RATIONAL DESIGN OF BIOACTIVE HYBRID PEPTIDES FOR THERAPEUTIC APPLICATION

• Grant number: 22/13011-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Effective date (Start): October 01, 2023
• Effective date (End): June 24, 2024
• Field of knowledge: Biological Sciences - Biochemistry
• Principal Investigator: Katia da Conceição
• Grantee: Bruna Vitória Vicente Scavassa
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil

Abstract

Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a multitude of pathologies. Unlike small organic compounds, peptides exhibit a high degree of specificity avoiding off-target side effects and a relatively low degree of toxicity. In this context, a successful strategy for the design and optimization of peptides/peptidomimetics would be the combination of different computational approaches ranging from structural bioinformatics to atomistic simulations. In the present project, new peptides with potential broad-spectrum activity against microorganisms and cellular penetration activity will be designed from the sequences of cecropins and cathepsins. A pipeline driven by computational databases for analysis and rational modification will be developed and used based on established principles of PAMs and PPCs activity, such as charge, hydrophobicity, length, amino acid frequency, charge density and hydrophobic moments. The designed peptides will be examined for immunogenicity, toxicity, allergenicity and pharmacokinetic parameters (ADMET) using different web servers. Antiviral and anticancer potential will be predicted using the Meta-iAVP and AVPred web servers. Finally, the secondary structures will be provided by the PEP-FOLD3 and PROCHECK servers. The results obtained in this study will be important to reinforce the applicability of machine learning models and evolutionary algorithms to design and optimize new synthetic bioactive peptides with reduced toxicity and increased cell type selectivity.