Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv)

Abstract

BackgroundMycobacterium tuberculosis (M.tb) is the causative agent of tuberculosis, killing ~1.7 million people annually. The remarkable capacity of this pathogen to escape the host immune system for decades and then to cause active tuberculosis disease, makes M.tb a successful pathogen. Currently available anti-mycobacterial therapy has poor compliance due to requirement of prolonged treatment resulting in accelerated emergence of drug resistant strains. Hence, there is an urgent need to identify new chemical entities with novel mechanism of action and potent activity against the drug resistant strains.ResultsThis study describes novel computational models developed for predicting inhibitors against both replicative and non-replicative phase of drug-tolerant M.tb under carbon starvation stage. These models were trained on highly diverse dataset of 2135 compounds using four classes of binary fingerprint namely PubChem, MACCS, EState, SubStructure. We achieved the best performance Matthews correlation coefficient (MCC) of 0.45 using the model based on MACCS fingerprints for replicative phase inhibitor dataset. In case of non-replicative phase, Hybrid model based on PubChem, MACCS, EState, SubStructure fingerprints performed better with maximum MCC value of 0.28. In this study, we have shown that molecular weight, polar surface area and rotatable bond count of inhibitors (replicating and non-replicating phase) are significantly different from non-inhibitors. The fragment analysis suggests that substructures like hetero_N_nonbasic, heterocyclic, carboxylic_ester, and hetero_N_basic_no_H are predominant in replicating phase inhibitors while hetero_O, ketone, secondary_mixed_amine are preferred in the non-replicative phase inhibitors. It was observed that nitro, alkyne, and enamine are important for the molecules inhibiting bacilli residing in both the phases. In this study, we introduced a new algorithm based on Matthews correlation coefficient called MCCA for feature selection and found that this algorithm is better or comparable to frequency based approach.ConclusionIn this study, we have developed computational models to predict phase specific inhibitors against drug resistant strains of M.tb grown under carbon starvation. Based on simple molecular properties, we have derived some rules, which would be useful in robust identification of tuberculosis inhibitors. Based on these observations, we have developed a webserver for predicting inhibitors against drug tolerant M.tb H37Rv available at http://crdd.osdd.net/oscadd/mdri/.

Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs)

“…Neural Networks are a modeling method that imitates the human brain [17]. The basic processing unit in an artificial neural network (neuron) is based on the biological cell, thus an ANN will have a large number of interconnected neurons.…”

Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines

“…In order to identify the best fingerprints, which are more abundant in anticancer or non-anticancer molecules, we used the MCC-based feature selection technique as described in Methods section. In brief MCC based feature selection involves two major steps; in first step the performance of each fingerprint is computed in terms of MCC; in 2nd step, fingerprints are ranked based on their MCC score [ 32 ]. In this study, we selected fingerprints having MCC score greater than 0.2 for the development of the model.…”

“…In this study, we used an MCC-based approach for feature selection, where mean of each fingerprint in active and inactive dataset was calculated using the eqs. 1 and 2 [ 32 ]. …”

Open Source Software and Web Services for Designing Therapeutic Molecules

“…MDRIpred [179] A webserver for prediction of Inhibitor against Drug tolerant M.Tuberculosis. (http://crdd.osdd.net/oscadd/mdri/) research organizations to provide affordable medicine against tuberculosis, malaria.…”