IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction.
Proteins
; 89(10): 1277-1288, 2021 10.
Article
em En
| MEDLINE
| ID: mdl-33993559
ABSTRACT
There is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.
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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Conformação Proteica
/
Proteínas
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Biologia Computacional
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article