Combined molecular dynamics and neural network method for predicting protein antifreeze activity.
Proc Natl Acad Sci U S A
; 115(52): 13252-13257, 2018 12 26.
Article
em En
| MEDLINE
| ID: mdl-30530650
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Água
/
Redes Neurais de Computação
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Proteínas Anticongelantes
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Simulação de Dinâmica Molecular
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Modelos Teóricos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
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Humans
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article