Structure Elucidation and Identification of Novel Lead Molecules against Sulfur Import Protein cysA of Mycobacterium tuberculosis.

AIMS: The present work considers the Sulphate import ABC transporter protein (cysA) as a potential drug target for the identification of inhibitors for the protein. BACKGROUND: The ABC (ATP binding cassette) transporters play a crucial role in the survival and virulence of Mycobacterium tuberculosis by the acquisition of micronutrients from host tissue. OBJECTIVES: The 3D structural features of the cysA protein are built. Molecular scaffolds are identified by implementing active site identification, ADME properties, Virtual Screening, and a few other computational techniques. METHODS: The theoretical model of cysA is predicted using homology modeling protocols, and the structure is validated by various validation methods. The prediction of partial dimer formation through protein-protein docking methods gave insight into the conformational changes taking place in the cysA protein. The natural substrate ATP is docked with cysA protein that confirms the ATP binding site. To find the drug-like compounds, virtual screening studies were carried out around the active site by several ligand databases. RESULTS: The findings demonstrate the significance of residues SER41, GLY42, ARG50, GLN85, HIS86, LYS91, ARG142, and ASP161 in drug-target interactions. The docking studies of existing TB drugs against cysA were also performed. The result analysis shows that none of the existing drugs inhibits the ATP active site, which confirms cysA as a promising drug target. Using in-silico methods, the ADME parameters of a few chosen ligand molecules are predicted and contrasted with the ADME characteristics of the available TB medications. CONCLUSION: The results revealed the values of ADME parameters of selected ligand molecules are more permissible than existing TB drugs, which emphasizes the drug-like activity of ligand molecules by inhibition of cysA proteins. The structural data, active site information, and selected ligand molecules help in the identification of new therapeutic scaffolds for Tuberculosis.

Structure-based drug design, synthesis and screening of MmaA1 inhibitors as novel anti-TB agents.

Tuberculosis is one of the leading causes of death across the world. The treatment regimens for tuberculosis are well established, but still the control of the disease faces many challenges such as lengthy treatment protocols, drug resistance and toxicity. In the present work, mycolic acid methyl transferase (MmaA1), a protein involved in the maturation of mycolic acids in the biochemical pathway of the Mycobacterium, was studied for novel drug discovery. The homology model of the MmaA1 protein was built and validated by using computational techniques. The MmaA1 protein has 286 amino acid residues consisting of 10 α-helices and 7 ß-sheets. The active site of the MmaA1 protein was identified using CASTp, SiteMap and PatchDock. Virtual screening studies were performed with two small molecule ligand databases: Asinex synergy and Diverse_Elite_Gold_Platinum databases having a total of 43,446 molecules and generated 1,30,814 conformers against the predicted and validated active site of the MmaA1 protein. Binding analysis showed that the residues ASP 19, PHE 22, TRP 30, TYR 32, TRP 74 and ALA 77 of MmaA1 protein have consistent interactions with the ligands. The hit ligands were further filtered by in silico ADME properties to eliminate potentially toxic molecules. Of the top 10 molecules, 3-(2-morpholinoacetamido)-N-(1,4-dihydro-4-oxoquinazolin-6-yl) benzamide was synthesised and screened for in vitro anti-TB activity against Mtb H37Rv using MABA assay. The compound and its intermediates exhibited good in vitro anti-TB activity which can be taken up for future lead optimisation studies. Structure based virtual screening study was performed using a validated homology model against small molecules from two virtual compound libraries. Synthesised the lead compound 3-(2-morpholinoacetamido)-N-(1,4-dihydro-4-oxoquinazolin-6-yl)benzamide obtained from virtual screening. In vitro activity against Mtb H37Rv has given a promising result.

Structural insights into suppressor of cytokine signaling 1 protein- identification of new leads for type 2 diabetes mellitus.

The study considers the Suppressor of cytokine signaling 1 (SOCS1) protein as a novel Type 2 diabetes mellitus (T2DM) drug target. T2DM in human beings is also triggered by the over expression of SOCS proteins. The SOCS1 acts as a ubiquitin ligase (E3), degrades Insulin Receptor Substrate 1 and 2 (IRS1 and IRS2) proteins, and causes insulin resistance. Therefore, the structure of the SOCS1 protein was evaluated using homology-modeling and molecular dynamics methods and validated using standard computational protocols. The Protein-Protein docking study of SOCS1 with its natural substrates, IRS1 and IRS2, and subsequent solvent accessible surface area analysis gave insight into the binding region of the SOCS1 protein. The in silico active site prediction tools highlight the residues Val155 to Ile211 in SOCS1 being implicated in the ubiquitin mediated protein degradation of the proteins IRS1 and IRS2. Virtual screening in the active site region, using large structural databases, results in selective lead structures with 3-Pyridinol, Xanthine, and Alanine moieties as Pharmacophore. The virtual screening study shows that the residues Glu149, Gly187, Arg188, Leu191, and Ser205 of the SOCS1 are important for binding. The docking study with current anti-diabetic therapeutics shows that the drugs Glibenclamide and Glyclopyramide have a partial affinity towards SOCS1. The predicted ADMET and IC50 properties for the identified ligands are within the acceptable range with drug-like properties. The structural data of SOCS1, its active site, and the identified lead structures are expedient in the development of new T2DM therapeutics.

Identification of small molecular inhibitors for efflux protein Rv2688c of Mycobacterium tuberculosis.

Mycobacterium tuberculosis (Mtb) is the pathogen, which causes tuberculosis. The development of multidrug-resistant and extensively drug-resistant strains in Mtb is due to an efflux mechanism of antibiotics in the bacteria. The efflux pump proteins in the bacteria are implicated in the active efflux of antibiotics. The efflux pump protein, "fluoroquinolones export ATP-binding protein Rv2688c" (FEAB), is considered as a potential therapeutic target to prevent tuberculosis. In the present work, in silico protocols are applied to identify inhibitors for the FEAB protein to arrest the efflux mechanism. Comparative modeling techniques are used to build the protein structure. The generated structure consists of 9 helices, 13 beta strands, and 3 ß sheets. The active site is predicted using active site prediction server tools. The virtual screening protocols are carried out to generate small ligand inhibitor structures. The identified ligand molecules show selective binding with Ser97, Glu99, Lys149, Asp171, Glu172, and Ser175 amino acid residues of the protein. The ligand molecules are subjected to in silico prediction of pharmaco kinetic properties, and the predicted IC50 (HERG) of all the molecules are less than -5.0, which is indicative of the identified ligand molecules is being potentially good FEAB inhibitors.

Structural Evaluation and Binding Mode Analysis of CCL19 and CCR7 Proteins-Identification of Novel Leads for Rheumatic and Autoimmune Diseases: An Insilico study.

The Human Chemokine (C-C motif) ligand 19 (CCL19) protein plays a major role in rheumatic and autoimmune diseases. The 3D models of the CCL19 and its receptor CCR7 are generated using homology modeling and are validated using standard computational protocols. Disulfide bridges identified in 3D model of CCL19 protein give extra stability to the overall protein structure. The active site region of protein CCL19, containing N-terminal amino acid residues (Gly22 to Leu31), is predicted using in silico techniques. Protein-protein docking studies are carried out between the CCL19 and CCR7 proteins to analyse the active site binding interactions of CCL19. The binding domain of CCL19 is subjected to structure-based virtual screening of small molecule databases, and identified several bioisosteric ligand molecules having pyrrolidone and piperidone pharmacophores. The prioritized ligands with acceptable ADME properties are reported as new leads for the design of potential CCL19 antagonists for rheumatic and autoimmune disease therapies.

Targeting the ubiquitin-conjugating enzyme E2D4 for cancer drug discovery-a structure-based approach.

Cancer progression is a global burden. The incidence and mortality now reach 30 million deaths per year. Several pathways of cancer are under investigation for the discovery of effective therapeutics. The present study highlights the structural details of the ubiquitin protein 'Ubiquitin-conjugating enzyme E2D4' (UBE2D4) for the novel lead structure identification in cancer drug discovery process. The evaluation of 3D structure of UBE2D4 was carried out using homology modelling techniques. The optimized structure was validated by standard computational protocols. The active site region of the UBE2D4 was identified using computational tools like CASTp, Q-site Finder and SiteMap. The hydrophobic pocket which is responsible for binding with its natural receptor ubiquitin ligase CHIP (C-terminal of Hsp 70 interacting protein) was identified through protein-protein docking study. Corroborating the results obtained from active site prediction tools and protein-protein docking study, the domain of UBE2D4 which is responsible for cancer cell progression is sorted out for further docking study. Virtual screening with large structural database like CB_Div Set and Asinex BioDesign small molecular structural database was carried out. The obtained new ligand molecules that have shown affinity towards UBE2D4 were considered for ADME prediction studies. The identified new ligand molecules with acceptable parameters of docking, ADME are considered as potent UBE2D4 enzyme inhibitors for cancer therapy.

Identification of New Lead Molecules Against UBE2NL Enzyme for Cancer Therapy.

Cancer is characterized by abnormal growth of cells. Targeting ubiquitin proteins in the discovery of new anticancer therapeutics is an attractive strategy. The present study uses the structure-based drug discovery methods to identify new lead structures, which are selective to the putative ubiquitin-conjugating enzyme E2N-like (UBE2NL). The 3D structure of the UBE2NL was evaluated using homology modeling techniques. The model was validated using standard in silico methods. The hydrophobic pocket of UBE2NL that aids in binding with its natural receptor ubiquitin-conjugating enzyme E2 variant (UBE2V) was identified through protein-protein docking study. The binding site region of the UBE2NL was identified using active site prediction tools. The binding site of UBE2NL which is responsible for cancer cell progression is considered for docking study. Virtual screening study with the small molecular structural database was carried out against the active site of UBE2NL. The ligand molecules that have shown affinity towards UBE2NL were considered for ADME prediction studies. The ligand molecules that obey the Lipinski's rule of five and Jorgensen's rule of three pharmacokinetic properties like human oral absorption etc. are prioritized. The resultant ligand molecules can be considered for the development of potent UBE2NL enzyme inhibitors for cancer therapy.

Homology modeling and virtual screening studies of FGF-7 protein-a structure-based approach to design new molecules against tumor angiogenesis.

Keratinocyte growth factor (KGF) protein is a member of the fibroblast growth factor (FGF) family, which is also known as FGF-7. The FGF-7 plays an important role in tumor angiogenesis. In the present work, FGF-7 is treated as a potential therapeutic target to prevent angiogenesis in cancerous tissue. Computational techniques are applied to evaluate and validate the 3D structure of FGF-7 protein. The active site region of the FGF-7 protein is identified based on hydrophobicity calculations using CASTp and Q-site Finder active site prediction tools. The protein-protein docking study of FGF-7 with its natural receptor FGFR2b is carried out to confirm the active site region in FGF-7. The amino acid residues Asp34, Arg67, Glu116, and Thr194 in FGF-7 interact with the receptor protein (FGFR2b). A grid is generated at the active site region of FGF-7 using Glide module of Schrödinger suite. Subsequently, a virtual screening study is carried out at the active site using small molecular structural databases to identify the ligand molecules. The binding interactions of the ligand molecules, with piperazine moiety as a pharmacophore, are observed at Arg67 and Glu149 residues of the FGF-7 protein. The identified ligand molecules against the FGF-7 protein show permissible pharmacokinetic properties (ADME). The ligand molecules with good docking scores and satisfactory pharmacokinetic properties are prioritized and identified as novel ligands for the FGF-7 protein in cancer therapy.

Homology modeling and virtual screening of ubiquitin conjugation enzyme E2A for designing a novel selective antagonist against cancer.

Cancer is a major health problem in the world. The initiation and progression of cancer is due to imbalance between the programmed cell growth and death. These processes are triggered by the ubiquitin family enzymes. The ubiquitin-like proteins are responsible for the cell metabolism. Ubiquitin-dependent proteolysis by the 26s proteasome plays a crucial role in cell cycle progression as well as in tumorigenesis. In the ubiquitin proteasomal degradation pathway, ubiquitin conjugation enzyme E2A (UBE2A) binds with ubiquitin ligase RAD18, results in polyubiquitation reaction and cell cycle progression. UBE2A is an important contributing factor for the control of tumorigenesis. In the present work, the 3D model of the protein UBE2A was generated by homology modeling technique. The generated 3D structure of the UBE2A was validated, and active site was identified using standard computational protocols. The active site was subjected to structure-based virtual screening using small molecule data banks, and new molecules were identified. The ADME properties of the new ligand molecules were predicted, and the new ligands are identified as potent UBE2A antagonists for cancer therapy.