Abstract
Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, that considers protein conformations as 3D-images, binding sites as objects on these images to detect, and conformational ensembles of proteins as 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding site in G protein-coupled receptor. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minutes to analyze 1000 conformations of a protein with ~2000 atoms.
Highlights
Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery
The input to the BiteNet is the spatial structure of a protein and the output is the centers of the predicted binding sites along with the probability scores
In this study we introduced BiteNet, a deep learning approach for spatiotemporal identification of binding sites
Summary
Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Identification of novel binding sites expands druggable genome and opens new strategies for therapy and drug discovery[1]. Drug-like molecules target either orthosteric binding site, where protein interacts with endogenous molecules, or topologically distinct allosteric binding sites[2]. The latter is of a special interest, because allosteric binding sites exhibit higher degree of sequence diversity between protein subtypes, allowing to design more selective ligands, in contrast to the orthosteric ligands[3,4,5]. Computational methods allow to perform large scale binding site identification, investigate protein flexibility via molecular dynamics simulation, and probe to fit chemical compounds using virtual ligand or fragment-based screening. In spite of present progress, large-scale binding site detection remains to be a challenge, let alone that there is still a big room for improvement in terms of the method’s accuracy[28]
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