3D-QSAR, Molecular Docking and Molecular Dynamics Simulation of Pseudomonas aeruginosa LpxC Inhibitors

S288T mutation altering MmpL3 periplasmic domain channel and H-bond network: a novel dual drug resistance mechanism

CONTEXT

Mycobacterial membrane proteins Large 3 (MmpL3) is responsible for the transport of mycobacterial acids out of cell membrane to form cell wall, which is essential for the survival of Mycobacterium tuberculosis (Mtb) and has become a potent anti-tuberculosis target. SQ109 is an ethambutol (EMB) analogue, as a novel anti-tuberculosis drug, can effectively inhibit MmpL3, and has completed phase 2b-3 clinical trials. Drug resistance has always been the bottleneck problem in clinical treatment of tuberculosis. The S288T mutant of MmpL3 shows significant resistance to the inhibitor SQ109, while the specific action mechanism remains unclear. The results show that MmpL3 S288T mutation causes local conformational change with little effect on the global structure. With MmpL3 bound by SQ109 inhibitor, the distance between D710 and R715 increases resulting in H-bond destruction, but their interactions and proton transfer function are still restored. In addition, the rotation of Y44 in the S288T mutant leads to an obvious bend in the periplasmic domain channel and an increased number of contact residues, reducing substrate transport efficiency. This work not only provides a possible dual drug resistance mechanism of MmpL3 S288T mutant but also aids the development of novel anti-tuberculosis inhibitors.

METHODS

In this work, molecular dynamics (MD) and quantum mechanics (QM) simulations both were performed to compare inhibitor (i.e., SQ109) recognition, motion characteristics, and H-bond energy change of MmpL3 after S288T mutation. In addition, the WT_SQ109 complex structure was obtained by molecular docking program (Autodock 4.2); Molecular Mechanics/ Poisson Boltzmann Surface Area (MM-PBSA) and Solvated Interaction Energy (SIE) methods were used to calculate the binding free energies (∆Gbind); Geometric criteria were used to analyze the changes of hydrogen bond networks.

Machine learning approaches to study the structure-activity relationships of LpxC inhibitors

Antimicrobial resistance (AMR) has emerged as one of the global threats to human health in the 21st century. Drug discovery of inhibitors against novel targets rather than conventional bacterial targets has been considered an inevitable strategy for the growing threat of AMR infections. In this study, we applied quantitative structure-activity relationship (QSAR) modeling to the LpxC inhibitors to predict the inhibitory activity. In addition, we performed various cheminformatics analysis consisting of the exploration of the chemical space, identification of chemotypes, performing structure-activity landscape and activity cliffs as well as construction of the Structure-Activity Similarity (SAS) map. We built a total of 24 QSAR classification models using PubChem and MACCS fingerprint with 12 various machine learning algorithms. The best model with PubChem fingerprint is the Extremely Gradient Boost model (accuracy on the training set: 0.937; accuracy on the 10-fold cross-validation set: 0.795; accuracy on the test set: 0.799). Furthermore, it was found that the best model using the MACCS fingerprint was the Random Forest model (accuracy on the training set: 0.955; accuracy on the 10-fold cross-validation set: 0.803; accuracy on the test set: 0.785). In addition, we have identified eight consensus activity cliff generators that are highly informative for further SAR investigations. It is hoped that findings presented herein can provide guidance for further lead optimization of LpxC inhibitors.

LpxC (UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase) inhibitors: A long path explored for potent drug design

Microbial infections are becoming resistant to traditional antibiotics. As novel resistance mechanisms are developed and disseminated across the world, our ability to treat the most common infectious diseases is becoming increasingly compromised. As existing antibiotics are losing their effectiveness, especially treatment of bacterial infections, is difficult. In order to combat this issue, it is of utmost importance to identify novel pharmacological targets or antibiotics. LpxC, a zinc-dependent metalloamidase that catalyzes the committed step in the biosynthesis of lipid A (endotoxin) in bacteria, is a prime candidate for drug/therapeutic target. So far, the rate-limiting metallo-amidase LpxC has been the most-targeted macromolecule in the Raetz pathway. This is because it is important for the growth of these bacterial infections. This review showcases on the research done to develop efficient drugs in this area before and after the 2015.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning

A key enzyme in human immunodeficiency virus type 1 (HIV-1) life cycle, integrase (IN) aids the integration of viral DNA into the host DNA, which has become an ideal target for the development of anti-HIV drugs. A total of 1785 potential HIV-1 IN inhibitors were collected from the databases of ChEMBL, Binding Database, DrugBank, and PubMed, as well as from 40 references. The database was divided into the training set and test set by random sampling. By exploring the correlation between molecular descriptors and inhibitory activity, it is found that the classification and specific activity data of inhibitors can be more accurately predicted by the combination of molecular descriptors and molecular fingerprints. The calculation of molecular fingerprint descriptor provides the additional substructure information to improve the prediction ability. Based on the training set, two machine learning methods, the recursive partition (RP) and naive Bayes (NB) models, were used to build the classifiers of HIV-1 IN inhibitors. Through the test set verification, the RP technique accurately predicted 82.5% inhibitors and 86.3% noninhibitors. The NB model predicted 88.3% inhibitors and 87.2% noninhibitors with correlation coefficient of 85.2%. The results show that the prediction performance of NB model is slightly better than that of RP, and the key molecular segments are also obtained. Additionally, CoMFA and CoMSIA models with good activity prediction ability both were constructed by exploring the structure-activity relationship, which is helpful for the design and optimization of HIV-1 IN inhibitors.

QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

A High-Efficiency Artificial Synthetic Pathway for 5-Aminovalerate Production From Biobased L-Lysine in Escherichia coli

Bioproduction of 5-aminovalerate (5AVA) from renewable feedstock can support a sustainable biorefinery process to produce bioplastics, such as nylon 5 and nylon 56. In order to achieve the biobased production of 5AVA, a 2-keto-6-aminocaproate-mediated synthetic pathway was established. Combination of L-Lysine α-oxidase from Scomber japonicus, α-ketoacid decarboxylase from Lactococcus lactis and aldehyde dehydrogenase from Escherichia coli could achieve the biosynthesis of 5AVA from biobased L-Lysine in E. coli. The H₂O₂ produced by L-Lysine α-oxidase was decomposed by the expression of catalase KatE. Finally, 52.24 g/L of 5AVA were obtained through fed-batch biotransformation. Moreover, homology modeling, molecular docking and molecular dynamic simulation analyses were used to identify mutation sites and propose a possible trait-improvement strategy: the expanded catalytic channel of mutant and more hydrogen bonds formed might be beneficial for the substrates stretch. In summary, we have developed a promising artificial pathway for efficient 5AVA synthesis.

In silico exploration of aryl sulfonamide analogs as voltage-gated sodium channel 1.7 inhibitors by using 3D-QSAR, molecular docking study, and molecular dynamics simulations

It has been demonstrated by human genetics that the voltage-gated sodium channel Nav1.7 is currently a promising target for the treatment of pain. In this research, we performed molecular simulation works on a series of classic aryl sulfonamide Nav1.7 inhibitors using three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking and molecular dynamics (MD) simulations for the first time to explore the correlation between their structures and activities. The results of the relevant statistical parameters of comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analyses (CoMSIA) had been verified to be reasonable, and the deep relationship between the structures and activities of these inhibitors was obtained by analyzing the contour maps. The generated 3D-QSAR model showed a good predictive ability and provided valuable clues for the rational modification of molecules. The interactions between compounds and proteins were modeled by molecular docking studies. Finally, accuracy of the docking results and stability of the complexes were verified by 100 ns MD simulations. Detailed information on the key residues at the binding site and the types of interactions they participate in involved was obtained. The van der Waals energy contributed the most in the molecular binding process according to the calculation of binding free energy. All research results provided a good basis for further research on novel and effective Nav1.7 inhibitors.

A pathway-directed positive growth restoration assay to facilitate the discovery of lipid A and fatty acid biosynthesis inhibitors in Acinetobacter baumannii

Acinetobacter baumannii ATCC 19606 can grow without lipooligosaccharide (LOS). Lack of LOS can result from disruption of the early lipid A biosynthetic pathway genes lpxA, lpxC or lpxD. Although LOS itself is not essential for growth of A. baumannii ATCC 19606, it was previously shown that depletion of the lipid A biosynthetic enzyme LpxK in cells inhibited growth due to the toxic accumulation of lipid A pathway intermediates. Growth of LpxK-depleted cells was restored by chemical inhibition of LOS biosynthesis using CHIR-090 (LpxC) and fatty acid biosynthesis using cerulenin (FabB/F) and pyridopyrimidine (acetyl-CoA-carboxylase). Here, we expand on this by showing that inhibition of enoyl-acyl carrier protein reductase (FabI), responsible for converting trans-2-enoyl-ACP into acyl-ACP during the fatty acid elongation cycle also restored growth during LpxK depletion. Inhibition of fatty acid biosynthesis during LpxK depletion rescued growth at 37°C, but not at 30°C, whereas rescue by LpxC inhibition was temperature independent. We exploited these observations to demonstrate proof of concept for a targeted medium-throughput growth restoration screening assay to identify small molecule inhibitors of LOS and fatty acid biosynthesis. The differential temperature dependence of fatty acid and LpxC inhibition provides a simple means by which to separate growth stimulating compounds by pathway. Targeted cell-based screening platforms such as this are important for faster identification of compounds inhibiting pathways of interest in antibacterial discovery for clinically relevant Gram-negative pathogens.