PaCS-Q: Python Toolkits for Path Sampling in MD and QM/MM MD Simulation
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| Title: | PaCS-Q: Python Toolkits for Path Sampling in MD and QM/MM MD Simulation |
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| Authors: | Lian Duan, Kowit Hengphasatporn, Yasuteru Shigeta |
| Publication Year: | 2025 |
| Subject Terms: | Biochemistry, Medicine, Genetics, Evolutionary Biology, Sociology, Inorganic Chemistry, Plant Biology, Space Science, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, studying covalent reactions, parallel cascade selection, install via pip, https :// github, enabling efficient exploration, source python toolkit, enabling advanced simulations, amber md suite, python toolkits, md simulations, versatile solution, unbinding events, structure analysis, seamlessly integrated, representative structures, quantum calculations, path sampling, orca directly, mm md |
| Description: | PaCS-Q is an open-source Python toolkit that simplifies QM/MM MD and MD simulations, making complex pathway sampling accessible and user-friendly. Seamlessly integrated with the AMBER MD suite, it automates QM/MM MD simulations using the parallel cascade selection (PaCS) algorithm, enabling efficient exploration of reaction pathways without predefined reaction coordinates. PaCS-Q supports both RMSD- and distance-based sampling, which is ideal for studying covalent reactions and ligand binding/unbinding events. A key feature is its ability to automatically generate QM input files for Gaussian and ORCA directly from representative structures, streamlining the transition from MD to quantum calculations. With built-in tools for structure analysis and energy profiling, PaCS-Q minimizes setup complexity and enhances reproducibility. Easy to install via pip and compatible with Unix-based systems, PaCS-Q offers a practical, versatile solution for researchers in computational chemistry and drug discovery, enabling advanced simulations with speed, accuracy, and minimal effort. The PaCS-Q Python toolkit publicly available at https://github.com/nyelidl/PaCS-Q/. |
| Document Type: | article in journal/newspaper |
| Language: | unknown |
| DOI: | 10.1021/acs.jcim.5c00936.s001 |
| Availability: | https://doi.org/10.1021/acs.jcim.5c00936.s001 https://figshare.com/articles/journal_contribution/PaCS-Q_Python_Toolkits_for_Path_Sampling_in_MD_and_QM_MM_MD_Simulation/29413230 |
| Rights: | CC BY-NC 4.0 |
| Accession Number: | edsbas.96DB39C6 |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | PaCS-Q is an open-source Python toolkit that simplifies QM/MM MD and MD simulations, making complex pathway sampling accessible and user-friendly. Seamlessly integrated with the AMBER MD suite, it automates QM/MM MD simulations using the parallel cascade selection (PaCS) algorithm, enabling efficient exploration of reaction pathways without predefined reaction coordinates. PaCS-Q supports both RMSD- and distance-based sampling, which is ideal for studying covalent reactions and ligand binding/unbinding events. A key feature is its ability to automatically generate QM input files for Gaussian and ORCA directly from representative structures, streamlining the transition from MD to quantum calculations. With built-in tools for structure analysis and energy profiling, PaCS-Q minimizes setup complexity and enhances reproducibility. Easy to install via pip and compatible with Unix-based systems, PaCS-Q offers a practical, versatile solution for researchers in computational chemistry and drug discovery, enabling advanced simulations with speed, accuracy, and minimal effort. The PaCS-Q Python toolkit publicly available at https://github.com/nyelidl/PaCS-Q/. |
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| DOI: | 10.1021/acs.jcim.5c00936.s001 |