ArticleBriefings in bioinformatics2023
EnGens: a computational framework for generation and analysis of representative protein conformational ensembles.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 21 citations in OpenAlex.
- OrgNet+: towards robust protein stability prediction with convolutional neural networks.Bioinformatics (Oxford, England) · 2026Article
- Binding of Steroid Hormones to the FA1 and FA6 Sites of Human Serum Albumin through Computational Biology and Quantum Biochemistry.ACS omega · 2026Article
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- IDPEnsembleTools: An open-source library for analysis of conformational ensembles of disordered proteins.Protein science : a publication of the Protein Society · 2026Article
- Special Issue: "Advanced Research on Molecular Modeling of Protein Structure and Functions".International journal of molecular sciences · 2025Article
- InSty: A ProDy Module for Evaluating Protein Interactions and Stability.Journal of molecular biology · 2025Article
- DINC-ensemble: A web server for docking large ligands incrementally to an ensemble of receptor conformations.Journal of molecular biology · 2025Article
- Equivariant learning leveraging geometric invariances in 3D molecular conformers for accurate prediction of quantum chemical properties.Scientific reports · 2025Article
- Systematic analysis of biomolecular conformational ensembles with PENSA.The Journal of chemical physics · 2025Article
- Domain Mobility in the ORF2p Complex Revealed by Molecular Dynamics Simulations and Big Data Analysis.International journal of molecular sciences · 2024Article
- Article
- Weighted families of contact maps to characterize conformational ensembles of (highly-)flexible proteins.Bioinformatics (Oxford, England) · 2024Article
- Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2-7 Binding Affinity.ACS chemical neuroscience · 2024Article
- Molecular Basis of Influence of A501X Mutations in Penicillin-Binding Protein 2 ofInternational journal of molecular sciences · 2024Article
- Protein ensemble modeling and analysis with MMMx.Protein science : a publication of the Protein Society · 2024Article
- Cell phenotypes can be predicted from propensities of protein conformations.Current opinion in structural biology · 2023Review
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Authors and funding
10 authors at 5 institutions in 4 countries.
Funding
Abstract
Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.
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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.