Multiscale Modeling of the Bacterial Ribosome to Identify Potential Peptide Modulators and Their Allosteric Effects

Area of Science:

• Microbiology
• Computational Biology
• Drug Discovery

Background:

• The bacterial ribosome is a crucial target for antibiotics.
• Peptide-based modulation of ribosomal sites remains underexplored.
• Novel strategies are needed to combat antibiotic resistance.

Purpose of the Study:

• To develop and apply a computational pipeline for identifying peptide candidates targeting bacterial ribosomal sites.
• To explore functional and allosteric modulation of the ribosome using peptide inhibitors.
• To guide the rational design of novel peptide-based therapeutics against bacterial infections.

Main Methods:

• Integrated computational pipeline: SiteMap binding-site detection, consensus docking (Glide, rDock), all-atom truncated molecular dynamics (MD), and coarse-grained MD (CGMD) simulations.
• Virtual screening of peptide candidates against four *E. coli* ribosomal sites (decoding center, peptidyl transferase center, 30S pocket, B8 bridge).
• Analysis of peptide-ribosome interactions, dynamic cross-correlation, and residue interaction networks.

Main Results:

• Identified consensus-selected peptides with hallmark contacts of known inhibitors (viomycin, dalfopristin).
• Delineated site-specific scaffolds for prioritizing ribosomal affinity and guiding therapeutic design.
• Discovered CycPeptMPDB_2508 as a versatile lead peptide with broad binding affinity across ribosomal sites.
• CGMD simulations revealed coupled motions and identified hub residues potentially involved in allosteric pathways.

Conclusions:

• The developed computational framework provides a concise and testable approach for ribosome-targeted peptide discovery.
• This study represents the first virtual screening of ribosome-peptide complexes using the viparr module for truncated systems.
• The findings nominate CycPeptMPDB_2508 as a versatile core for antimicrobial peptide development and highlight potential allosteric modulation pathways.