Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov

Version 1 : Received: 4 February 2020 / Approved: 5 February 2020 / Online: 5 February 2020 (10:59:09 CET)

A peer-reviewed article of this Preprint also exists.

Journal reference: Interdisciplinary Sciences: Computational Life Sciences 2020
DOI: 10.1007/s12539-020-00376-6

Abstract

A novel coronavirus called 2019-nCoV was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. 2019-nCoV spreads more rapidly than SARS-CoV. Unfortunately, there is no drug to combat the virus. It is of high significance to develop a drug that can combat the virus effectively before the situation gets worse. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In this paper, we first collected virus RNA sequences from the GISAID database, translated the RNA sequences into protein sequences, and built a protein 3D model using homology modeling. Coronavirus main protease is considered to be a major therapeutic target, thus this paper focused on drug screening based on the modeled 2019-nCov_main_protease structure. The deep learning based method DFCNN, developed by our group, can identify/rank the protein-ligand interactions with relatively high accuracy. DFCNN is capable of performing virtual screening quickly since no docking or molecular dynamic simulation is needed. DFCNN identifies potential drugs for 2019-nCoV protease by performing drug screening against 4 chemical compound databases. Also, we performed drug screening for all tripeptides against the binding site of 2019-nCov_main_protease since peptides often show better stability, more bio-availability and negligible immune responses. In the end, we provided the list of possible chemical ligands and peptide drugs for experimental validation.

Subject Areas

Coronavirus; Deep learning; Drug screening; homology modeling; main protease

Comments (3)

Comment 1
Received: 8 February 2020
Commenter: Quantao Sun
The commenter has declared there is no conflict of interests.
Comment: The author indicate that the new method doesn’t need molecular dynamics, which can save a lot of time, but how can the author guarantee the accuracy since it is generally believed MD is the most reliable process to achieve accuracy which sometime have to cost time ? Has this method been proved to have better performance against the molecular dynamic process ?
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Response 1 to Comment 1
Received: 10 February 2020
Commenter: Haiping Zhang
The commenter has declared there is no conflict of interests.
Comment: The deep learning based DFCNN is a data-driven model, which learns protein-ligand interaction from known binding and nonbinder data. It doesn't need protein-ligand conformation, hence save a lot of time. Actually, this kind of method is complementary to the traditional physical-chemical based method, such as docking, MD simulation. Usually, the DFCNN was applied to do the large virtual screening, and then the narrowed potential protein-ligand pairs can do the docking, and carry MD simulation to further check the binding stability and atomic interaction pattern, or even the binding free energy with techniques such as metadynamics. So, Yes, currently, the MD is usually more accurate and suitable to check the binding in the final step. It would be helpful to do further analysis in later step to narrow down the candidate list.
Comment 2
Received: 8 February 2020
Commenter: Quantao Sun
The commenter has declared there is no conflict of interests.
Comment: The author indicate that the new method doesn’t need molecular dynamics, which can save a lot of time, but how can the author guarantee the accuracy since it is generally believed MD is the most reliable process to achieve accuracy which sometime have to cost time ?
+ Respond to this comment

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