Journal
JOURNAL OF CHEMINFORMATICS
Volume 13, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13321-021-00547-7
Keywords
Ligand binding sites; Binding site prediction; Deep residual network; Convolutional neural network; Data cleaning
Categories
Funding
- National Research Foundation of Korea(NRF) - Korea government(MSIT) [2020R1A2C2005612]
- Brain Research Program of the National Research Foundation (NRF) - Korean government (MSIT) [NRF-2017M3C7A1044816]
- Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) from the Ministry of Trade, Industry & Energy, Republic of Korea [20204010600470]
Ask authors/readers for more resources
This study introduces a deep learning model and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. The method achieved better and justifiable performance when evaluating two independent datasets using metrics such as distance, volume, and proportion.
Background Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required. Results In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available