ArticleThe journal of physical chemistry. B2020
A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.
Article in The journal of physical chemistry. B, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Determination of α-Synuclein Protein Interactions by μMap Photoproximity Labeling.Journal of the American Chemical Society · 2026Article
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
- Human O-GlcNAcase catalytic-stalk dimer anchors flexible histone binding domains.Communications chemistry · 2025Article
- Targeting protein disorder: the next hurdle in drug discovery.Nature reviews. Drug discovery · 2025Review
- Determination of α-Synuclein Protein Interactions by μMap Photo-proximity Labeling.bioRxiv : the preprint server for biology · 2025Article
- Biological insights from integrative modeling of intrinsically disordered protein systems.Current opinion in structural biology · 2025Review
- Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0].Living journal of computational molecular science · 2025Article
- HumanResearch square · 2025Article
- Computational Methods to Investigate Intrinsically Disordered Proteins and their Complexes.ArXiv · 2024Article
- Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments.Bioinformatics (Oxford, England) · 2023Article
- Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modeling and Clustering.The journal of physical chemistry. B · 2023Article
- Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments.bioRxiv : the preprint server for biology · 2023Article
- Semi-Synthetic CoA-α-Synuclein Constructs Trap N-Terminal Acetyltransferase NatB for Binding Mechanism Studies.Journal of the American Chemical Society · 2023Article
- Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.The Journal of chemical physics · 2023Article
- Recent Advances in NMR Protein Structure Prediction with ROSETTA.International journal of molecular sciences · 2023Review
- SPyCi-PDB: A modular command-line interface for back-calculating experimental datatypes of protein structures.Journal of open source software · 2023Article
- Effects of Mutations and Post-Translational Modifications on α-Synuclein In Vitro Aggregation.Journal of molecular biology · 2022Review
- IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States.The journal of physical chemistry. A · 2022Article
- Article
- Unifying coarse-grained force fields for folded and disordered proteins.Current opinion in structural biology · 2022Review
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Authors and funding
2 authors.
Funding
Abstract
As recognition of the abundance and relevance of intrinsically disordered proteins (IDPs) continues to grow, demand increases for methods that can rapidly predict the conformational ensembles populated by these proteins. To date, IDP simulations have largely been dominated by molecular dynamics (MD) simulations, which require significant compute times and/or complex hardware. Recent developments in MD have afforded methods capable of simulating both ordered and disordered proteins, yet to date, accurate fold prediction from a sequence has been dominated by Monte Carlo (MC)-based methods such as Rosetta. To overcome the limitations of current approaches in IDP simulation using Rosetta while maintaining its utility for modeling folded domains, we developed PyRosetta-based algorithms that allow for the accurate de novo prediction of proteins across all degrees of foldedness along with structural ensembles of disordered proteins. Our simulations have accuracy comparable to state-of-the-art MD with vastly reduced computational demands.
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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.