Novel peptide inhibitor of human tumor necrosis factor-α has antiarthritic activity.

The inhibition of tumor necrosis factor (TNF)-α trimer formation renders it inactive for binding to its receptors, thus mitigating the vicious cycle of inflammation. We designed a peptide (PIYLGGVFQ) that simulates a sequence strand of human TNFα monomer using a series of in silico methods, such as active site finding (Acsite), protein-protein interaction (PPI), docking studies (GOLD and Flex-X) followed by molecular dynamics (MD) simulation studies. The MD studies confirmed the intermolecular interaction of the peptide with the TNFα. Fluorescence-activated cell sorting and fluorescence microscopy revealed that the peptide effectively inhibited the binding of TNF to the cell surface receptors. The cell culture assays showed that the peptide significantly inhibited the TNFα-mediated cell death. In addition, the nuclear translocation of the nuclear factor kappa B (NFκB) was significantly suppressed in the peptide-treated A549 cells, as observed in immunofluorescence and gel mobility-shift assays. Furthermore, the peptide protected against joint damage in the collagen-induced arthritis (CIA) mouse model, as revealed in the micro focal-CT scans. In conclusion, this TNFα antagonist would be helpful for the prevention and repair of inflammatory bone destruction and subsequent loss in the mouse model of CIA as well as human rheumatoid arthritis (RA) patients. This calls upon further clinical investigation to utilize its potential effect as an antiarthritic drug.

Identification of potential inhibitors of Mtb InhA: a pharmacoinformatics approach.

The emergence of superbugs of multi-drug resistant (MDR/RR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis (Mtb) strains at a faster rate is posing a serious threat to Tuberculosis (TB) control worldwide. Mtb enoyl-acyl carrier protein reductase (InhA) is a well-established target of the front-line anti-TB prodrug Isoniazid (INH), which requires activation by Catalase-peroxidase enzyme (KatG) in order to inhibit InhA enzyme, that is crucial for the biosynthesis of the mycobacterial cell wall. Currently, due to widespread resistance to this drug, it is necessary to identify new clinical candidates that directly inhibit InhA enzyme and do not require activation by KatG, thereby circumventing most of the resistance mechanisms. In the present study, high-throughput virtual screening of ASINEX database was carried out to identify potential direct inhibitors of Mtb InhA. Best twenty compounds with good binding energies ranging between -12.36 and -9.27 kcal/mol were selected as promising virtual screening hits. These molecules were subjected to ADME study followed by toxicity prediction. Finally, four top-ranked molecules which are structurally diverse and possess best binding affinity than the co-crystalized ligand have been chosen for MD simulation studies followed by MM-GBSA analysis to validate and ensure the stability of hits in the active site of the enzyme. Based on the 100 ns MD simulation studies and binding free energy estimates, three hit molecules B244, B369, and B310 could be considered as potential inhibitors for Mtb InhA, which are likely to be potent against INH-resistant Mtb strains after successful experimental validation.Communicated by Ramaswamy H. Sarma.

Identification of selective LdDHFR inhibitors using quantum chemical and molecular modeling approach.

Among the various known targets for the treatment of Leishmaniasis, dihydrofolate reductase (DHFR) is an essential target which plays an important role in the folate metabolic pathway. In the current study, pharmacoinformatics approaches including quantum chemistry methods, molecular docking and molecular dynamics simulations have been utilized to identify selective Leishmania donovani DHFR (LdDHFR) inhibitors. Initially, for the design of new LdDHFR inhibitors, a virtual combinatorial library was created by considering various head groups (scaffolds), linkers and tail groups. The scaffolds utilized in the library design were selected on the basis of their proton affinity (PA) estimated using quantum chemical methods, required to make a strong H-bond interaction with negatively charged LdDHFR active site. Later on, molecular docking-based virtual screening was performed to screen the designed library. Selectivity of the chosen hits toward the LdDHFR was established through re-docking in the human DHFR enzyme (HsDHFR). Best five hits were subjected to molecular dynamics (MD) simulations to validate their selectivity as well as stability in LdDHFR. Out of the five hits, four were found to be energetically more favorable and promising for selective binding toward LdDHFR in comparison to HsDHFR.Communicated by Ramaswamy H. Sarma.

Molecular docking and molecular dynamics to identify collagenase inhibitors as lead compounds to address osteoarthritis.

Osteoarthritis (OA) is a degenerative disease which affects a large number of individuals. Collagenases, which belong to a class of metalloproteases (MMPs), are responsible for the degradation of cartilage manifested in OA. Inhibition of the catalytic domains of these MMPs is one of the important therapeutic strategies proposed for the prevention of OA. The main objective of this work is to evaluate the binding of curcumin and its metabolites with the active sites of collagenases in comparison to standard inhibitors on the basis of our hypothesis that curcumin/metabolites could exhibit an inhibitory effect on MMPs. Here, we report the molecular docking analysis of curcumin and its metabolites with collagenases (MMP-1, MMP-8, MMP-13). Among the molecules tested, curcumin monoglucuronide (CMG) demonstrated the best binding affinity with MMP-13, which is specifically implicated in OA. The CMG-MMP-complexes were further subjected to molecular dynamic simulations to explore the stability of the complexes and to estimate the free binding energies. The results indicated that CMG preferentially bind to MMP-13 in comparison to that of MMP-1 and MMP-8 with binding free energies (ΔGbind) of (-60.55), (-27.02) and (-46.91) kcal/mol, respectively. This is the first study which suggests that curcumin monoglucuronide can be considered as an effective lead compound to prevent the progression of OA.Communicated by Ramaswamy H. Sarma.

Identification of Selective Inhibitors of LdDHFR Enzyme Using Pharmacoinformatic Methods.

Dihydrofolate reductase (DHFR) is a well-known enzyme of the folate metabolic pathway and it is a validated drug target for leishmaniasis. However, only a few leads are reported against Leishmania donovani DHFR (LdDHFR), and thus, there is a need to identify new inhibitors. In this article, pharmacoinformatic tools such as molecular docking, virtual screening, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, and molecular dynamics (MD) simulations were utilized to identify potential LdDHFR inhibitors. Initially, a natural DHFR substrate (dihydrofolate), a classical DHFR inhibitor (methotrexate), and a potent LdDHFR inhibitor, that is, "5-(3-(octyloxy)benzyl)pyrimidine-2,4-diamine" (LEAD) were docked in the active site of the LdDHFR and MD simulated to understand the binding mode characteristics of the substrates/inhibitors in the LdDHFR. The shape of the LEAD molecule was used as a query for shape-based virtual screening, while the three-dimensional structure of LdDHFR was utilized for docking-based virtual screening. In silico ADMET factors were also considered during virtual screening. These two screening processes yielded 25 suitable hits, which were further validated for their selectivity toward LdDHFR using molecular docking and prime molecular mechanics/generalized born surface area analysis in the human DHFR (HsDHFR). Best six hits, which were selective and energetically favorable for the LdDHFR, were chosen for MD simulations. The MD analysis showed that four of the hits exhibited very good binding affinity for LdDHFR with respect to HsDHFR, and two hits were found to be more selective than the reported potent LdDHFR inhibitor. The present study thus identifies hits that can be further designed and modified as potent LdDHFR inhibitors.