Deep-learning based bioactive therapeutic peptides generation and screening (preprint)

Many bioactive peptides demonstrated therapeutic effects over-complicated diseases, such as antiviral, antibacterial, anticancer, etc. Similar to the generating de novo chemical compounds, with the accumulated bioactive peptides as a training set, it is possible to generate abundant potential bioactive peptides with deep learning. Such techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. However, there are very few deep learning-based peptide generating models. Here, we have created an LSTM model (named LSTM_Pep) to generate de novo peptides and finetune learning to generate de novo peptides with certain potential therapeutic effects. Remarkably, the Antimicrobial Peptide Database has fully utilized in this work to generate various kinds of potential active de novo peptide. We proposed a pipeline for screening those generated peptides for a given target, and use Main protease of SARS-COV-2 as concept-of-proof example. Moreover, we have developed a deep learning-based protein-peptide prediction model (named DeepPep) for fast screening the generated peptides for the given targets. Together with the generating model, we have demonstrated iteratively finetune training, generating and screening peptides for higher predicted binding affinity peptides can be achieved. Our work sheds light on to the development of deep learning-based methods and pipelines to effectively generating and getting bioactive peptides with a specific therapeutic effect, and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study (preprint)

Background: Secondary waves of COVID-19 loom in many countries as strict physical distancing measures have been lifted. Since megacities have been hardest hit by the disease, science-based guidelines of non-pharmaceutical interventions are still in need for post-epidemic management in ‘business as usual’ cities before vaccines are widely available. This study aims to investigate the combined effects of contact tracing, mask wearing, and prompt testing on minimizing the risk of next COVID-19 waves in megacities. Methods: We integrated 5·8 million mobile phone users’ trajectory records into a spatially explicit individual-based model for simulating COVID-19 spread among 4·5 million households, 230 thousand workplaces (including schools), and other public places in 0.6 million buildings in Shenzhen city, China, which has been gradually reopened. Government interventions were incorporated to reconstruct the actual course of the 1st wave epidemic. After validated by empirical data, the model was used to assess the probability of resurgences if sporadic cases occurred in a fully reopened city under different scenarios of contact tracing settings (household, work, school, and public place), mask use, and test-seeking behavior along with receding public vigilance. Findings: Our model well predicted the spatiotemporal dynamics of the 1st wave epidemic in Shenzhen, by age distribution of symptomatic cases, and household secondary attack rate (11·02%). After city reopens, our results show a 50% chance or less of suppressing disease resurgence if not implementing contact tracing. Tracing household contacts, in combination with mandatory (100% compliance) mask use and prompt testing could limit the probability of next outbreak under 5%. If contact tracing can be expanded to work/class group members, the public compliance of masking and testing can be relaxed to 80% and 40%, respectively, to achieve the same suppression target. Further scaled-up contact tracing that includes casual contacts can suppress resurgences with a low compliance to mask use (40%) and prompt testing (20%-40%). Interpretation: To minimize the risk of resurgence in a reopened city, the local government is expected to spare no efforts to trace close contacts in household, workplace and school for a confirmed case. The authorities should promote mask use in a public space and encourage people with COVID-19-like symptoms to testing within two days after illness onset, along with measures such as sick leave compensation and extensive temperature screening in public places.Funding Statement: National Scientific Foundation of China, R & D project of key areas in Guangdong Province, Bill & Melinda Gates Foundation, Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyDeclaration of Interests: We declare no competing interests.

Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study (preprint)

Background: As COVID-19 resurges in many countries, science-based guidelines of non-pharmaceutical interventions are still in need for post-epidemic management in ‘business as usual’ cities before vaccines are widely available. This study aims to investigate the combined effects of contact tracing, mask wearing, and prompt testing on minimizing the risk of next COVID-19 waves caused by sporadic outbreaks in megacities.Methods: We integrated large-scale mobile phone tracking data into a spatially explicit individual-based model to simulate COVID-19 spread among 11.2 million individuals in Shenzhen City, China. Government interventions were incorporated to reconstruct the actual course of the 1st wave epidemic. After validated by empirical data, the model was used to assess the probability of resurgences if sporadic cases occurred in a fully reopened city under different scenarios of contact tracing settings (household, work, school, and public place), mask use, and test-seeking behavior along with receding public vigilance.Results: Our model well predicted the spatiotemporal dynamics of the 1st wave epidemic in Shenzhen, by age distribution of symptomatic cases, and household secondary attack rate (11·02%). After city reopens, our results show a 50% chance or less of suppressing disease resurgence if not implementing contact tracing. Tracing household contacts, in combination with mandatory (100% compliance) mask use and prompt testing could limit the probability of next outbreak under 5%. If contact tracing can be expanded to work/class group members, the public compliance of masking and testing can be relaxed to 80% and 40%, respectively, to achieve the same suppression target. Further scaled-up contact tracing that includes casual contacts can suppress resurgences with a low compliance to mask use (40%) and prompt testing (20%-40%). Conclusions: To minimize the risk of resurgence in a reopened city, the local government is expected to spare no efforts to trace close contacts in household, workplace and school for a confirmed case. The authorities should promote mask use in a public space and encourage people with COVID-19-like symptoms to testing within two days after illness onset, along with measures such as sick leave compensation and extensive temperature screening in public places.

FDA-approved Pralatrexate identified by virtual drug screening inhibits SARS-CoV-2 replication in vitro (preprint)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic poses serious threats to the global public health and leads to an unprecedented worldwide crisis. Unfortunately, no effective drugs or vaccines are available till now. Since the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a promising therapeutic target, a deep learning and molecular simulation based hybrid drug screening procedure was proposed and applied to identify potential drug candidates targeting RdRp from 1906 approved drugs. Among the four selected FDA-approved drug candidates, Pralatrexate and Azithromycin were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 0.008µM and 9.453 µM, respectively. For the first time, our study discovered that Pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than Remdesivir within the same experimental conditions. The paper demonstrates the feasibility of accurate virtual drug screening for inhibitors of SARS-CoV-2 and provides potential therapeutic agents against COVID-19.

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov (preprint)

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.