ArticleFrontiers in molecular biosciences2022
Collective Variable for Metadynamics Derived From AlphaFold Output.
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 6 papers.
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6 citing papers in PubMed.
- The Arch from the Stones: Understanding Protein Folding Energy Landscapes via Bioinspired Collective Variables.The journal of physical chemistry letters · 2025Article
- The multifaceted roles of aldolase A in cancer: glycolysis, cytoskeleton, translation and beyond.Human cell · 2025Review
- Unlocking the specificity of antimicrobial peptide interactions for membrane-targeted therapies.Computational and structural biotechnology journal · 2024Article
- Folding of N-terminally acetylated α-synuclein upon interaction with lipid membranes.Biophysical journal · 2024Article
- Enhanced Sampling with Machine Learning.Annual review of physical chemistry · 2024Review
- Molecular dynamics analysis of biomolecular systems including nucleic acids.Biophysics and physicobiology · 2022Article
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3 authors.
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Abstract
AlphaFold is a neural network-based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here, we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. Using parallel tempering metadynamics, we simulated the folding of a mini-protein Trp-cage and
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