Enumerating pathways of proton abstraction based on a spatial and electrostatic analysis of residues in the catalytic site

Mechanism of proton transfer in class A β-lactamase catalysis and inhibition by avibactam

Gram-negative bacteria expressing class A β-lactamases pose a serious health threat due to their ability to inactivate all β-lactam antibiotics. The acyl-enzyme intermediate is a central milestone in the hydrolysis reaction catalyzed by these enzymes. However, the protonation states of the catalytic residues in this complex have never been fully analyzed experimentally due to inherent difficulties. To help unravel the ambiguity surrounding class A β-lactamase catalysis, we have used ultrahigh-resolution X-ray crystallography and the recently approved β-lactamase inhibitor avibactam to trap the acyl-enzyme complex of class A β-lactamase CTX-M-14 at varying pHs. A 0.83-Å-resolution CTX-M-14 complex structure at pH 7.9 revealed a neutral state for both Lys73 and Glu166. Furthermore, the avibactam hydroxylamine-O-sulfonate group conformation varied according to pH, and this conformational switch appeared to correspond to a change in the Lys73 protonation state at low pH. In conjunction with computational analyses, our structures suggest that Lys73 has a perturbed acid dissociation constant (pK_a) compared with acyl-enzyme complexes with β-lactams, hindering its function to deprotonate Glu166 and the initiation of the deacylation reaction. Further NMR analysis demonstrated Lys73 pK_a to be ∼5.2 to 5.6. Together with previous ultrahigh-resolution crystal structures, these findings enable us to follow the proton transfer process of the entire acylation reaction and reveal the critical role of Lys73. They also shed light on the stability and reversibility of the avibactam carbamoyl acyl-enzyme complex, highlighting the effect of substrate functional groups in influencing the protonation states of catalytic residues and subsequently the progression of the reaction.

Computational insights of K1444N substitution in GAP-related domain of NF1 gene associated with neurofibromatosis type 1 disease: a molecular modeling and dynamics approach

The NF1 gene encodes for neurofibromin protein, which is ubiquitously expressed, but most highly in the central nervous system. Non-synonymous SNPs (nsSNPs) in the NF1 gene were found to be associated with Neurofibromatosis Type 1 disease, which is characterized by the growth of tumors along nerves in the skin, brain, and other parts of the body. In this study, we used several in silico predictions tools to analyze 16 nsSNPs in the RAS-GAP domain of neurofibromin, the K1444N (K1423N) mutation was predicted as the most pathogenic. The comparative molecular dynamic simulation (MDS; 50 ns) between the wild type and the K1444N (K1423N) mutant suggested a significant change in the electrostatic potential. In addition, the RMSD, RMSF, Rg, hydrogen bonds, and PCA analysis confirmed the loss of flexibility and increase in compactness of the mutant protein. Further, SASA analysis revealed exchange between hydrophobic and hydrophilic residues from the core of the RAS-GAP domain to the surface of the mutant domain, consistent with the secondary structure analysis that showed significant alteration in the mutant protein conformation. Our data concludes that the K1444N (K1423N) mutant lead to increasing the rigidity and compactness of the protein. This study provides evidence of the benefits of the computational tools in predicting the pathogenicity of genetic mutations and suggests the application of MDS and different in silico prediction tools for variant assessment and classification in genetic clinics.

A computational module assembled from different protease family motifs identifies PI PLC from Bacillus cereus as a putative prolyl peptidase with a serine protease scaffold

Proteolytic enzymes have evolved several mechanisms to cleave peptide bonds. These distinct types have been systematically categorized in the MEROPS database. While a BLAST search on these proteases identifies homologous proteins, sequence alignment methods often fail to identify relationships arising from convergent evolution, exon shuffling, and modular reuse of catalytic units. We have previously established a computational method to detect functions in proteins based on the spatial and electrostatic properties of the catalytic residues (CLASP). CLASP identified a promiscuous serine protease scaffold in alkaline phosphatases (AP) and a scaffold recognizing a β-lactam (imipenem) in a cold-active Vibrio AP. Subsequently, we defined a methodology to quantify promiscuous activities in a wide range of proteins. Here, we assemble a module which encapsulates the multifarious motifs used by protease families listed in the MEROPS database. Since APs and proteases are an integral component of outer membrane vesicles (OMV), we sought to query other OMV proteins, like phospholipase C (PLC), using this search module. Our analysis indicated that phosphoinositide-specific PLC from Bacillus cereus is a serine protease. This was validated by protease assays, mass spectrometry and by inhibition of the native phospholipase activity of PI-PLC by the well-known serine protease inhibitor AEBSF (IC50 = 0.018 mM). Edman degradation analysis linked the specificity of the protease activity to a proline in the amino terminal, suggesting that the PI-PLC is a prolyl peptidase. Thus, we propose a computational method of extending protein families based on the spatial and electrostatic congruence of active site residues.

A quantitative measure of electrostatic perturbation in holo and apo enzymes induced by structural changes

Biological pathways are subject to subtle manipulations that achieve a wide range of functional variation in differing physiological niches. In many instances, changes in the structure of an enzyme on ligand binding germinate electrostatic perturbations that form the basis of its changed catalytic or transcriptional efficiency. Computational methods that seek to gain insights into the electrostatic changes in enzymes require expertise to setup and computing prowess. In the current work, we present a fast, easy and reliable methodology to compute electrostatic perturbations induced by ligand binding (MEPP). The theoretical foundation of MEPP is the conserved electrostatic potential difference (EPD) in cognate pairs of active site residues in proteins with the same functionality. Previously, this invariance has been used to unravel promiscuous serine protease and metallo-β-lactamase scaffolds in alkaline phosphatases. Given that a similarity in EPD is significant, we expect differences in the EPD to be significant too. MEPP identifies residues or domains that undergo significant electrostatic perturbations, and also enumerates residue pairs that undergo significant polarity change. The gain in a certain polarity of a residue with respect to neighboring residues, or the reversal of polarity between two residues might indicate a change in the preferred ligand. The methodology of MEPP has been demonstrated on several enzymes that employ varying mechanisms to perform their roles. For example, we have attributed the change in polarity in residue pairs to be responsible for the loss of metal ion binding in fructose 1,6-bisphosphatases, and corroborated the pre-organized state of the active site of the enzyme with respect to functionally relevant changes in electric fields in ketosteroid isomerases.

An automated flow for directed evolution based on detection of promiscuous scaffolds using spatial and electrostatic properties of catalytic residues

The aspiration to mimic and accelerate natural evolution has fueled interest in directed evolution experiments, which endow or enhance functionality in enzymes. Barring a few de novo approaches, most methods take a template protein having the desired activity, known active site residues and structure, and proceed to select a target protein which has a pre-existing scaffold congruent to the template motif. Previously, we have established a computational method (CLASP) based on spatial and electrostatic properties to detect active sites, and a method to quantify promiscuity in proteins. We exploit the prospect of promiscuous active sites to serve as the starting point for directed evolution and present a method to select a target protein which possesses a significant partial match with the template scaffold (DECAAF). A library of partial motifs, constructed from the active site residues of the template protein, is used to rank a set of target proteins based on maximal significant matches with the partial motifs, and cull out the best candidate from the reduced set as the target protein. Considering the scenario where this ‘incubator’ protein lacks activity, we identify mutations in the target protein that will mirror the template motif by superimposing the target and template protein based on the partial match. Using this superimposition technique, we analyzed the less than expected gain of activity achieved by an attempt to induce β-lactamase activity in a penicillin binding protein (PBP) (PBP-A from T. elongatus), and attributed this to steric hindrance from neighboring residues. We also propose mutations in PBP-5 from E. coli, which does not have similar steric constraints. The flow details have been worked out in an example which aims to select a substitute protein for human neutrophil elastase, preferably related to grapevines, in a chimeric anti-microbial enzyme which bolsters the innate immune defense system of grapevines.