ReviewBriefings in bioinformatics2025
Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era.
Review in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed.
- MultilevelCurrent research in structural biology · 2026Article
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- DyProL: Dynamic Ensemble Representation Learning for Protein-Nucleic Acid Binding Site Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modeling conformational transitions in DNA, RNA, and protein-nucleic acid complexes.Nucleic acids research · 2026Article
- Collective Variable-Guided Engineering of the Free-Energy Surface of a Small Peptide.Journal of chemical information and modeling · 2026Article
- AI-Predicted Model-Guided Rebuilding of the Experimental Structure of Mouse δ-Aminolevulinic Acid Dehydratase.International journal of molecular sciences · 2026Article
- Artificial Intelligence and Protein Design: A retrospective study on 20-year emerging trends and core research areas from bibliometric perspectives.Probiotics and antimicrobial proteins · 2026Article
- Modular integration of nanopore sequencing, alphafold modeling, and statistical design boosts 1-hydroxyphenazine yield in Pseudomonas aeruginosa strain KAEH25.BMC microbiology · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A One Health Framework for Proteomics Across the Tree of Life to Advance Food Security, Animal Health, and Ecosystem Resilience.Proteomes · 2026Review
- A multiscale, Bayesian inference approach to augment mechanistic models of cell signaling with machine-learning predictions of binding affinity.PLoS computational biology · 2026Article
- The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction.Communications biology · 2026Article
- Alphafuser: a parsimonious approach to predicting higher-order protein complexes.Acta crystallographica. Section D, Structural biology · 2026Article
- Dynamic-Structure Redesign of Calmodulin Reveals Mechanistic Constraints on Ryr2 Regulation.bioRxiv : the preprint server for biology · 2026Article
- Improving the Reliability of Protein Folding Rate Predictions by Applying Guidelines for Validating QSAR/QSPR Models.International journal of molecular sciences · 2026Article
- Assessing the Performance of BioEmu in Understanding Protein Dynamics.International journal of molecular sciences · 2026Article
- AI-Driven Design of Miniproteins as Potential Allosteric Modulators.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Generating Dynamic Structures Through Physics-Based Sampling of Predicted Inter-Residue Geometries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computational approaches for RNA structure prediction and design.Cell reports. Physical science · 2026Article
- Chimeric Approach to Identify Molecular Determinants of Nicotinic Acetylcholine Receptors.International journal of molecular sciences · 2026Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The emergence of deep learning, particularly AlphaFold, has revolutionized static protein structure prediction, marking a transformative milestone in structural biology. However, protein function is not solely determined by static three-dimensional structures but is fundamentally governed by dynamic transitions between multiple conformational states. This shift from static to multi-state representations is crucial for understanding the mechanistic basis of protein function and regulation. This review outlines the fundamental concepts of protein dynamic conformations, surveys recent computational advances in modeling these dynamics in the post-AlphaFold era, and highlights key challenges, including data limitations, methodological constraints, and evaluation metrics. We also discuss potential strategies to address these challenges and explore future research directions to deepen our understanding of protein dynamics and their functional implications. This work aims to provide insights and perspectives to facilitate the ongoing development of protein conformation studies in the era of artificial intelligence-driven structural biology.
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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.