Machine Learning-Guided Discovery of AcrB and MexB Efflux Pump Inhibitors.

Multidrug efflux pump is one of the reasons behind the antimicrobial inactivity related to infection caused by Gram-negative pathogens. The inner membrane resistance-nodulation-cell division transporter proteins, AcrB and MexB, in association with outer membrane proteins, TolC and OprM, are responsible for the extrusion of a broad range of substrates, followed by recognizing them. Although various inhibitors were proposed to stop the efflux activity of the transporter protein, none of them had been approved clinically. Our study aims to identify potent inhibitor-like molecules employing supervised classification models trained upon the molecular descriptors of previously known inhibitors. Based on the intrinsic minimum inhibitory concentration (MIC) values of the reported inhibitors, they were classified into highly potent and less potent categories. A total of 10 different classification models were built using various molecular descriptors; among them, support vector machine, Random Forest, AdaBoost, and LightGBM models appeared to deliver promising results with >80% accuracy. These top four models were implemented on a library of 5043 to obtain 8 hit molecules after the multistep filtering process. To assess their activity toward AcrB and MexB, several molecular dynamics simulations of their ligand-bound structures were performed. We also calculated the binding free-energy values and analyzed other structural properties. Mol.3488 of the unknown molecules showed higher binding affinities for both AcrB and MexB. Also, the presence of "pyridopyrimidone" and "benzothiazole" moieties in the molecules and "V"-shaped orientation of ligands inside the deep binding pocket increase the binding affinity, thereby higher inhibitory properties.

Probing the pH Sensitivity of OprM: Insights into Metastable States and Semi-Open Conformation.

Efflux pumps are specialized transport proteins that play a key role in the bacterial defense against a wide spectrum of antibiotics. Hence, understanding the biophysical mechanism associated with this complex system of drug expulsion becomes crucial. This work deals with some vital aspects of the outer membrane factor (OMF) of MexAB-OprM. After being passed through MexB and MexA, efflux substrates have to go through OprM for their final judgment. Thus, it is very important to understand the periplasmic pore opening mechanism and the associated biophysical changes during this process. Our study captures a detailed analysis of the pore opening mechanism involving OprM. With powerful molecular dynamics (MD) techniques such as well-tempered metadynamics, the presence of metastable states in between open and closed states was confirmed. Also, upon mutating R376, the energy barrier for the conversion of the close to open conformation decreases, indicating an important role played by the residue. Further, constant pH MD was performed to capture the effect of pH in both conformations. OprM exhibits distinct conformational states at pH values greater than 5.5 and lower than 5.5, suggesting its pH-responsive characteristics. Overall, our study elucidates a crucial undertaking toward discovering potential inhibitors for MexAB-OprM efflux pumps.

Role of the Residue Q1919 in Increasing Kinase Activity of G2019S LRRK2 Kinase: A Computational Study.

Mutations in multi-domain leucine-rich repeat kinase 2 (LRRK2) have been an interest to researchers as these mutations are associated with Parkinson's disease. G2019S mutation in LRRK2 kinase domain leads to the formation of additional hydrogen bonds by S2019 which results in stabilization of the active state of the kinase, thereby increasing kinase activity. Two additional hydrogen bonds of S2019 are reported separately. Here, a mechanistic picture of the formation of additional hydrogen bonds of S2019 with Q1919 (also with E1920) is presented using 'active' Roco4 kinase as a homology model and its relationship with the stabilization of the 'active' G2019S LRRK2 kinase. A conformational flipping of residue Q1919 was found which helped to form stable hydrogen bond with S2019 and made 'active' state more stable in G2019S LRRK2. Two different states were found within the 'active' kinase with respect to the conformational change (flipping) in Q1919. Two doubly-mutated systems, G2019S/Q1919A and G2019S/E1920 K, were studied separately to check the effect of Q1919 and E1920. For both cases, the stable S2 state was not formed, leading to a decrease in kinase activity. These results indicate that both the additional hydrogen bonds of S2019 (with Q1919 and E1920) are necessary to stabilize the active G2019S LRRK2.

Prediction of COMT Inhibitors Using Machine Learning and Molecular Dynamics Methods.

Catechol O-methyltransferase (COMT) plays a vital role in deactivating neurotransmitters like dopamine, norepinephrine, etc., by methylating those compounds. However, the deactivation of an excess amount of neurotransmitters leads to serious mental ailments such as Parkinson's disease. Molecules that bind inside the enzyme's active site inhibit this methylation mechanism by methylating themselves, termed COMT inhibitors. Our study is focused on designing these inhibitors by various machine learning methods. First, we have developed a classification model with experimentally available COMT inhibitors, which helped us generate a new data set of small inhibitor-like molecules. Then, to predict the activity of the new molecules, we have applied regression techniques such as Random Forest, AdaBoost, gradient boosting, and support vector machines. Each of the regression models yielded an R2 value > 70% for both training and test data sets. Finally, to validate our models, 200 ns long molecular dynamics (MD) simulations of the two known inhibitors with known IC50 values and the resultant inhibitors were performed inside the binding pockets to check their stability within. The free energy barrier of the methyl transfer from S-adenosyl-l-methionine (SAM) to each inhibitor was determined by combining steered molecular dynamics (SMD) and umbrella sampling using the quantum mechanics/molecular mechanics (QM/MM) method.