Biology subjects

Surpeta, B.

Publications and source records attributed to Surpeta, B..

6 recordsLinked to original sources

bioRxiv2024

Benchmarking coarse-grained simulation methods for investigation of transport tunnels in enzymes

Enzymes are pivotal to numerous biological processes, often featuring buried active sites linked to the surrounding solvent through intricate and dynamic tunnels. These tunnels are vital for facilitating substrate access, enabling product release, and regulating solvent exchange, which collectively influence enzymatic function and efficiency. Consequently, knowledge of tunnels is key for a holistic understanding of the effect of mutations as well as predicting drug residence times. Unfortunately, most transport tunnels are transient, i.e., equipped by molecular gates, rendering their opening a rare event that is often notoriously hard to study with conventional molecular dynamics simulations. To overcome the sampling limitation of such simulations, this study investigated the efficacy of three different coarse-grained (CG) molecular dynamics simulation methods for inferring enzyme tunnel structure and dynamics. Here, we covered the Martini and SIRAH models with different restraint protocols providing stability to CG proteins while to some extent biasing the sampling towards a reference structure. By contrasting CG results with all-atom simulations, we benchmarked the ability of CG methods to replicate ensemble characteristics of complex tunnel networks in haloalkane dehalogenase LinB and two of its mutants with engineered tunnel networks. The assessed tunnel parameters are essential for prioritizing functionally relevant tunnels and delineating the effect of mutations on transport tunnels. Our findings reveal that while CG methods significantly enhance the efficiency of tunnel analyses, some of them, like Martini with Elastic network restraints, were limited in recapitulating all-atom tunnel dynamics due to the structural bias applied. In contrast, the Martini G[o] model even captured the intricate details of mutation perturbing tunnel dynamics. All studied CG methods performed well in capturing the geometry of tunnel ensembles in line with all-atom simulations. Additionally, the wider applicability of CG methods was verified by analyzing tunnel networks of nine enzymes from different combinations of structural and functional classes, demonstrating their potential to uncover new tunnel phenomena and validate their utility in broader biological and functional contexts. This comprehensive evaluation underscores the strengths and constraints of CG simulations in capturing enzyme tunnels and benefiting from their computational speed for studying huge datasets of enzymes. These insights are valuable for enzyme engineering, drug design, and understanding enzyme function while benefitting from the efficiency of coarse-grained models.

bioinformatics↗

bioRxiv2024

Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics

Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules to and from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging because they are typically transient. Computational methods such as molecular dynamics (MD) simulations have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer timescales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multi-step catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with potential for drug development, precision medicine, and rational protein engineering.

bioinformatics↗

bioRxiv2023

Incorporating prior knowledge to seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

Given that most proteins have buried active sites, protein tunnels or channels play a crucial role in mitigating the transport of small molecules to the buried cavity for enzymatic catalysis. Tunnels can critically modulate the biological process of protein-ligand recognition. Various molecular dynamics methods have been developed for exploring and exploiting the protein-ligand conformational space to extract high-resolution details of the binding processes, one of the most recent represented by energetically unbiased high-throughput adaptive sampling simulations. The current study systematically contrasts the role of integrating prior knowledge while generating useful initial protein-ligand configurations, called seeds, for these simulations. Using a non-trivial system of haloalkane dehalogenase mutant with multiple transport tunnels leading to a deeply buried active site, these simulations were employed to derive kinetic models describing the process of association and dissociation of the substrate molecule. The more knowledge-based seed generation enabled high-throughput simulations that could more consistently capture the entire transport process, effectively explore the complex network of transport tunnels, and predict equilibrium dissociation constants, koff/kon, on the same order of magnitude as experimental measurements. Overall, the infusion of more knowledge into the initial seeds of adaptive sampling simulations could render analyses of transport mechanisms in enzymes more consistent even for very complex biomolecular systems, thereby promoting the rational design of enzymes with buried active sites and drug development efforts.

biophysics↗

bioRxiv2023

Rational engineering of binding pocket's structure and dynamics in penicillin G acylase for selective degradation of bacterial signaling molecules

The rapid rise of antibiotic-resistant bacteria necessitates the search for alternative, unconventional solutions, such as targeting bacterial communication. Signal disruption can be achieved by enzymatic degradation of signaling compounds, reducing the expression of genes responsible for virulence, biofilm formation, and drug resistance while evading common resistance mechanisms. Therefore, enzymes with such activity have considerable potential as antimicrobial agents for medicine, industry, and other areas of life. Here, we designed molecular gates that control the binding site of penicillin G acylase to shift its preference from native substrate to signaling molecules. Using an ensemble-based design, three variants carrying triple-point mutations were proposed and experimentally characterized. Integrated inference from biochemical and computational analyses demonstrated that these three variants had markedly reduced activity towards penicillin and each preferred specific signal molecules of different pathogenic bacteria, exhibiting up to three orders of magnitude shifts in substrate specificity. Curiously, while we could consistently expand the pockets in these mutants, the reactive binding of larger substrates was limited, either by overpromoting or overstabilizing the pocket dynamics. Overall, we demonstrated the designability of this acylase for signal disruption and provided insights into the role of appropriately modulated pocket dynamics for such a function. The improved mutants, the knowledge gained, and the computational workflow developed to prioritize large datasets of promising variants may provide a suitable toolbox for future exploration and design of enzymes tailored to disrupt specific signaling pathways as viable antimicrobial agents.

biochemistry↗

bioRxiv2022

Dynamic Determinants of Quorum Quenching Mechanism Shared among N-terminal Serine Hydrolases

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 that serve as signaling compounds for Gram-negative bacteria. Using molecular dynamics simulations of the free enzymes and their complexes with two signaling molecules of different lengths, followed by quantum mechanics/molecular mechanics molecular dynamics simulations of their initial catalytic steps, we clarify the molecular processes underpinning their QQ activity. We conclude that all three enzymes degrade bacterial signaling molecules 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 biotechnologically well-optimized enzymes to the QQ toolbox. We also observed enzyme- and 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 because its shallow acyl binding site could not accommodate a productive substrate-binding configuration. Conversely, aPGA in complex with both substrates exhibited unfavorable energetics in both reaction steps 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 Arg297{beta} 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.

biochemistry↗

bioRxiv2021

TransportTools: a library for high-throughput analyses of internal voids in biomolecules and ligand transport through them

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.

bioinformatics↗