Computational drug discovery on canonical and alternative sites of influenza polymerases

• Grant number: 25/19549-0
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: February 01, 2026
• End date: January 31, 2027
• Field of knowledge: Biological Sciences - Biophysics - Molecular Biophysics
• Principal Investigator: Kathia Maria Honorio
• Grantee: Camila Fonseca Amorim da Silva
• Supervisor: Thales Kronenberger
• Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: Eberhard Karls Universität Tübingen, Germany
• Associated to the scholarship: 25/02517-8 - Evaluation of algorithms and tools for the design of drug candidates: a case study on H5N1 and WNV viruses, BP.DD

Abstract

Current outbreaks of the highly-pathogenic avian influenza A H5N1 virus highlight the need for new antiviral therapies and pandemic preparedness. It is noteworthy that the viral influenza RNA polymerase comprises a valuable target for drug discovery. Its cap-binding domain on the PB2 subunit (PB2cap) is conserved among influenza viruses and involved in the "cap-snatching" mechanism that precedes the transcription of viral RNA. However, PB2 presents multiple druggable sites beyond its canonical site. Indeed, our group in collaboration with Prof. Wrenger within the Tematico 2023/07746-0 recently identified druggable pockets by co-crystallization of the PB2cap with a capped RNA fragment. As a preliminary work, virtual screening (VS) of a small commercially-available compound library, followed by molecular dynamics (MD) simulations supported the selection of candidate inhibitors targeting PB2cap to be further evaluated experimentally. The current BEPE project intends to scale up the VS process (from ~5 million compounds) to obtain the baseline data for training a machine-learning (ML) docking model, which will be used to predict the binding affinity of molecules of an ultra-large scale library (~6 billion compounds). This approach represents the state-of-the-art of a computational pipeline to identify novel inhibitors and evaluate their selectivity against viral vs. human proteins. Moreover, hit compounds will be screened against distinct sites and influenza subtypes. MD simulations will characterize their binding modes, key interactions with relevant conserved residues and stability, followed by antiviral activity experiments and computer-aided structure-activity optimization. Therefore, the proposed drug discovery pipeline intends to be robust against targets of established and (re)emerging influenza strains. (AU)