Modeling of a new tubercular maltosyl transferase, GlgE, study of its binding sites and virtual screening.

Recently maltosyl transferase of Mycobacterium tuberculosis (mtb GlgE) belonging to α-amylase family has been identified as a potential drug target. Despite its importance, its three dimensional (3D) structure is unavailable. In this study we have modeled its 3D homo-dimeric structure using its homologue in Streptomyces ceolicolor (stp GlgE) as the template. Its monomer consists of five domains and four inserts, out of which two inserts are unique to mtb GlgE. It also has three binding cavities. One primary (pbs) and two secondary (sbs1 and sbs2), with one unique insert appearing within sbs2. Investigation of its homo-dimeric model revealed the presence of a disulphide bridge between Cys-29 of both the chains which is absent in stp GlgE. Virtual screening with known substrates and substrate analogues of α-amylase family proteins indicated better binding of maltose to sbs1 than pbs. Among all computationally screened substrates 3-O-Alpha-D-Glucopyranosyl-D-Fructose (OTU) docked with best binding affinity to pbs. Interaction of known inhibitors of α-amylase family proteins from CHEMBL is also studied. This reveals for the first time the unique 3D structure of mtb GlgE and provides insights into its active sites and substrate binding affinities. This may help in developing new anti-tubercular drugs and its analogues.

Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening, docking, and molecular dynamics simulations.

Pharmacophore-based virtual screening, subsequent docking, and molecular dynamics (MD) simulations have been done to identify potential inhibitors of maltosyl transferase of Mycobacterium tuberculosis (mtb GlgE). Ligand and structure-based pharmacophore models representing its primary binding site (pbs) and unique secondary binding site 2 (sbs2), respectively, were constructed based on the three dimensional structure of mtb GlgE. These pharmacophore models were further used for screening of ZINC and antituberculosis compounds database (ATD). Virtually screened molecules satisfying Lipinski's rule of five were then analyzed using docking studies and have identified 23 molecules with better binding affinity than its natural substrate, maltose. Four top scoring ligands from ZINC and ATD that either binds to pbs or sbs2 have been subjected to 10 ns each MD simulations and binding free energy calculations. Results of these studies have confirmed stable protein ligand binding. Results reported in the article are likely to be helpful in antitubercular therapeutic development research.

De novo design of potential RecA inhibitors using multi objective optimization.

De novo ligand design involves optimization of several ligand properties such as binding affinity, ligand volume, drug likeness, etc. Therefore, optimization of these properties independently and simultaneously seems appropriate. In this paper, the ligand design problem is modeled in a multiobjective using Archived MultiObjective Simulated Annealing (AMOSA) as the underlying search algorithm. The multiple objectives considered are the energy components similarity to a known inhibitor and a novel drug likeliness measure based on Lipinski's rule of five. RecA protein of Mycobacterium tuberculosis, causative agent of tuberculosis, is taken as the target for the drug design. To gauge the goodness of the results, they are compared to the outputs of LigBuilder, NEWLEAD, and Variable genetic algorithm (VGA). The same problem has also been modeled using a well-established genetic algorithm-based multiobjective optimization technique, Nondominated Sorting Genetic Algorithm-II (NSGA-II), to find the efficacy of AMOSA through comparative analysis. Results demonstrate that while some small molecules designed by the proposed approach are remarkably similar to the known inhibitors of RecA, some new ones are discovered that may be potential candidates for novel lead molecules against tuberculosis.

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

IVGA3D: De novo ligand design using a variable sized tree representation.

Rational drug design is an important step towards effective patient care enabling lead molecule discovery in a relatively faster and inexpensive way. This article describes a variable string length genetic algorithm with domain specific operators for de novo ligand design. The algorithm first mines the active site of the given protein receptor whose geometry and chemical composition guides the ligand building. Active site mined by the algorithm is compared with two more established active sites detecting schemes to evaluate its efficiency. Various combinations from a suite of forty one fragments are mined to design the ligands. Bond stretching, angle bending, torsional terms, van der Waals and electrostatic interaction energy with distance dependent dielectric constant contribute are used to compute the internal energy of the ligand and the interaction energy of the ligand receptor complex. Forty one fragments are used to design the ligands. Experimental results are provided for HIV-1 Protease, HIV-1 Nef and Thrombin demonstrating the superiority of the proposed scheme vis-a-vis three other approaches. Comparison with known inhibitors also demonstrates the effectiveness of the proposed approach.