Combined molecular dynamics and neural network method for predicting protein antifreeze activity

Daniel J. Kozuch, Frank H. Stillinger, Pablo Gaston Debenedetti

Research output: Contribution to journalArticle

14 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)13252-13257
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume115
Issue number52
DOIs
StatePublished - Dec 26 2018

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Antifreeze
  • Molecular dynamics
  • Neural networks
  • Proteins
  • Simulation

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