Characterization and Dynamic Studies of Drugs for Combating Multi-Drug Resistance

Abstract

Drug resistance within a wide range of infectious agents has become a global menace, handling of which through the application of computational chemistry and molecular mechanics techniques is one of the possible solutions. Advancements in multidisciplinary field of computational chemistry have paved the way for computational scientists to take lead in exploring structural and dynamical attributes of various biological systems that play a significant role in addressing drug resistance. Furthermore, the contemporary headway in computational biology is primarily attributed to massive growth in data, power and its mammoth ability to predict multiscale modeling of complex biological phenomena. Subsequently, the improvement in computer speed and performance has permitted integration of advanced computational techniques such as hybrid quantum mechanical and molecular mechanical (QM/MM) simulations to elucidate intricate details of complex biological processes such as enzyme catalysis, protein ligand interactions, and binding and unbinding dynamics of a protein. In line with the aim to materialize classical molecular dynamics and QM/MM simulations in response to drug resistance, this dissertation outlines the enormous potential of these methods particularly in connection with nosocomial infections. The first section of thesis is focused on the infections controlled by quorum sensing (QS), biofilm dispersion and transcription factors within Pseudomonas aeruginosa. The state-of-the-art QM/MM methods are employed to investigate the intricate details of covalent inhibition of target proteins that interfere with the defense mechanism of P. aeruginosa. Umbrella sampling (US) simulations at the DFTB3/MM level of theory are employed to propose a reaction mechanism with four potential covalent inhibitors. The potential of mean force reveals the direct role of intrinsic reactivity of inhibitors, for example inhibitors with methyl oxo-enoate warhead activate carbonyl samples in the first step of a reaction, which shed light on the significance of proton transfer indispensable for full inhibition. Whereas the nitrile inhibitor undergoes a stepwise mechanism with a small proton-transfer energy barrier and 1 Characterization and Dynamic Studies of Drugs for Combating Multi-Drug Resistance SUMMARY lower imaginary frequencies that materialize instantly after nucleophilic attack. Furthermore, to unveil the molecular determinants of respective binding affinities, transition states along the reaction path are optimized and characterized with B3LYP6-31+G(d,p). This study serves as a preamble to add variation in the proposed structures and unveils the impact of functional groups lying in warheads that modulate the kinetics of proton transfer, which will certainly aid to design more selective and efficient irreversible inhibitors for bacterial transcription factors. Under the same domain of addressing MDR through P. aeruginosa, a quorum sensing (QS) signal molecule is targeted with the primary objective to identify and propose competitive inhibitors for multiple sites within the same binding tunnel. This study not only adapts a broad-spectrum strategy for the lucid design of small molecule modulators but also provides novel allosteric inhibitors of the enzyme in hand. The term “competo-allosteric” is coined for the first time in this study, which entails the presence of competo-allosteric site in the same binding tunnel as the normal site competing for binding to similar residues. Another contemplating resistance emerging nowadays is parasitic resistance which is another main focus of this work. This domain enlightens rapidly emerging drug resistance in leishmaniasis particularly to available treatment of antimonial (Sb) drugs and propose the computational design of alternate formulations encompassing relatively less explored functional metalloids. Large-scale atomistic simulations particularly with Au(I), Ag, Bi(V), and Sb(V) metalloids pose a major challenge to elucidate their molecular mechanism due to the absence of force field parameters. This study quantum mechanically (QM) derives force field parameterization of heteroleptic triorganobismuth(V) biscarboxylates. Findings from force field parameterization are simultaneously supported by the antileishmanial experimental study, results of which prove to be a preamble for successive QM treatment. Two organo-bismuth(V) carboxylates are modeled, which are optimized and paramtrized along the famous pentavalent antimonial drug: meglumine antimoniate using QM original Seminarian methods with SBKJC basis set coupled with effective core potential (ECP) level of theory. Findings underpin the role of bidentate chelating 2 Characterization and Dynamic Studies of Drugs for Combating Multi-Drug Resistance SUMMARY behavior acquired by Bi(V) models on bond length that highlight the significance of OH group present at ortho position of sub-structure subsalicylate. This is preceded by atomistic simulations of bismuth and antimony containing compounds in complex with two enzymes, trypanothione synthetase-amidase (TSA) and trypanothione reductase (TR) to target the (T(SH)2) pathway at multiple points. MD simulations provide novel insights into the binding mechanism of both enzymes and highlight the role of active site residues in modulating ligand dynamics. Moreover, the presence of an ortho group in ligand is emphasized to facilitate interactions for higher inhibitory activity of enzymes. This preliminary generation of parameters specific to bismuth validated by simulations in replica will become a source of further in future computational and experimental research work to open avenues for newer and suitable drug targets. The final section of this work is categorically focused on Covid19 pandemic with the aim to identify appropriate choice of inhibitors to curb this deadly viral infection and bring to light alternative methods that would address the existing problem of drug resistance. In this respect, with the aim to contribute to scientific research community in the challenging times of Covid19 pandemic, this study pulled efforts to bring awareness to public as a matter of global dissemination that resorted to excessive use of herbal tea “Senna Makki” in treatment of Covid19. There has been a myth regarding the use of famous Senna Tea in treatment of Covid19, overdose of which led to diuresis and death of many patients unknowingly within the paraphernalia of this pandemic. Under this domain, the binding potential of chemical compounds of Senna in comparison with the experimentally tested active phytochemicals against SARS-CoV-2 protein targets are investigated. The entire set of phytochemicals from both the groups are subjected to 3D-QSAR modeling followed by MD simulations with multiple SARS-CoV-2 target proteins namely; the spike protein, helicase nsp13, RdRp nsp12, and 3-Chymotrypsin-Like Protease (3CLpro). Findings manifest the importance of hydrophobic substituents in chemical structures of potential inhibitors through cross-validation with the FDA-approved anti-3CLpro drugs. Noteworthy improvement in end-point binding free energies and pharmacokinetic profiles of active phytochemicals is perceived in comparison to the 3 Characterization and Dynamic Studies of Drugs for Combating Multi-Drug Resistance SUMMARY control drug, Vizimpro. However, Senna metabolites in comparison exhibit weak binding affinity, instability and toxicity against all drug targets. Findings contravene fallacious efficacy claims of Senna tea interventions circulating on electronic/social media as Covid19 cure and discredit the use of Senna tea as whole with all its chemical constituents in Covid19 treatment. This emphasizes the importance of well examined standardized data of the natural products in hand; thereby preventing unnecessary deaths under pandemic hit situations worldwide. Additionally, the comparative MD simulations analysis is carried out on SARS-Cov-2 Omicron variant and its highly transmissible sublineages that undergo mutations to mitigate antimicrobial resistance. This work is in connection with the viral architecture or changes in viral structural attributes due to mutations and propose their potential effects on stability and transmission of the virus. A thorough investigation on Omicron and other variants of concern (VoC) namely Alpha, Beta, Gamma, Delta, and Omicron reveal the role of hydration forces in mediating function and dynamics based on a stronger interplay between protein and solvent. Mutations of multiple hydrophobic residues into hydrophilic residues trigger concerted interactions with water leading to variations in charge distribution during MD simulations. Moreover, comparative analysis of interacting moieties characterize a large number of mutations lying at RBD into constrained, homologous and low affinity groups referred to as mutational drivers inferring that the probability of future mutations relies on the function of these residues. Furthermore, the computational findings reveal a significant difference in angular distances among variants of concern due 3 amino acid insertion (EPE) in Omicron variant that not only facilitates tight domain organization but also seems requisite for characterization of mutational processes. The outcome of this work signifies the possible relation between hydration forces, their impact on conformation and binding affinities, and viral fitness that will significantly aid in understanding dynamics of drug targets for Covid-19 countermeasures. The overall concurred outcome of this dissertation in reference to integration of QM/MM methods reaffirm role of computational modeling in yielding intricate 4 Characterization and Dynamic Studies of Drugs for Combating Multi-Drug Resistance SUMMARY details about processes like proton transfer, role of well-optimized coordination geometry of metal ions, and other important structural and dynamical attributes of biomolecules. These instrumental phenomena are of utmost medicinal importance that can impact ARM/drug resistance if applied in the context of biologically relevant enzymes.