ArticleFrontiers in molecular biosciences2022
Challenges in describing the conformation and dynamics of proteins with ambiguous behavior.
Article in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Biological and Biologically Inspired Functional Nanostructures: Insights into Structural, Optical, Thermal, and Sensing Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of the influenza A virus.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.International journal of molecular sciences · 2024Review
- DisProt in 2024: improving function annotation of intrinsically disordered proteins.Nucleic acids research · 2024Article
- Recent Advances in Protein Folding Pathway Prediction through Computational Methods.Current medicinal chemistry · 2024Review
- In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease.International journal of molecular sciences · 2023Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Traditionally, our understanding of how proteins operate and how evolution shapes them is based on two main data sources: the overall protein fold and the protein amino acid sequence. However, a significant part of the proteome shows highly dynamic and/or structurally ambiguous behavior, which cannot be correctly represented by the traditional fixed set of static coordinates. Representing such protein behaviors remains challenging and necessarily involves a complex interpretation of conformational states, including probabilistic descriptions. Relating protein dynamics and multiple conformations to their function as well as their physiological context (e.g., post-translational modifications and subcellular localization), therefore, remains elusive for much of the proteome, with studies to investigate the effect of protein dynamics relying heavily on computational models. We here investigate the possibility of delineating three classes of protein conformational behavior: order, disorder, and ambiguity. These definitions are explored based on three different datasets, using interpretable machine learning from a set of features, from AlphaFold2 to sequence-based predictions, to understand the overlap and differences between these datasets. This forms the basis for a discussion on the current limitations in describing the behavior of dynamic and ambiguous proteins.
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