ArticleScientific data2023
AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention.Nature communications · 2026Article
- Scaling Graph Neural Networks to Large Proteins.Journal of chemical theory and computation · 2025Article
- Article
- Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050.ACS physical chemistry Au · 2024Review
- Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.Nature communications · 2024Article
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Authors and funding
5 authors.
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Abstract
Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy.
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