ArticleChemical science2021
Structure prediction of cyclic peptides by molecular dynamics + machine learning.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 66 citations in OpenAlex.
- Benchmarking AI-driven protocols for cyclic peptide conformer prediction and ranking.RSC medicinal chemistry · 2026Article
- Overcoming LPS-mediated resistance in gram-negative pathogens: a review of LL-37 analogs and computational design strategies.Archives of microbiology · 2026Review
- Fast Generation of Simulation-Quality Structural Ensembles of Mixed-Chirality Cyclic Peptides via Diffusion Models.Journal of chemical theory and computation · 2026Article
- From Membrane Composition to Antimicrobial Strategies: Experimental and Computational Approaches to AMP Design and Selectivity.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- StrEAMM-Thioether: Efficient Structure Prediction for Thioether-Linked Cyclic Peptides.The journal of physical chemistry. B · 2026Article
- Isothiocyanate-mediated cyclization of phage-displayed peptides enables discovery of macrocyclic binders.Science advances · 2026Article
- Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.World journal of microbiology & biotechnology · 2025Review
- Robust Conformational Space Exploration of Cyclic Peptides by Combining Different MD Protocols and Force Fields.Journal of chemical theory and computation · 2025Article
- Hydrophobicity-Controlled Self-Assembly of Supramolecular Peptide Nanotubes in Water.Angewandte Chemie (International ed. in English) · 2025Article
- Cyclic peptide structure prediction and design using AlphaFold2.Nature communications · 2025Article
- Computational Design of a Bicyclic Peptide Inhibitor Targeting the ICOS/ICOS-L Protein-Protein Interaction.Chemical biology & drug design · 2025Article
- Molecular Dynamics (MD)-Derived Features for Canonical and Noncanonical Amino Acids.Journal of chemical information and modeling · 2025Article
- Unsupervised learning of collective variables for conformational sampling of cyclic peptides.Methods in enzymology · 2025Article
- Tackling Undruggable Targets with Designer Peptidomimetics and Synthetic Biologics.Chemical reviews · 2024Review
- Article
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides.The journal of physical chemistry. B · 2024Article
- HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.Briefings in bioinformatics · 2024Article
- Molecular chameleons in drug discovery.Nature reviews. Chemistry · 2024Review
- Computational Prediction of Cyclic Peptide Structural Ensembles and Application to the Design of Keap1 Binders.Journal of chemical information and modeling · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Recent computational methods have made strides in discovering well-structured cyclic peptides that preferentially populate a single conformation. However, many successful cyclic-peptide therapeutics adopt multiple conformations in solution. In fact, the chameleonic properties of some cyclic peptides are likely responsible for their high cell membrane permeability. Thus, we require the ability to predict complete structural ensembles for cyclic peptides, including the majority of cyclic peptides that have broad structural ensembles, to significantly improve our ability to rationally design cyclic-peptide therapeutics. Here, we introduce the idea of using molecular dynamics simulation results to train machine learning models to enable efficient structure prediction for cyclic peptides. Using molecular dynamics simulation results for several hundred cyclic pentapeptides as the training datasets, we developed machine-learning models that can provide molecular dynamics simulation-quality predictions of structural ensembles for all the hundreds of thousands of sequences in the entire sequence space. The prediction for each individual cyclic peptide can be made using less than 1 second of computation time. Even for the most challenging classes of poorly structured cyclic peptides with broad conformational ensembles, our predictions were similar to those one would normally obtain only after running multiple days of explicit-solvent molecular dynamics simulations. The resulting method, termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), is the first technique capable of efficiently predicting complete structural ensembles of cyclic peptides without relying on additional molecular dynamics simulations, constituting a seven-order-of-magnitude improvement in speed while retaining the same accuracy as explicit-solvent simulations.
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What OpenQuestion holds
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.