Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 140, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.105119
Keywords
SARS-CoV-2; Antiviral drug; Virus-drug association; Unbalanced bi-random walk; Laplacian regularized least squares; Molecular docking; Molecular dynamics simulation
Categories
Funding
- National Natural Science Foundation of China [61803151, 62072172, 62172158]
- Natural Science Foundation of Hunan province [2021JJ30219]
- Scientific Research Project of Hunan Provincial Department of Education [20C0636]
- Scientific Research and Innovation Foundation of Hunan University of Technology [CX2031]
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This study developed a Virus-Drug Association (VDA) identification framework combining various methods to screen potential anti-COVID-19 drugs, including remdesivir and ribavirin. The results showed that VDA-RWLRLS demonstrated superior performance and may contribute to preventing the transmission of COVID-19.
Background: A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. Methods: In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. Results: In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. Conclusions: Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.
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