Protonation of the -lactam nitrogen is the trigger event in the catalytic action of class A -lactamases

Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data

Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.

Active-Site Protonation States in an Acyl-Enzyme Intermediate of a Class A β-Lactamase with a Monobactam Substrate

The monobactam antibiotic aztreonam is used to treat cystic fibrosis patients with chronic pulmonary infections colonized by Pseudomonas aeruginosa strains expressing CTX-M extended-spectrum β-lactamases. The protonation states of active-site residues that are responsible for hydrolysis have been determined previously for the apo form of a CTX-M β-lactamase but not for a monobactam acyl-enzyme intermediate. Here we used neutron and high-resolution X-ray crystallography to probe the mechanism by which CTX-M extended-spectrum β-lactamases hydrolyze monobactam antibiotics. In these first reported structures of a class A β-lactamase in an acyl-enzyme complex with aztreonam, we directly observed most of the hydrogen atoms (as deuterium) within the active site. Although Lys 234 is fully protonated in the acyl intermediate, we found that Lys 73 is neutral. These findings are consistent with Lys 73 being able to serve as a general base during the acylation part of the catalytic mechanism, as previously proposed.

Exploring the Mechanism of β-Lactam Ring Protonation in the Class A β-lactamase Acylation Mechanism Using Neutron and X-ray Crystallography

The catalytic mechanism of class A β-lactamases is often debated due in part to the large number of amino acids that interact with bound β-lactam substrates. The role and function of the conserved residue Lys 73 in the catalytic mechanism of class A type β-lactamase enzymes is still not well understood after decades of scientific research. To better elucidate the functions of this vital residue, we used both neutron and high-resolution X-ray diffraction to examine both the structures of the ligand free protein and the acyl-enzyme complex of perdeuterated E166A Toho-1 β-lactamase with the antibiotic cefotaxime. The E166A mutant lacks a critical glutamate residue that has a key role in the deacylation step of the catalytic mechanism, allowing the acyl-enzyme adduct to be captured for study. In our ligand free structures, Lys 73 is present in a single conformation, however in all of our acyl-enzyme structures, Lys 73 is present in two different conformations, in which one conformer is closer to Ser 70 while the other conformer is positioned closer to Ser 130, which supports the existence of a possible pathway by which proton transfer from Lys 73 to Ser 130 can occur. This and further clarifications of the role of Lys 73 in the acylation mechanism may facilitate the design of inhibitors that capitalize on the enzyme's native machinery.

Evolution of conformational dynamics determines the conversion of a promiscuous generalist into a specialist enzyme

β-Lactamases are produced by many modern bacteria as a mechanism of resistance toward β-lactam antibiotics, the most common antibiotics in use. β-Lactamases, however, are ancient enzymes that originated billions of years ago. Recently, proteins corresponding to 2- to 3-Gy-old Precambrian nodes in the evolution of Class A β-lactamases have been prepared and shown to be moderately efficient promiscuous catalysts, able to degrade a variety of antibiotics with catalytic efficiency levels similar to those of an average modern enzyme. Remarkably, there are few structural differences (in particular at the active-site regions) between the resurrected enzymes and a penicillin-specialist modern β-lactamase. Here, we propose that the ancestral promiscuity originates from conformational dynamics. We investigate the differences in conformational dynamics of the ancient and extant β-lactamases through MD simulations and quantify the contribution of each position to functionally related dynamics through Dynamic Flexibility Index. The modern TEM-1 lactamase shows a comparatively rigid active-site region, likely reflecting adaptation for efficient degradation of a specific substrate (penicillin), whereas enhanced deformability at the active-site neighborhood in the ancestral resurrected proteins likely accounts for the binding and subsequent degradation of antibiotic molecules of different size and shape. Clustering of the conformational dynamics on the basis of Principal Component Analysis is in agreement with the functional divergence, as the ancient β-lactamases cluster together, separated from their modern descendant. Finally, our analysis leads to testable predictions, as sites of potential relevance for the evolution of dynamics are identified and mutations at those sites are expected to alter substrate-specificity.

Quantum.Ligand.Dock: protein-ligand docking with quantum entanglement refinement on a GPU system

Quantum.Ligand.Dock (protein-ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein-ligand interactions via high-performance docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of docking partners. On the other hand, Quantum.Ligand.Dock is the the only docking server offering such a subtle supplement to protein docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments-the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein-ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.