Abstract
BackgroundPredicting 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.ResultsIn 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.
Highlights
In living organisms, all biological processes involve proteins that are dynamic molecules with functions almost invariably dependent on the interactions with other molecules, which are affected in physiologically important ways through subtle, or striking changes in the protein conformation [1]
We propose new metrics, the Proportion of Ligand Inside (PLI) for the accountability of ligands and predicted binding sites
Proportion of Ligand Inside (PLI) In case of ligands using discretized volume overlap (DVO) metrics to find overlap does not provide a comprehensive idea of the overlap, the binding sites are usually larger than the ligand
Summary
All biological processes involve proteins that are dynamic molecules with functions almost invariably dependent on the interactions with other molecules, which are affected in physiologically important ways through subtle, or striking changes in the protein conformation [1]. Such interactions occur in a specific site of a protein known as binding site, and any interacting molecule, ion, or protein is known as ligand. Other than the traditional methods for predicting the binding site, which are based on geometry, energy, evolutionary, consensus, and template, machine learning and deep learning methods have successfully emerged in recent years. 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 further investigation is required
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