Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein–protein and protein–nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Summary Information regarding pathways through voids in biomolecules and their roles in ligand transport is critical to our understanding of the function of many biomolecules. Recently, the advent of high-throughput molecular dynamics simulations has enabled the study of these pathways, and of rare transport events. However, the scale and intricacy of the data produced requires dedicated tools in order to conduct analyses efficiently and without excessive demand on users. To fill this gap, we developed the TransportTools, which allows the investigation of pathways and their utilization across large, simulated datasets. TransportTools also facilitates the development of custom-made analyses. Availability and Implementation TransportTools is implemented in Python3 and distributed as pip and conda packages. The source code is available at https://github.com/labbit-eu/transport_tools. Supplementary information Supplementary data are available at Bioinformatics online.

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

Information regarding pathways through voids in biomolecules and their roles in ligand transport is critical to our understanding of the function of many biomolecules. Recently, the advent of high-throughput molecular dynamics simulations has enabled the study of these pathways, and of rare transport events. However, the scale and intricacy of the data produced requires dedicated tools in order to conduct analyses efficiently and without excessive demand on users. To fill this gap, we developed the TransportTools, which allows the investigation of pathways and their utilization across large, simulated datasets. TransportTools also facilitates the development of custom-made analyses. TransportTools is implemented in Python3 and distributed as pip and conda packages. The source code is available at https://github.com/labbit-eu/transport_tools.

No abstract

Due to the alarming global crisis of the growing microbial antibiotic resistance, investigation of alternative strategies to combat this issue has gained considerable momentum in the recent decade. A quorum quenching (QQ) process disrupts bacterial communication through so-called quorum sensing that enables bacteria to sense the cell density in the surrounding environment. Due to its indirect mode of action, QQ is believed to exert limited pressure on essential bacterial functions and consequently avoid inducing resistance. Although many enzymes are known to display the QQ activity towards various molecules used for bacterial signaling, the in-depth mechanism of their action is not well understood hampering their possible optimization for such exploitation. In this study, we compare the potential of three members of N-terminal serine hydrolases to degrade N-acyl homoserine lactones--signaling compounds employed by Gram-negative bacteria. Using molecular dynamics simulation of free enzymes and their complexes with two signaling molecules of different lengths, followed by quantum mechanics/molecular mechanics molecular dynamics simulation of their initial catalytic steps, we explored molecular details behind their QQ activities. We observed that all three enzymes were able to degrade bacterial signaling molecules following an analogous reaction mechanism. For the two investigated penicillin G acylases from Escherichia coli (ecPGA) and Achromobacter spp. (aPGA), we confirmed their putative activities experimentally hereby extending the set of known quorum quenching enzymes by these representatives of biotechnologically well-optimized enzymes. Interestingly, we detected enzyme- and substrate-depended differences among the three enzymes caused primarily by the distinct structure and dynamics of acyl-binding cavities. As a consequence, the first reaction step catalyzed by ecPGA with a longer substrate exhibited an elevated energy barrier due to a too shallow acyl-binding site incapable of accomodating this molecule in a required configuration. Conversely, unfavorable energetics on both reaction steps were observed for aPGA in complex with both substrates, conditioned primarily by the increased dynamics of the residues gating the entrance to the acyl-binding cavity. 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 distinct positioning of Arg297β. These discovered dynamic determinants constitute valuable guidance for further research towards designing robust QQ agents capable of selectively controlling the virulence of resistant bacteria species.

The rapid increase in antibiotic-resistant bacteria and the inability to provide new generations of potent antimicrobials necessitates the search for new, unconventional solutions. Methods based on targeting bacterial communication induced by signaling molecules, known as quorum sensing, are gaining increasing interest. Quorum quenching (QQ), as the process of interrupting this communication is called, can be achieved by enzymatic degradation of signaling molecules and represents a promising solution as it limits the expression of genes responsible for virulence, biofilm formation, and drug resistance. It is also believed to circumvent common resistance mechanisms. Therefore, enzymes with QQ activity represent potential next-generation antimicrobial agents for use in medicine, industry, and other areas of life. This work focuses on a biotechnologically optimized penicillin G acylase from Escherichia coli (ecPGA), for which primary QQ activity for smaller signaling molecules was recently confirmed. Herein, we introduced triple-point mutations within the binding pocket by an ensemble-based design aimed at modulating the pocket structure and the dynamics of its entrance gates. Next, we proposed a computational workflow to select promising combinations for further modeling. We selected three candidates for experimental evaluation using molecular dynamics simulations of the constructs with six different, biologically relevant signaling molecules. These comprised (i) the VAF variant with enhanced activity towards the medium-sized ligands like the signaling molecule of Burkholderia cenocepacia, C08-HSL (N-octanoyl-L-homoserine lactone); (ii) the YAF variant preferring longer substrates like the signaling compound of pathogenic Vibrio species, C10-HSL (N-decanoyl-L-homoserine lactone); and finally (iii) the MSF variant with improved efficacy for the longest substrate, C12-3O-HSL (N-3-oxo-dodecanoyl-L-homoserine lactone), the signaling molecule of Pseudomonas aeruginosa. In-depth analyses of these engineered variants revealed modulated topology and dynamics of the binding pockets. While we could consistently expand the pockets in these variants, the reactive binding of longer substrates became limited, due to either overly promoted dynamics of the pocket in the VAF variant or an overstabilized pocket in the MSF variant. In summary, we demonstrated the designability of ecPGA for improved QQ and provided insights into the role of adequately modulated pocket dynamics for the activity. Such knowledge, together with the methodology developed for filtering and scoring large datasets of potential variants that reflect the outcomes of our biochemical assays, may provide a suitable toolbox for future exploration and design of tailored QQ acylases toward particular signaling molecules, making them viable antimicrobial agents.

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.