ArticleProceedings of the National Academy of Sciences of the United States of America2015
Accelerating molecular simulations of proteins using Bayesian inference on weak information.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Ensemble Sensitivity to Chemical Modifications in Free and Bound Macrocyclic Peptides.The journal of physical chemistry. B · 2026Article
- Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties.Chemical reviews · 2025Review
- Hierarchical Extended Linkage Method (HELM)'s Deep Dive into Hybrid Clustering Strategies.Journal of chemical information and modeling · 2025Article
- PERRC: Protease Engineering with Reactant Residence Time Control.ACS synthetic biology · 2025Article
- Structural differentiation of protein charge state conformers via gas-phase ion/ion cross-linking mass spectrometry.International journal of mass spectrometry · 2025Article
- MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.Journal of chemical information and modeling · 2025Review
- RNA-Puzzles Round V: blind predictions of 23 RNA structures.Nature methods · 2025Article
- Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices.Journal of chemical theory and computation · 2024Article
- Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.Nature methods · 2024Article
- FoldPAthreader: predicting protein folding pathway using a novel folding force field model derived from known protein universe.Genome biology · 2024Article
- DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.Nature methods · 2024Article
- An implementation of the Martini coarse-grained force field in OpenMM.Biophysical journal · 2023Article
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Structural predictions of protein-DNA binding: MELD-DNA.Nucleic acids research · 2023Article
- Deep learning to decompose macromolecules into independent Markovian domains.Nature communications · 2022Article
- Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers.Journal of chemical theory and computation · 2022Article
- Searching for Low Probability Opening Events in a DNA Sliding Clamp.Life (Basel, Switzerland) · 2022Article
- From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output.Frontiers in pharmacology · 2022Review
- Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures.The journal of physical chemistry. B · 2021Article
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3 authors.
Funding
Abstract
Atomistic molecular dynamics (MD) simulations of protein molecules are too computationally expensive to predict most native structures from amino acid sequences. Here, we integrate "weak" external knowledge into folding simulations to predict protein structures, given their sequence. For example, we instruct the computer "to form a hydrophobic core," "to form good secondary structures," or "to seek a compact state." This kind of information has been too combinatoric, nonspecific, and vague to help guide MD simulations before. Within atomistic replica-exchange molecular dynamics (REMD), we develop a statistical mechanical framework, modeling using limited data with coarse physical insight(s) (MELD + CPI), for harnessing weak information. As a test, we apply MELD + CPI to predict the native structures of 20 small proteins. MELD + CPI samples to within less than 3.2 Å from native for all 20 and correctly chooses the native structures (<4 Å) for 15 of them, including ubiquitin, a millisecond folder. MELD + CPI is up to five orders of magnitude faster than brute-force MD, satisfies detailed balance, and should scale well to larger proteins. MELD + CPI may be useful where physics-based simulations are needed to study protein mechanisms and populations and where we have some heuristic or coarse physical knowledge about states of interest.
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