Mycobacterium tuberculosis Cell Wall Permeability Model Generation Using Chemoinformatics and Machine Learning Approaches

Abstract

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx−efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC 50 ) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and nai ̈ve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds

“…As noted above, similar to the previous studies [ 60 , 63 ], the compounds that have shown activity in any of the selected target-based assays were classified as permeable if they were active in any of the selected cell-based assays; otherwise they were taken to be impermeable. To this end, the publicly available PubChem 2022 database [ 77 ] was used as the source of (Big) raw data (in particular, it included both the assays synchronized with ChEMBL [ 61 ] and a number of additional assays).…”

“…As noted above, similar to the previous studies [ 60 , 63 ], the compounds that have shown activity in any of the selected target-based assays were classified as permeable if they were active in any of the selected cell-based assays, otherwise, they were taken to be impermeable. As the source of (Big) raw data, the publicly available PubChem 2022 database [ 77 ] was employed.…”

“…For various subsets of 366 common compounds and additional 273 compounds highly potent in cell-based assays, the Partial Least Squares Regression (PLSR) models based on the subset of PaDEL [ 62 ] 1D and 2D descriptors were built and further translated to classification predictions, and the sensitivity of 70–95% and specificity of 8–45% were achieved for the validation set. Developing this approach, the recent work [ 63 ] used a ChEMBL-derived dataset of 1114 compounds, PaDEL descriptors, and a variety of machine learning methods to achieve the area under ROC curve (AUC) value of 0.81 for the validation set of 40 compounds.…”

Maintenance of cell wall remodeling and vesicle production are connected inMycobacterium tuberculosis

“…We consider this finding of high relevance since Mtb permeability is one the major concerns when developing potential antimycobacterial drugs. Nevertheless, bacterial cell permeability is a complex property that it is controlled by physiochemical, biological, and chemical processes such as stereochemistry, lipophilicity, saturation and unsaturation, flexibility, viscosity, fluidity, pressure, temperature and physiological conditions (Nagamani and Sastry, 2021). How these variables change to allow the release of MEVs is not known.…”

Free Energy Barriers for Passive Drug Transport through the Mycobacterium tuberculosis Outer Membrane: A Molecular Dynamics Study

“…The proposed predictive QSAR models suggest the importance of molecular connectivity, lipophilicity, electrotopological states, and functional groups of a molecule in determining its Mtb cell wall permeability. Different machine learning models to predict the Mtb membrane permeability were compared [24]. In the study [25], a predictive in silico model of Mtb cell wall permeability applicable to diverse drugs and drug-like compounds was derived from the extensive Big Data-based dataset.…”