Designing of phenol-based ß-carbonic anhydrase1 inhibitors through QSAR, molecular docking, and MD simulation approach.

Tuberculosis (Tb) is an airborne infectious disease caused by Mycobacterium tuberculosis. Beta-carbonic anhydrase 1 (ß-CA1) has emerged as one of the potential targets for new antitubercular drug development. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking, and molecular dynamics (MD) simulation approaches were performed on a series of natural and synthetic phenol-based ß-CA1 inhibitors. The developed 3D-QSAR model (r2 = 0.94, q2 = 0.86, and pred_r2 = 0.74) indicated that the steric and electrostatic factors are important parameters to modulate the bioactivity of phenolic compounds. Based on this indication, we designed 72 new phenolic inhibitors, out of which two compounds (D25 and D50) effectively stabilized ß-CA1 receptor and, thus, are potential candidates for new generation antitubercular drug discovery program.

QSAR based therapeutic management of M. tuberculosis.

Mycobacterium tuberculosis is responsible for severe mortality and morbidity worldwide but, under-developed and developing countries are more prone to infection. In search of effective and wide-spectrum anti-tubercular agents, interdisciplinary approaches are being explored. Of the several approaches used, computer based quantitative structure activity relationship (QSAR) have gained momentum. Structure-based drug design and discovery implies a combined knowledge of accurate prediction of ligand poses with the good prediction and interpretation of statistically validated models derived from the 3D-QSAR approach. The validated models are generally used to screen a small combinatorial library of potential synthetic candidates to identify hits which further subjected to docking to filter out compounds as novel potential emerging drug molecules to address multidrug-resistant tuberculosis. Several newer models are integrated to QSAR methods which include different types of chemical and biological data, and simultaneous prediction of pharmacological activities including toxicities and/or other safety profiles to get new compounds with desired activity. In the process, several newer molecules have been identified which are now being assessed for their clinical efficacy. Present review deals with the advances made in the field highlighting overall future prospects of the development of anti-tuberculosis drugs.

A QSAR model of benzoxazole derivatives as potential inhibitors for inosine 5`-monophosphate dehydrogenase from Cryptosporidium parvum.

Cryptosporidium parvum is the common enteric protozoan pathogen causing cryptosporidiosis in human. Available drugs to treat cryptosporidiosis are ineffective and there is yet no vaccine against C. parvum. Therefore, it is of interest to design an improved yet effective drug against C. parvum. Here, we docked benzoxazole derivatives (collected from literature) with inosine 5`- monophosphate dehydrogenase (IMPDH) from Cryptosporidium parvum using the program AutoDock 4.2. The docked protein - inhibitor complex structure was optimized using molecular dynamics simulation for 5 ps with the CHARMM-22 force field using NAMD (NAnoscale Molecular Dynamics program) incorporated in visual molecular dynamics (VMD 1.9.2) and then evaluating the stability of complex structure by calculating RMSD values. NAMD is a parallel, object-oriented molecular dynamics code designed for high-performance simulation of large biomolecular systems. A quantitative structure activity relationship (QSAR) model was built using energy-based descriptors as independent variable and pIC50 value as dependent variable of fifteen known benzoxazole derivatives with C. parvum IMPDH protein, yielding correlation coefficient r2 of 0.7948. The predictive performance of QSAR model was assessed using different cross-validation procedures. Our results suggest that a ligand-receptor binding interaction for inosine 5`-monophosphate dehydrogenase using a QSAR model is promising approach to design more potent inosine 5`-monophosphate dehydrogenase inhibitors prior to their synthesis.

2D-QSAR model development and analysis on variant groups of anti-tuberculosis drugs.

A quantitative structure activity relationship study was performed on different groups of anti-tuberculosis drug compound for establishing quantitative relationship between biological activity and their physicochemical /structural properties. In recent years, a large number of herbal drugs are promoted in treatment of tuberculosis especially due to the emergence of MDR (multi drug resistance) and XDR (extensive drug resistance) tuberculosis. Multidrug-resistant TB (MDR-TB) is resistant to front-line drugs (isoniazid and rifampicin, the most powerful anti-TB drugs) and extensively drug-resistant TB (XDR-TB) is resistant to front-line and second-line drugs. The possibility of drug resistance TB increases when patient does not take prescribed drugs for defined time period. Natural products (secondary metabolites) isolated from the variety of sources including terrestrial and marine plants and animals, and microorganisms, have been recognized as having antituberculosis action and have recently been tested preclinically for their growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. A quantitative structure activity relationship (QSAR) studies were performed to explore the antituberculosis compound from the derivatives of natural products . Theoretical results are in accord with the in vitro experimental data with reported growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. Antitubercular activity was predicted through QSAR model, developed by forward feed multiple linear regression method with leave-one-out approach. Relationship correlating measure of QSAR model was 74% (R(2) = 0.74) and predictive accuracy was 72% (RCV(2) = 0.72). QSAR studies indicate that dipole energy and heat of formation correlate well with anti-tubercular activity. These results could offer useful references for understanding mechanisms and directing the molecular design of new lead compounds with improved anti-tubercular activity. The generated QSAR model revealed the importance of structural, thermodynamic and electro topological parameters. The quantitative structure activity relationship provides important structural insight in designing of potent antitubercular agent.

A molecular model for diacylglycerol acyltransferase from Mortierella ramanniana var. angulispora.

Acyl CoA diacylglycerol acyltransferase (DGAT, EC 2.3.120) is recognized as a key player of cellular diacylglycerol metabolism. It catalyzes the terminal, yet the committed step in triacylglycerol synthesis using diacylglycerol and fatty acyl CoA as substrates. The protein sequence of diacylglycerol acyltransferse (DGAT) Type 2B in Moretierella ramanniana var. angulispora (Protein_ID = AAK84180.1) was retrieved from GenBank. However, a structure is not yet available for this sequence. The 3D structure of DGAT Type 2B was modeled using a template structure (PDB ID: 1K30) obtained from Protein databank (PDB) identified by searching with position specific iterative BLAST (PSI-BLAST). The template (PDB ID: 1K30) describes the structure of DGAT from Cucurbita moschata. Modeling was performed using Modeller 9v2 and protein model is hence generated. The DGAT type 2B protein model was subsequently docked with six inhibitors (sphingosine; trifluoroperazine; phosphatidic acid; lysophospatidylserine; KCl; 1, 2-diolein) using AutoDock (a molecular docking program). The binding of inhibitors to the protein model of DGAT type 2B is discussed.

Computational identification of composite regulatory sites in 16s-rRNA gene promoters of Mycobacterium species.

The availability of completely sequenced genomes allow the use of computational techniques to investigate cis-acting sequences controlling transcription regulation associated with groups of functionally related genes. Theoretical analysis was performed to assign functions to regulatory systems. The identification of such sites is relevant for locating a promoter at the 5' boundary of a gene. They also allow the prediction of specific gene-expression pattern and response to disturbances in a known signaling pathway. Here, we describe the identification of composite transcription factor (TF) binding sites over promoter regions in16s-rRNA gene for mycobacterium species strains ICC47, ICC67, ICC43 and CMVL700. It is established that the ribosomal gene comprises of sequences that are conserved during evolution and interspersed with divergent regions. Computational identification of known TF-binding sites was performed using TFSITESCAN tool and ooTFD database. The ICC67, ICC47, ICC43 and CMYL700 strains showed 12, 13, 9 and 15 known TF binding sites, respectively. Comparison between strains suggests 9 known TF predicted binding sites to be conserved among them. These data provide basis for the understanding of promoter regulation in 16s-rRNA.