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Prediction of Protein Metal Binding Sites Using Deep Neural Networks
Author(s) -
Haberal İsmail,
Oğul Hasan
Publication year - 2019
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201800169
Subject(s) - convolutional neural network , computational biology , computer science , structural bioinformatics , artificial neural network , protein function prediction , artificial intelligence , function (biology) , protein structure prediction , deep learning , machine learning , protein structure , chemistry , protein function , biology , biochemistry , genetics , gene
Abstract Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three‐dimensional structure by folding show which vital function is fulfilled. The prediction of metal‐binding in proteins will be considered as a step‐in function assignment for new proteins, which helps to obtain functional proteins in genomic studies, is critical to protein function annotation and drug discovery. Computational predictions made by using machine learning methods from the data obtained from amino acid sequences are widely used in the protein metal‐binding and various bioinformatics fields. In this work, we present three different deep learning architectures for prediction of metal‐binding of Histidines (HIS) and Cysteines (CYS) amino acids. These architectures are as follows: 2D Convolutional Neural Network, Long‐Short Term Memory and Recurrent Neural Network. Their comparison is carried out on the three different sets of attributes derived from a public dataset of protein sequences. These three sets of features extracted from the protein sequence were obtained using the PAM scoring matrix, protein composition server, and binary representation methods. The results show that a better performance for prediction of protein metal‐ binding sites is obtained through Convolutional Neural Network architecture.