ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
1/1
0/0

In Silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

preprint
submitted on 02.06.2020 and posted on 03.06.2020 by Andrés López-Cortés, Patricia Guevara-Ramírez, Nikolaos C Kyriakidis, Carlos Barba-Ostria, Ángela León Cáceres, Santiago Guerrero, Cristian Robert Munteanu, Eduardo Tejera, Esteban Ortiz-Prado, Doménica Cevallos-Robalino, Ana María Gómez J., Katherine Simbaña-Rivera, Adriana Granizo-Martínez, Gabriela Pérez-M, Jennyfer M. García-Cárdenas, Ana Karina Zambrano, Silvana Moreno, Yunierkis Pérez-Castillo, Alejandro Cabrera-Andrade, Lourdes Puig San Andrés, Carolina Proaño-Castro, Jhomayra Bautista, Nelson Varela, Luis Abel Quiñones, Cesar Paz-y-Miño
There is pressing urgency to better understand the immunological underpinnings of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in order to identify potential therapeutic targets and drugs that allow treating patients effectively. To fill in this gap, we performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. As results, the high-confidence protein interactome network was conformed by 1,588 nodes between immune system proteins and human proteins physically associated with SARS-CoV-2. Subsequently, we screened all these nodes in ACE2 and TMPRSS2 co-expressing cells according to the Alexandria Project, finding 75 potential therapeutic targets significantly overexpressed (Z score > 2) in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs for 25 of the 75 aforementioned proteins. On one hand, we obtained 45 approved drugs, 16 compounds under investigation, and 35 experimental compounds with the highest area under the receiver operating characteristic (AUROCs) for 15 immune system proteins. On the other hand, we obtained 4 approved drugs, 9 compounds under investigation, and 16 experimental compounds with the highest multi-target affinities for 9 immune system proteins. In conclusion, computational structure-based drug discovery focused on immune system proteins is imperative to select potential drugs that, after being effectively analyzed in cell lines and clinical trials, these can be considered for treatment of complex symptoms of COVID-19 patients, and for co-therapies with drugs directly targeting SARS-CoV-2.

History

Email Address of Submitting Author

aalc84@gmail.com

Institution

Universidad UTE

Country

Ecuador

ORCID For Submitting Author

0000-0003-1503-1929

Declaration of Conflict of Interest

The authors declare no competing interests.

Licence

Exports