ArticleBriefings in bioinformatics2024
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Benchmarking AI-driven protocols for cyclic peptide conformer prediction and ranking.RSC medicinal chemistry · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- HFGuidedDesign:Chemical science · 2026Article
- Cyclic Peptides as Modulators of Protein-Protein Interactions: A Survival Guide from Discovery Platforms to AI-Driven Design.International journal of molecular sciences · 2026Review
- Development of LRRC15-binding disulfide-constrained peptides for PET imaging of cancer-associated fibroblasts.Nature communications · 2026Article
- HighRes_Builder: improved access and modeling of noncanonical residues for protein structure prediction.Briefings in bioinformatics · 2026Article
- Fast Generation of Simulation-Quality Structural Ensembles of Mixed-Chirality Cyclic Peptides via Diffusion Models.Journal of chemical theory and computation · 2026Article
- Comparative Analysis of Deep Learning-Based Algorithms for Peptide Structure Prediction.Proteins · 2026Article
- CycloPepper: a machine learning platform for predicting cyclization outcomes and optimizing synthesis of therapeutic cyclopeptides.Nature communications · 2026Article
- CyclicMPNN: Stable Cyclic Peptide Sequence Generation.bioRxiv : the preprint server for biology · 2026Article
- Accurate de novo design of high-affinity protein-binding macrocycles using deep learning.Nature chemical biology · 2025Article
- HighFold-MeD: a Rosetta distillation model to accelerate structure prediction of cyclic peptides with backbone N-methylation and D-amino acids.Journal of cheminformatics · 2025Article
- Flexible Docking of Cyclic Peptides to Proteins Using CABS-dock.Journal of chemical theory and computation · 2025Article
- Accurate structure prediction of cyclic peptides containing unnatural amino acids using HighFold3.Briefings in bioinformatics · 2025Article
- Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking.Scientific reports · 2025Article
- Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.Briefings in bioinformatics · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Heuristic energy-based cyclic peptide design.PLoS computational biology · 2025Article
- Heuristic energy-based cyclic peptide design.bioRxiv : the preprint server for biology · 2025Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.
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Registered trials
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