DOI: 10.1073/pnas.1814945115
论文题名: Combined molecular dynamics and neural network method for predicting protein antifreeze activity
作者: Kozuch D.J. ; Stillinger F.H. ; Debenedetti P.G.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2018
卷: 115, 期: 52 起始页码: 13252
结束页码: 13257
语种: 英语
英文关键词: Antifreeze
; Molecular dynamics
; Neural networks
; Proteins
; Simulation
Scopus关键词: antifreeze protein
; water
; antifreeze protein
; Article
; artificial neural network
; automation
; controlled study
; molecular dynamics
; prediction
; priority journal
; protein binding
; protein function
; protein structure
; quantitative analysis
; surface property
; thermal analysis
; animal
; chemistry
; crystallization
; freezing
; human
; kinetics
; metabolism
; protein conformation
; temperature
; theoretical model
; thermodynamics
; Animals
; Antifreeze Proteins
; Crystallization
; Freezing
; Humans
; Kinetics
; Models, Theoretical
; Molecular Dynamics Simulation
; Neural Networks (Computer)
; Protein Conformation
; Temperature
; Thermodynamics
; Water
英文摘要: 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. © 2018 National Academy of Sciences. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163627
Appears in Collections: 气候变化与战略
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作者单位: Kozuch, D.J., Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, United States; Stillinger, F.H., Department of Chemistry, Princeton University, Princeton, NJ 08544, United States; Debenedetti, P.G., Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, United States
Recommended Citation:
Kozuch D.J.,Stillinger F.H.,Debenedetti P.G.. Combined molecular dynamics and neural network method for predicting protein antifreeze activity[J]. Proceedings of the National Academy of Sciences of the United States of America,2018-01-01,115(52)