Sequeiros-Borja C, Surpeta B, Marchlewski I,  Brezovsky J, 2022: Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations. MethodsX (in press – DOI: 10.1016/j.mex.2022.101968). full text

Nowadays, molecular dynamics (MD) simulations of proteins with hundreds of thousands of snapshots are commonly produced using modern GPUs. However, due to the abundance of data, analyzing transport tunnels present in the internal voids of these molecules, in all generated snapshots, has become challenging. Here, we propose to combine the usage of CAVER3, the most popular tool for tunnel calculation, and the TransportTools Python3 library into a divide-and-conquer approach to speed up tunnel calculation and reduce the hardware resources required to analyze long MD simulations in detail. By slicing an MD trajectory into smaller pieces and performing a tunnel analysis on these pieces by CAVER3, the runtime and resources are considerably reduced. Next, the TransportTools library merges the smaller pieces and gives an overall view of the tunnel network for the complete trajectory without quality loss.

Surpeta B, Grulich M, Palyzová A, Marešová H, Brezovsky J, 2022: Common Dynamic Determinants Govern Quorum Quenching Activity in N-Terminal Serine Hydrolases. ACS Catalysis 12: 6359-6374. full text

Growing concerns about microbial antibiotic resistance have motivated extensive research into ways of overcoming antibiotic resistance. Quorum quenching (QQ) processes disrupt bacterial communication via quorum sensing, which enables bacteria to sense the surrounding bacterial cell density and markedly affects their virulence. Due to its indirect mode of action, QQ is believed to exert limited pressure on essential bacterial functions and may thus avoid inducing resistance. Although many enzymes display QQ activity against various bacterial signaling molecules, their mechanisms of action are poorly understood, limiting their potential optimization as QQ agents. Here, we evaluate the capacity of three N-terminal serine hydrolases to degrade N-acyl-homoserine lactones (HSLs) that serve as signaling compounds for Gram-negative bacteria. Using molecular dynamics (MD) simulations of the free enzymes and their complexes with two signaling molecules of different lengths, followed by quantum mechanics/molecular mechanics MD simulations of two catalytic steps, we clarify the molecular processes underpinning their QQ activity. We conclude that all three enzymes degrade HSLs via similar reaction mechanisms. Moreover, we experimentally confirmed the activity of two penicillin G acylases from Escherichia coli (ecPGA) and Achromobacter spp. (aPGA), adding these industrially optimized enzymes to the QQ toolbox. We also observed substrate-dependent differences in the catalytic actions of these enzymes, arising primarily from the distinct structures of their acyl-binding cavities and the dynamics of their molecular gates. As a consequence, the first reaction step catalyzed by ecPGA with a longer substrate had an elevated energy barrier compared to its complex with a shorter substrate because its shallow acyl-binding site could not accommodate a productive substrate-binding configuration of the former one. Conversely, aPGA in complex with the shorter substrate exhibited unfavorable energetics in the first step, while the longer substrate was penalized in the second step, both due to the dynamics of the residues gating the acyl-binding cavity entrance. Finally, the energy barriers of the second reaction step catalyzed by Pseudomonas aeruginosa acyl-homoserine lactone acylase with both substrates were higher than in the other two enzymes due to the unique positioning of Arg297β in this enzyme. The discovery of these dynamic determinants will guide future efforts to design robust QQ agents capable of selectively controlling virulence in resistant bacterial species.