COMPUTATIONAL STUDY OF THE BACTERIOCIN CONTRACTION MECHANISM

Antibiotic resistance is rapidly escalating, exacerbated by a stagnation in new antimicrobial development, posing severe public health challenges. This issue is particularly critical for Gram-negative bacteria, which have a highly impermeable outer membrane that limits the effectiveness of many antibiotics. The rise of resistance complicates treatment further. Bacteriocins offer a promising alternative for addressing resistant bacterial strains due to their specific microbicidal activity. They target and eliminate closely related bacterial strains. Bacteriocins homologous to bacteriophage tails undergo a huge conformational change during their action. This involves the contraction of an outer sheath, propelling an inner tube that penetrates the target cell membrane, leading to cell lysis and death. This mechanism highlights the potential of bacteriocins in combating antibiotic-resistant bacteria. To understand the molecular mechanisms involved, hybrid approaches were employed to predict the transition between pre- and post-contraction states. This strategy combined normal mode analysis (NMA) with molecular dynamics (MD) simulations. NMA predicts conformational changes in proteins, while MD simulations, specifically with excited normal modes (MDeNM), capture long time-scale conformational transitions by exciting normal modes through multi-replicate short simulations. This method allows efficient integration of slow (NMA) and anharmonic motions (conventional MD). Preliminary simulations identified normal modes describing the conformational transition between the functional states. By exciting these modes through MDeNM, several probable transition paths were observed. Future work will focus on detailed energy analyses to further evaluate this process.

This work was supported by: Nasc. Des. Cient. Tecnológico (CNPq), Fund. Oswaldo Cruz (Fiocruz), Fund. Coord. Pessoa Nível Superior (CAPES), Fund. Carlos Chagas Filho Amp. Research State of Rio de Janeiro (FAPERJ) and Prog. Inova Covid-19 (Geração de Conhecimento).