TY - JOUR
T1 - Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network
AU - Nguyen, Trinh Trung Duong
AU - Le, Nguyen Quoc Khanh
AU - Kusuma, Rosdyana Mangir Irawan
AU - Ou, Yu Yen
N1 - Funding Information:
This research partially supported by Ministry of Science and Technology, Taiwan, R.O.C . under Grant no. MOST 104-2221-E-155-037 and 105-2221-E-155-065 .
Funding Information:
This research partially supported by Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 104-2221-E-155-037 and 105-2221-E-155-065.
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Membrane proteins, the most important drug targets, account for around 30% of total proteins encoded by the genome of living organisms. An important role of these proteins is to bind adenosine triphosphate (ATP), facilitating crucial biological processes such as metabolism and cell signaling. There are several reports elucidating ATP-binding sites within proteins. However, such studies on membrane proteins are limited. Our prediction tool, DeepATP, combines evolutionary information in the form of Position Specific Scoring Matrix and two-dimensional Convolutional Neural Network to predict ATP-binding sites in membrane proteins with an MCC of 0.89 and an AUC of 99%. Compared to recently published ATP-binding site predictors and classifiers that use traditional machine learning algorithms, our approach performs significantly better. We suggest this method as a reliable tool for biologists for ATP-binding site prediction in membrane proteins.
AB - Membrane proteins, the most important drug targets, account for around 30% of total proteins encoded by the genome of living organisms. An important role of these proteins is to bind adenosine triphosphate (ATP), facilitating crucial biological processes such as metabolism and cell signaling. There are several reports elucidating ATP-binding sites within proteins. However, such studies on membrane proteins are limited. Our prediction tool, DeepATP, combines evolutionary information in the form of Position Specific Scoring Matrix and two-dimensional Convolutional Neural Network to predict ATP-binding sites in membrane proteins with an MCC of 0.89 and an AUC of 99%. Compared to recently published ATP-binding site predictors and classifiers that use traditional machine learning algorithms, our approach performs significantly better. We suggest this method as a reliable tool for biologists for ATP-binding site prediction in membrane proteins.
KW - Bioinformatics
KW - Convolutional neural network
KW - Deep learning
KW - Imbalanced data
KW - Membrane protein
KW - Position specific scoring matrix
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U2 - 10.1016/j.jmgm.2019.07.003
DO - 10.1016/j.jmgm.2019.07.003
M3 - Article
C2 - 31344547
AN - SCOPUS:85069720700
SN - 1093-3263
VL - 92
SP - 86
EP - 93
JO - Journal of Molecular Graphics and Modelling
JF - Journal of Molecular Graphics and Modelling
ER -