![]() Our algorithm employs a convolutional neural network, a class of deep learning already commonly used in visual imagery analysis, recommender systems, and natural language processing. In order to improve the detection and classification of binding pockets in proteins, we developed a new computational tool, DeepDrug3D. Annotating ligand-binding sites is complicated by a fact that the same small molecule often binds to similar pockets but located in different proteins. A typical ligand-binding site is a small pocket formed by a few residues while the remaining protein structure acts as a framework providing the correct orientation of binding residues. ![]() Small organic ligands bind to the locations of chemical specificity and affinity on their protein targets, called binding sites. DeepDrug3D is available as an open-source program at with the accompanying TOUGH-C1 benchmarking dataset accessible from. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. On that account, novel algorithms to accurately classify binding sites are needed. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles.
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