| Autor: |
de Abreu AP; Laboratory of Bioinformatics and Systems, Department of Computer Science, Institute of Exact Sciences, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil., Carvalho FC; Laboratory of Bioinformatics and Systems, Department of Computer Science, Institute of Exact Sciences, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil., Mariano D; Laboratory of Bioinformatics and Systems, Department of Computer Science, Institute of Exact Sciences, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil., Bastos LL; Laboratory of Bioinformatics and Systems, Department of Computer Science, Institute of Exact Sciences, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil., Silva JRP; Department of Biochemistry and Immunology, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil., de Oliveira LM; Laboratory of Bioinformatics and Systems, Department of Computer Science, Institute of Exact Sciences, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil., de Melo-Minardi RC; Laboratory of Bioinformatics and Systems, Department of Computer Science, Institute of Exact Sciences, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil., Sabino AP; Laboratory of Clinical and Experimental Hematology, Clinical and Toxicological Analysis Department, Faculty of Pharmacy, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil. |
| Abstrakt: |
SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides. |
