Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis $$(\mathbf{MPDS}^{\mathbf{TB}})$$ ( MPDS TB )

Gaur, Anamika Singh; Bhardwaj, Anshu; Sharma, Arun; John, Lijo; Vivek, M.; Tripathi, Neha; Bharatam, Prasad V.; Kumar, Rakesh; Janardhan, Sridhara; Mori, Abhaysinh; Banerji, Anirban; Lynn, Andrew M.; Hemrom, Anmol Jaywant; Passi, Anurag; Singh, Aparna; Kumar, Asheesh; Muvva, Charuvaka; Madhuri, Chinmai; Choudhury, Chinmayee; Kumar, Devesh; Pandit, Deepak; Singam, ER Azhagiya; Raghava, Gajendra P. S.; Sailaja, Hari; Jangra, Harish; Raithatha, Kaamini; Tanneeru, Karunakar; Chaudhary, Kumardeep; Karthikeyan, Muthukumarasamy; Prasanthi, M; Kumar, Nandan; Yedukondalu, N.; Rajput, Neeraj Kumar; Saranya, Perumal; Narang, Pankaj; Dutta, P. K.; Krishnan, Rohini; Sharma, Reetu; Srinithi, R; Mishra, Ritesh; Hemasri, S; Singh, Sandeep; Venkatesan, S.; Kumar, Suresh; Jaleel, U. C. Abdul; Khedkar, Vijay M.; Joshi, Yogesh M.; Sastry, G. Narahari

doi:10.1007/s12039-017-1268-4

Cited by 21 publications

(13 citation statements)

References 83 publications

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“…A typical chemical space size is estimated to be 10 60 molecules, and screening these molecules experimentally for the identification of lead molecules is a mammoth task. Virtual screening applies a series of filters to identify potential lead compounds from a huge pool of compounds [ 1 – 3 ]. Many commercial and open-source drug discovery software are available for drug discovery to minimize time and cost.…”

Section: Introductionmentioning

confidence: 99%

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite

et al. 2022

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A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085 . Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11030-022-10506-5.

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Section: Introductionmentioning

confidence: 99%

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite

et al. 2022

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“…The advancement of structure-based and ligand-based drug design approaches has made drug discovery processes more feasible by identifying potential lead-like molecules before synthesis and biological evaluation. − However, M.tb permeability is the major concern in developing potential antimycobacterial drugs. Even most potential M.tb inhibitors that are validated against InhA do not have the potential efficacy due to their inability to penetrate through the M.tb cell membrane.…”

Section: Introductionmentioning

confidence: 99%

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

Nagamani

Sastry

2021

ACS Omega

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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.

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“…In the last few decades, there has been rapid development in computational drug discovery algorithms for ab initio protein modeling, homology modeling, protein folding dynamics, molecular docking, pharmacophore modeling, virtual screening, quantitative structure activity relationship (QSAR) etc. (Kumar Srivastava et al, 2012;Kurumurthy et al, 2012;Choudhury et al, 2014Choudhury et al, , 2015Choudhury et al, , 2016aGaur et al, 2017). Several new strategies are also designed for repurposing existing drugs or discover new ones (Passi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB

Choudhury

Bhardwaj

2020

Front. Chem.

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Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.

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Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis $$(\mathbf{MPDS}^{\mathbf{TB}})$$ ( MPDS TB )

Cited by 21 publications

References 83 publications

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite

Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite

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

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB

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