Displaying publications 21 - 29 of 29 in total

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  1. Cournia Z, Soares TA, Wahab HA, Amaro RE
    J Chem Inf Model, 2021 11 22;61(11):5305-5306.
    PMID: 34668709 DOI: 10.1021/acs.jcim.1c01185
  2. Lee YV, Choi SB, Wahab HA, Choong YS
    J Chem Inf Model, 2017 09 25;57(9):2351-2357.
    PMID: 28820943 DOI: 10.1021/acs.jcim.7b00265
    Tuberculosis (TB) still remains a global threat due to the emergence of a drug-resistant strain. Instead of focusing on the drug target of active stage TB, we are highlighting the isocitrate lyase (ICL) at the dormant stage TB. ICL is one of the persistent factors for Mycobacterium tuberculosis (MTB) to survive during the dormant phase. In addition, the absence of ICL in human has made ICL a potential drug target for TB therapy. However, the dynamic details of ICL which could give insights to the ICL-ligand interaction have yet to be solved. Therefore, a series of ICL dimer dynamics studies through molecular dynamics simulation were performed in this work. The ICL active site entrance gate closure is contributed to by hydrogen bonding and electrostatic interactions with the C-terminal. Analysis suggested that the open-closed behavior of the ICL active site entrance depends on the type of ligand present in the active site. We also observed four residues (Ser91, Asp108, Asp153, and Cys191) which could possibly be the nucleophiles for nucleophilic attack on the cleavage of isocitrate at the C2-C3bond. We hope that the elucidation of ICL dynamics can benefit future works such as lead identification or antibody design against ICL for TB therapeutics.
  3. Abdo A, Chen B, Mueller C, Salim N, Willett P
    J Chem Inf Model, 2010 Jun 28;50(6):1012-20.
    PMID: 20504032 DOI: 10.1021/ci100090p
    A Bayesian inference network (BIN) provides an interesting alternative to existing tools for similarity-based virtual screening. The BIN is particularly effective when the active molecules being sought have a high degree of structural homogeneity but has been found to perform less well with structurally heterogeneous sets of actives. In this paper, we introduce an alternative network model, called a Bayesian belief network (BBN), that seeks to overcome this limitation of the BIN approach. Simulated virtual screening experiments with the MDDR, WOMBAT and MUV data sets show that the BIN and BBN methods allow effective screening searches to be carried out. However, the results obtained are not obviously superior to those obtained using a much simpler approach that is based on the use of the Tanimoto coefficient and of the square roots of fragment occurrence frequencies.
  4. Cheng Z, Hwang SS, Bhave M, Rahman T, Chee Wezen X
    J Chem Inf Model, 2023 Nov 13;63(21):6912-6924.
    PMID: 37883148 DOI: 10.1021/acs.jcim.3c01252
    Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.
  5. Aljabal G, Teh AH, Yap BK
    J Chem Inf Model, 2023 Sep 11;63(17):5619-5630.
    PMID: 37606921 DOI: 10.1021/acs.jcim.3c00791
    14-3-3σ plays an important role in controlling tumor metabolic reprogramming and cancer cell growth. However, its function is often compromised in many cancers due to its downregulation. Previous studies found that homodimerization of 14-3-3σ is critical for its activity. However, to date, it is not known if stabilization of 14-3-3σ homodimers can improve its activity or prevent its degradation. In our previous work, we have showed that GCP-Lys-OMe is a potential 14-3-3σ homodimer stabilizer. However, its stabilizing effect was not experimentally validated. Therefore, in this study, we have attempted to predict few potential peptides that can stabilize the dimeric form of 14-3-3σ using similar in silico techniques as described previously for GCP-Lys-OMe. Subsequent [1H]-CPMG NMR experiments confirmed the binding of the peptides (peptides 3, 5, 9, and 16) on 14-3-3σ, with peptide 3 showing the strongest binding. Competitive [1H]-CPMG assays further revealed that while peptide 3 does not compete with a 14-3-3σ binding peptide (ExoS) for the protein's amphipathic groove, it was found to improve ExoS binding on 14-3-3σ. When 14-3-3σ was subjected to dynamic light scattering experiments, the 14-3-3σ homodimer was found to undergo dissociation into monomers prior to aggregation. Intriguingly, the presence of peptide 3 increased 14-3-3σ stability against aggregation. Overall, our findings suggest that (1) docking accompanied by MD simulations can be used to identify potential homodimer stabilizing compounds of 14-3-3σ and (2) peptide 3 can slow down 14-3-3σ aggregation (presumably by preventing its dissociation into monomers), as well as improving the binding of 14-3-3σ to ExoS protein.
  6. Tong M, Liu P, Li C, Zhang Z, Sun W, Dong P, et al.
    J Chem Inf Model, 2024 Feb 12;64(3):785-798.
    PMID: 38262973 DOI: 10.1021/acs.jcim.3c01584
    The allosteric modulation of the homodimeric H10-03-6 protein to glycan ligands L1 and L2, and the STAB19 protein to glycan ligands L3 and L4, respectively, has been studied by molecular dynamics simulations and free energy calculations. The results revealed that the STAB19 protein has a significantly higher affinity for L3 (-11.38 ± 2.32 kcal/mol) than that for L4 (-5.51 ± 1.92 kcal/mol). However, the combination of the H10-03-6 protein with glycan L2 (1.23 ± 6.19 kcal/mol) is energetically unfavorable compared with that of L1 (-13.96 ± 0.35 kcal/mol). Further, the binding of glycan ligands L3 and L4 to STAB19 would result in the significant closure of the two CH2 domains of the STAB19 conformation with the decrease of the centroid distances between the two CH2 domains compared with the H10-03-6/L1/L2 complex. The CH2 domain closure of STAB19 relates directly to the formation of new hydrogen bonds and hydrophobic interactions between the residues Ser239, Val240, Asp265, Glu293, Asn297, Thr299, Ser337, Asp376, Thr393, Pro395, and Pro396 in STAB19 and glycan ligands L3 and L4, which suggests that these key residues would contribute to the specific regulation of STAB19 to L3 and L4. In addition, the distance analysis revealed that the EF loop in the H10-03-6/L1/L2 model presents a high flexibility and partial disorder compared with the stabilized STAB19/L3/L4 complex. These results will be helpful in understanding the specific regulation through the asymmetric structural characteristics in the CH2 and CH3 domains of the H10-03-6 and STAB19 proteins.
  7. Chee Wezen X, Chandran A, Eapen RS, Waters E, Bricio-Moreno L, Tosi T, et al.
    J Chem Inf Model, 2022 May 23;62(10):2586-2599.
    PMID: 35533315 DOI: 10.1021/acs.jcim.2c00300
    Lipoteichoic acid synthase (LtaS) is a key enzyme for the cell wall biosynthesis of Gram-positive bacteria. Gram-positive bacteria that lack lipoteichoic acid (LTA) exhibit impaired cell division and growth defects. Thus, LtaS appears to be an attractive antimicrobial target. The pharmacology around LtaS remains largely unexplored with only two small-molecule LtaS inhibitors reported, namely "compound 1771" and the Congo red dye. Structure-based drug discovery efforts against LtaS remain unattempted due to the lack of an inhibitor-bound structure of LtaS. To address this, we combined the use of a molecular docking technique with molecular dynamics (MD) simulations to model a plausible binding mode of compound 1771 to the extracellular catalytic domain of LtaS (eLtaS). The model was validated using alanine mutagenesis studies combined with isothermal titration calorimetry. Additionally, lead optimization driven by our computational model resulted in an improved version of compound 1771, namely, compound 4 which showed greater affinity for binding to eLtaS than compound 1771 in biophysical assays. Compound 4 reduced LTA production in S. aureus dose-dependently, induced aberrant morphology as seen for LTA-deficient bacteria, and significantly reduced bacteria titers in the lung of mice infected with S. aureus. Analysis of our MD simulation trajectories revealed the possible formation of a transient cryptic pocket in eLtaS. Virtual screening (VS) against the cryptic pocket led to the identification of a new class of inhibitors that could potentiate β-lactams against methicillin-resistant S. aureus. Our overall workflow and data should encourage further drug design campaign against LtaS. Finally, our work reinforces the importance of considering protein conformational flexibility to a successful VS endeavor.
  8. Ikram NK, Durrant JD, Muchtaridi M, Zalaludin AS, Purwitasari N, Mohamed N, et al.
    J Chem Inf Model, 2015 Feb 23;55(2):308-16.
    PMID: 25555059 DOI: 10.1021/ci500405g
    Recent outbreaks of highly pathogenic and occasional drug-resistant influenza strains have highlighted the need to develop novel anti-influenza therapeutics. Here, we report computational and experimental efforts to identify influenza neuraminidase inhibitors from among the 3000 natural compounds in the Malaysian-Plants Natural-Product (NADI) database. These 3000 compounds were first docked into the neuraminidase active site. The five plants with the largest number of top predicted ligands were selected for experimental evaluation. Twelve specific compounds isolated from these five plants were shown to inhibit neuraminidase, including two compounds with IC50 values less than 92 μM. Furthermore, four of the 12 isolated compounds had also been identified in the top 100 compounds from the virtual screen. Together, these results suggest an effective new approach for identifying bioactive plant species that will further the identification of new pharmacologically active compounds from diverse natural-product resources.
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