A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.
IEEE/ACM Trans Comput Biol Bioinform
; 18(4): 1336-1349, 2021.
Article
em En
| MEDLINE
| ID: mdl-31603792
ABSTRACT
In order to successfully predict a proteins function throughout its trajectory, in addition to uncovering changes in its conformational state, it is necessary to employ techniques that maintain its 3D information while performing at scale. We extend a protein representation that encodes secondary and tertiary structure into fix-sized, color images, and a neural network architecture (called GEM-net) that leverages our encoded representation. We show the applicability of our method in two ways (1) performing protein function prediction, hitting accuracy between 78 and 83 percent, and (2) visualizing and detecting conformational changes in protein trajectories during molecular dynamics simulations.
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Conformação Proteica
/
Gráficos por Computador
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Processamento de Imagem Assistida por Computador
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Proteínas
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Biologia Computacional
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
ACM Trans Comput Biol Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2021
Tipo de documento:
Article