ArticleJournal of chemical theory and computation2022
Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers.
Article in Journal of chemical theory and computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.Journal of chemical information and modeling · 2025Review
- A Perspective on Protein Structure Prediction Using Quantum Computers.Journal of chemical theory and computation · 2024Review
- Freeprotmap: waiting-free prediction method for protein distance map.BMC bioinformatics · 2024Article
- Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.Journal of chemical theory and computation · 2024Article
- Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media.Journal of chemical theory and computation · 2024Article
- Approximating conformational Boltzmann distributions with AlphaFold2 predictions.bioRxiv : the preprint server for biology · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Collective Variable for Metadynamics Derived From AlphaFold Output.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Recently, predicting the native structures of proteins has become possible using computational molecular physics (CMP)─physics-based force fields sampled with proper statistics─but only for small proteins. Algorithms with better scaling are needed. We describe ML x MELD x MD, a molecular dynamics (MD) method that inputs residue contacts derived from machine learning (ML) servers into MELD, a Bayesian accelerator that preserves detailed-balance statistics. Contacts are derived from trRosetta-predicted distance histograms (distograms) and are integrated into MELD's atomistic MD as spatial restraints through parametrized potential functions. In the CASP14 blind prediction event, ML x MELD x MD predicted 13 native structures to better than 4.5 Å error, including for 10 proteins in the range of 115-250 amino acids long. Also, the scaling of simulation time vs protein length is much better than unguided MD:
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.