ReviewBiochemical Society transactions2020
Computational methods for exploring protein conformations.
Review in Biochemical Society transactions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
39 citing papers in PubMed.
- Integrative Structural Bioinformatics and Molecular Dynamics Analyses of Synaptic Vesicle Proteins and Their Water-Soluble QTY-Variants Reveal Membrane Constraints and Evolutionary Coupling of T ⇔ V.Journal of molecular evolution · 2026Article
- Methionine coordination to the heme iron and its displacement by nitric oxide in the CcoP subunit of Pseudomonas aeruginosa cytochrome cbbJournal of inorganic biochemistry · 2026Article
- All-atom protein sequence design using discrete diffusion models.Journal of cheminformatics · 2025Article
- Evolution of the conformational ensemble and allosteric networks of apoptotic caspases in chordates.The Biochemical journal · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Journal of chemical information and modeling · 2025Article
- Toward the Prediction of Binding Events in Very Flexible, Allosteric, Multidomain Proteins.Journal of chemical information and modeling · 2025Article
- 3D variability analysis reveals a hidden conformational change controlling ammonia transport in human asparagine synthetase.Nature communications · 2024Article
- Predicting binding events in very flexible, allosteric, multi-domain proteins.bioRxiv : the preprint server for biology · 2024Article
- Molecular Dynamic Simulations Reveal that Water-Soluble QTY-Variants of Glutamate Transporters EAA1, EAA2 and EAA3 Retain the Conformational Characteristics of Native Transporters.Pharmaceutical research · 2024Article
- Employing Metadynamics to Predict the Membrane Partitioning of Carboxy-2The journal of physical chemistry. B · 2024Article
- 3D Variability Analysis Reveals a Hidden Conformational Change Controlling Ammonia Transport in Human Asparagine Synthetase.bioRxiv : the preprint server for biology · 2024Article
- APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.Research square · 2024Article
- De novo antioxidant peptide design via machine learning and DFT studies.Scientific reports · 2024Article
- Article
- Applications of Molecular Dynamics Simulations in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Cell phenotypes can be predicted from propensities of protein conformations.Current opinion in structural biology · 2023Review
- Addressing Challenges of Macrocyclic Conformational Sampling in Polar and Apolar Solvents: Lessons for Chameleonicity.Journal of chemical information and modeling · 2023Article
- Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding.Journal of chemical theory and computation · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.
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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.