ArticleThe journal of physical chemistry. A2022
IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States.
Article in The journal of physical chemistry. A, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 61 citations in OpenAlex.
- Review
- Backbone rigidity of disordered protein linkers from NMR experiments and MD simulations.Biophysical journal · 2026Article
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- Characterization of flexible RNA binding by tandem RNA recognition motifs through integrative ensemble modelling.Nucleic acids research · 2026Article
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Disulfide engineering reveals unexpected pro- and anti-aggregation conformers of human α-synuclein.bioRxiv : the preprint server for biology · 2026Article
- Predicting Pose Distribution of Protein Domains Connected by Flexible Linkers Is an Unsolved Problem.Proteins · 2026Article
- NMR in the Age of Modern Biomedical Research and Drug Discovery.Journal of molecular biology · 2025Review
- AlphaFlex: Ensembles of the human proteome representing disordered regions.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-assisted tools to understand the structural biology of the synapse.Biomedical engineering letters · 2025Review
- Targeting protein disorder: the next hurdle in drug discovery.Nature reviews. Drug discovery · 2025Review
- Deciphering conformational dynamics in AFM data using fast nonlinear NMA and FFT-based search with AFMFit.Communications biology · 2025Article
- Quality Evaluation Based Simulation Selection (QEBSS) for analysis of conformational ensembles and dynamics of multidomain proteins.Communications chemistry · 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
- The ribonucleoprotein hnRNPA1 mediates binding to RNA and DNA telomeric G-quadruplexes through an RGG-rich region.The Journal of biological chemistry · 2025Article
- Decoding Order and Disorder in Proteins by NMR Spectroscopy.Journal of the American Chemical Society · 2025Article
- Martini3-IDP: improved Martini 3 force field for disordered proteins.Nature communications · 2025Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.
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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.