StreaMD: the toolkit for high-throughput molecular dynamics simulations

被引:0
作者
Ivanova, Aleksandra [1 ]
Mokshyna, Olena [1 ,2 ]
Polishchuk, Pavel [1 ]
机构
[1] Palacky Univ, Inst Mol & Translat Med, Fac Med & Dent, Hnevotinska 5, Olomouc 77900, Czech Republic
[2] Czech Acad Sci, Inst Organ Chem & Biochem, Flemingovo Namesti 542-2, Prague 6, Czech Republic
来源
JOURNAL OF CHEMINFORMATICS | 2024年 / 16卷 / 01期
关键词
Molecular dynamics; High-throughput molecular dynamics; Distributed simulations; GROMACS; SOFTWARE NEWS;
D O I
10.1186/s13321-024-00918-w
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.
引用
收藏
页数:13
相关论文
共 29 条
  • [1] NEW METHOD FOR PREDICTING BINDING-AFFINITY IN COMPUTER-AIDED DRUG DESIGN
    AQVIST, J
    MEDINA, C
    SAMUELSSON, JE
    [J]. PROTEIN ENGINEERING, 1994, 7 (03): : 385 - 391
  • [2] A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations
    Bahia, Malkeet Singh
    Kaspi, Omer
    Touitou, Meir
    Binayev, Idan
    Dhail, Seema
    Spiegel, Jacob
    Khazanov, Netaly
    Yosipof, Abraham
    Senderowitz, Hanoch
    [J]. MOLECULAR INFORMATICS, 2023, 42 (04)
  • [3] ProLIF: a library to encode molecular interactions as fingerprints
    Bouysset, Cedric
    Fiorucci, Sebastien
    [J]. JOURNAL OF CHEMINFORMATICS, 2021, 13 (01)
  • [4] Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial
    Bray, Simon A.
    Senapathi, Tharindu
    Barnett, Christopher B.
    Gruening, Bjoern A.
    [J]. JOURNAL OF CHEMINFORMATICS, 2020, 12 (01)
  • [5] Discovery of N-(4-(Benzyloxy)-phenyl)-sulfonamide Derivatives as Novel Antagonists of the Human Androgen Receptor Targeting the Activation Function 2
    Chai, Xin
    Sun, Huiyong
    Zhou, Wenfang
    Chen, Changwei
    Shan, Luhu
    Yang, Yuhui
    He, Junzhao
    Pang, Jinping
    Yang, Liu
    Wang, Xinyue
    Cui, Sunliang
    Fu, Yaqin
    Xu, Xiaohong
    Xu, Lei
    Yao, Xiaojun
    Li, Dan
    Hou, Tingjun
    [J]. JOURNAL OF MEDICINAL CHEMISTRY, 2022, 65 (03) : 2507 - 2521
  • [6] Role of Molecular Dynamics and Related Methods in Drug Discovery
    De Vivo, Marco
    Masetti, Matteo
    Bottegoni, Giovanni
    Cavalli, Andrea
    [J]. JOURNAL OF MEDICINAL CHEMISTRY, 2016, 59 (09) : 4035 - 4061
  • [7] HTMD: High-Throughput Molecular Dynamics for Molecular Discovery
    Doerr, S.
    Harvey, M. J.
    Noe, Frank
    De Fabritiis, G.
    [J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2016, 12 (04) : 1845 - 1852
  • [8] Interaction Entropy: A New Paradigm for Highly Efficient and Reliable Computation of Protein-Ligand Binding Free Energy
    Duan, Lili
    Liu, Xiao
    Zhang, John Z. H.
    [J]. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2016, 138 (17) : 5722 - 5728
  • [9] OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
    Eastman, Peter
    Swails, Jason
    Chodera, John D.
    McGibbon, Robert T.
    Zhao, Yutong
    Beauchamp, Kyle A.
    Wang, Lee-Ping
    Simmonett, Andrew C.
    Harrigan, Matthew P.
    Stern, Chaya D.
    Wiewiora, Rafal P.
    Brooks, Bernard R.
    Pande, Vijay S.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2017, 13 (07)
  • [10] On the Use of Interaction Entropy and Related Methods to Estimate Binding Entropies
    Ekberg, Vilhelm
    Ryde, Ulf
    [J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2021, 17 (08) : 5379 - 5391