plays a critical role in the development and protection of the nervous system. Dysfunction and accumulation of tau in the brain can lead to various neurodegenerative diseases. This study reveals the detailed mechanism of acetylation-driven tau aggregation.

ACS CHEMICAL NEUROSCIENCE (2023)

Article Biochemistry & Molecular Biology

Unveiling the Selectivity Mechanism of Type-I LRRK2 Inhibitors by Computational Methods: Insights from Binding Thermodynamics and Kinetics Simulation

Shuoyan Tan, Jun Wang, Peng Gao, Guotong Xie, Qianqian Zhang, Huanxiang Liu, Xiaojun Yao

Summary: Understanding the selectivity mechanisms of inhibitors for highly similar proteins is crucial in new drug discovery. Developing highly selective targeting of leucine-rich repeat kinase 2 (LRRK2) kinases for Parkinson's disease is challenging due to the similarity of the kinase ATP binding pocket. In this study, computational methods and comprehensive analyses were conducted to investigate the selectivity mechanisms of two representative LRRK2 inhibitors (DNL201 and GNE7915). The results suggest that structural and kinetic differences between the proteins may play a key role in determining the activity of selective small-molecule inhibitors. The proposed selectivity mechanisms could aid in the rational design of novel LRRK2 kinase inhibitors for Parkinson's disease.

ACS CHEMICAL NEUROSCIENCE (2023)

Article Biochemical Research Methods

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions

Yang Yue, Shu Li, Lingling Wang, Huanxiang Liu, Henry H. Y. Tong, Shan He

Summary: In this study, a novel framework called MpbPPI is proposed for accurate prediction of amino acid mutations on protein-protein interactions. Pre-training on a strictly screened dataset enables MpbPPI to generate high-quality representations and support flexible application on different mutant-type protein-protein complexes.

BRIEFINGS IN BIOINFORMATICS (2023)

Article Biochemistry & Molecular Biology

Biological Evaluation of 8-Methoxy-2,5-dimethyl-5H-indolo[2,3-b] Quinoline as a Potential Antitumor Agent via PI3K/AKT/mTOR Signaling

Yunhao Ma, Hongmei Zhu, Xinrong Jiang, Zhongkun Zhou, Yong Zhou, Yanan Tian, Hao Zhang, Mengze Sun, Lixue Tu, Juan Lu, Yuqing Niu, Huanxiang Liu, Yingqian Liu, Peng Chen

Summary: This study evaluated the cytotoxicity of 8-methoxy-2,5-dimethyl-5H-indolo[2,3-b]quinoline (MMNC) in colorectal cancer cells and found that MMNC exerted cytotoxicity by inhibiting the expression of PI3K/AKT/mTOR signaling pathway-related proteins, inhibiting cell proliferation, blocking the cell cycle, and inducing apoptosis.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2023)

Article Chemistry, Medicinal

Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets

Zhe Wang, Haiyang Zhong, Jintu Zhang, Peichen Pan, Dong Wang, Huanxiang Liu, Xiaojun Yao, Tingjun Hou, Yu Kang

Summary: This study systematically evaluates the performance of traditional methods and AI models in small-molecule conformer generation. The results show that traditional methods outperform AI models in reproducing bioactive conformations, while an AI model has an advantage in generating low-energy conformations.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2023)

Article Chemistry, Medicinal

FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization

Jieyu Jin, Dong Wang, Guqin Shi, Jingxiao Bao, Jike Wang, Haotian Zhang, Peichen Pan, Dan Li, Xiaojun Yao, Huanxiang Liu, Tingjun Hou, Yu Kang

Summary: Recently, deep generative models, such as FFLOM, have shown promise in fragment-based drug design by generating molecules with desired properties. FFLOM achieves state-of-the-art performance in terms of validity, uniqueness, novelty, and recovery of generated molecules. It also demonstrates excellent potential in practical scenarios including fragment linking, PROTAC design, R-group growing, and R-group optimization, generating molecules with novel fragments and higher binding affinity.

JOURNAL OF MEDICINAL CHEMISTRY (2023)

Review Chemistry, Multidisciplinary

Application of computational approaches in biomembranes: From structure to function

Jingjing Guo, Yiqiong Bao, Mengrong Li, Shu Li, Lili Xi, Pengyang Xin, Lei Wu, Huanxiang Liu, Yuguang Mu

Summary: Biological membranes are complex structures vital for life. Experimental investigation of biomembranes is challenging, but computational approaches such as molecular dynamics (MD) simulations have provided insights into their atomic details and cellular functions. This review highlights the latest advancements in computational methods, from force fields to MD simulations and trajectory analysis. It also discusses current research topics, challenges, and future directions for applying computational technologies in biomembrane systems.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2023)

暂无数据