ArticleProceedings of the National Academy of Sciences of the United States of America2018
Combined molecular dynamics and neural network method for predicting protein antifreeze activity.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. 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, 50 citations in OpenAlex.
- Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies.PNAS nexus · 2024Article
- On the engulfment of antifreeze proteins by ice.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Computational Design of Peptides for Biomaterials Applications.ACS applied bio materials · 2024Review
- Divergent Mechanisms of Ice Growth Inhibition by Antifreeze Proteins.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Analysis of Ice-Binding Protein Evolution.Methods in molecular biology (Clifton, N.J.) · 2024Article
- High Water Density at Non-Ice-Binding Surfaces Contributes to the Hyperactivity of Antifreeze Proteins.The journal of physical chemistry letters · 2021Article
- Insights into the interactions of bisphenol and phthalate compounds with unamended and carnitine-amended montmorillonite clays.Computers & chemical engineering · 2020Article
- Peptidic Antifreeze Materials: Prospects and Challenges.International journal of molecular sciences · 2019Review
- Ice-binding proteins and the applicability and limitations of the kinetic pinning model.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antifreeze proteins (AFPs) are a diverse class of proteins that depress the kinetically observable freezing point of water. AFPs have been of scientific interest for decades, but the lack of an accurate model for predicting AFP activity has hindered the logical design of novel antifreeze systems. To address this, we perform molecular dynamics simulation for a collection of well-studied AFPs. By analyzing both the dynamic behavior of water near the protein surface and the geometric structure of the protein, we introduce a method that automatically detects the ice binding face of AFPs. From these data, we construct a simple neural network that is capable of quantitatively predicting experimentally observed thermal hysteresis from a trio of relevant physical variables. The model's accuracy is tested against data for 17 known AFPs and 5 non-AFP controls.
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