Computational Methodology for Discovery of Potential Inhibitory Peptides

Abstract

In 2020, a new pandemic caused by a coronavirus has impacted the economic and public health landscape on a global level. Named SARS-CoV-2, it causes COVID-19 and, in two years, has caused thousands of deaths. Among its viral particles, SARS-CoV-2 has an important structural protein called Spike (S), and its entry into human cells is mediated by an interaction between the Spike and the human receptor Angiotensin Converting Enzyme 2 (ACE2). This S/ACE2 binding depends on the cleavage of the Spike into three parts (S1, S2 and S2’) by host cell proteases. For this, the S protein undergoes a conformational change that exposes a cleavage site between the S1 and S2 domains, being initially cleaved by the Furin enzyme. The S2 part is cleaved by TMPRSS2 (Transmembrane Serine Protease II) to expose the fusion peptide, promoting endocytic entry of the virus. TMPRSS2 can be inhibited by clinically approved serine protease inhibitors, making it a promising target for the treatment of viral infections. Consequently, our objective was to look for peptides that weren’t described as inhibitors for SARS-CoV-2 but can be repositioned. In this paper, we propose a computational method to collect, filter, simulate protein-peptide interaction and identify the best hits based on the pattern of interactions. In addition to the main contribution of the paper that is the method, another contribution of this work is the proposal of candidate peptides.