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

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.

authors

Kozuch DJ,Stillinger FH,Debenedetti PG

doi

10.1073/pnas.1814945115

subject

Has Abstract

pub_date

2018-12-26 00:00:00

pages

13252-13257

issue

52

eissn

0027-8424

issn

1091-6490

pii

1814945115

journal_volume

115

pub_type

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