Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145360
Title: DeepBindPoc : a deep learning method to rank ligand binding pockets using molecular vector representation
Authors: Zhang, Haiping
Saravanan, Konda Mani
Lin, Jinzhi
Liao, Linbu
Ng, Justin Tze-Yang
Zhou, Jiaxiu
Wei, Yanjie
Keywords: Science::Biological sciences
Issue Date: 2020
Source: Zhang, H., Saravanan, K. M., Lin. J., Liao, L., Ng, J. T.-Y., Zhou, J., & Wei, Y. (2020). DeepBindPoc : a deep learning method to rank ligand binding pockets using molecular vector representation. PeerJ, 8, e8864-. doi:10.7717/peerj.8864
Journal: PeerJ
Abstract: Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design. In recent years, several deep learning models were developed to learn important physical-chemical and spatial information to predict ligand-binding pockets in a protein. However, ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for computational molecular biologists using a single web-based tool. Hence, we believe, by using closer to real application data set as training and by providing ligand information, an enhanced model to identify accurate pockets can be obtained. In this article, we propose a new deep learning method called DeepBindPoc for identifying and ranking ligand-binding pockets in proteins. The model is built by using information about the binding pocket and associated ligand. We take advantage of the mol2vec tool to represent both the given ligand and pocket as vectors to construct a densely fully connected layer model. During the training, important features for pocket-ligand binding are automatically extracted and high-level information is preserved appropriately. DeepBindPoc demonstrated a strong complementary advantage for the detection of native-like pockets when combined with traditional popular methods, such as fpocket and P2Rank. The proposed method is extensively tested and validated with standard procedures on multiple datasets, including a dataset with G-protein Coupled receptors. The systematic testing and validation of our method suggest that DeepBindPoc is a valuable tool to rank near-native pockets for theoretically modeled protein with unknown experimental active site but have known ligand. The DeepBindPoc model described in this article is available at GitHub (https://github.com/haiping1010/DeepBindPoc) and the webserver is available at (http://cbblab.siat.ac.cn/DeepBindPoc/index.php).
URI: https://hdl.handle.net/10356/145360
ISSN: 2167-8359
DOI: 10.7717/peerj.8864
Rights: © 2020 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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