SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction
| Autor: | Francesco Trozzi, Yunwen Tao, Xingming Qu, Eric C. Larson, Nischal Karki, Elfi Kraka, Niraj Verma, Brian D. Zoltowski, Mohamed Elsaied |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Computer science Pipeline (computing) Allosteric regulation Datasets as Topic Computational biology Ligands 010402 general chemistry 01 natural sciences Protein Structure Secondary Article Catalysis drug discovery Inorganic Chemistry 03 medical and health sciences Protein Domains Peptide bond Animals Humans Physical and Theoretical Chemistry Binding site Caenorhabditis elegans Molecular Biology Protein secondary structure Spectroscopy 030304 developmental biology 0303 health sciences Binding Sites Ligand Drug discovery business.industry Deep learning Organic Chemistry A protein deep learning General Medicine Ligand (biochemistry) 0104 chemical sciences Computer Science Applications protein-ligand interaction 030104 developmental biology SSnet Artificial intelligence business Protein Binding Protein ligand |
| Zdroj: | International Journal of Molecular Sciences |
| ISSN: | 1422-0067 |
| DOI: | 10.3390/ijms22031392 |
| Popis: | Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-machine learning models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure based deep learning, which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research while being readily accessible for de novo drug designers as a standalone package. |
| Databáze: | OpenAIRE |
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