Calculation of relative binding affinities of fructose 1,6-bisphosphatase mutants with adenosine monophosphate using free energy perturbation method

In Silico Screening and Identification of Antidiabetic Inhibitors Sourced from Phytochemicals of Philippine Plants against Four Protein Targets of Diabetes (PTP1B, DPP-4, SGLT-2, and FBPase)

Current oral medications for type 2 diabetes target a single main physiological mechanism. They either activate or inhibit receptors to enhance insulin sensitivity, increase insulin secretion, inhibit glucose absorption, or inhibit glucose production. In advanced stages, combination therapy may be required because of the limited efficacy of single-target drugs; however, medications are becoming more costly, and there is also the risk of developing the combined side effects of each drug. Thus, identifying a multi-target drug may be the best strategy to improve treatment efficacy. This study sees the potential of 2657 Filipino phytochemicals as a source of natural inhibitors against four targets of diabetes: PTP1B, DPP-4, SGLT-2, and FBPase. Different computer-aided drug discovery techniques, including ADMET profiling, DFT optimization, molecular docking, MD simulations, and MM/PBSA energy calculations, were employed to elucidate the stability and determine the binding affinity of the candidate ligands. Through in silico methods, we have identified seven potential natural inhibitors against PTP1B, DPP-4, and FBPase, and ten against SGLT-2. Eight plants containing at least one natural inhibitor of each protein target were also identified. It is recommended to further investigate the plants' potential to be transformed into a safe and scientifically validated multi-target drug for diabetes therapies.

In silico study of selective inhibition mechanism of S-adenosyl-L-methionine analogs for human DNA methyltransferase 3A

Epigenetic mechanisms leading to transcriptional regulation, including DNA methylation, are frequently dysregulated in diverse cancers. Interfering with aberrant DNA methylation performed by DNA cytosine methyltransferases (DNMTs) is a clinically validated approach. In particular, the selective inhibition of the de novo DNMT3A and DNMT3B enzymes, whose expression is limited to early embryogenesis, adult stem cells, and in cancers, is particularly attractive; such selectivity is likely to attenuate the dose limiting toxicity shown by current, non-selective DNMT inhibitors. We use molecular dynamics (MD) based computational analysis to study known small molecule binders of DNMT3A, then propose reversible, tight binding, and selective inhibitors that exploit the Asn¹¹⁹²/Arg⁶⁸⁸ difference between the maintenance DNMT1 and DNMT3A near the active site. A similar strategy exploiting the presence of a unique active site cysteine Cys⁶⁶⁶ is used to propose DNMT3A-selective irreversible inhibitors. We report our results of relative binding energies of the known and proposed compounds estimated using MM/GBSA and umbrella sampling (US) techniques, and our evaluation of other end-point binding free energy calculation methods for these receptors. These calculations offer insight into the potential for small molecules to selectively target the active site of DNMT3A.

Exploration of N-Arylsulfonyl-indole-2-carboxamide Derivatives as Novel Fructose-1,6-bisphosphatase Inhibitors by Molecular Simulation

A series of N-arylsulfonyl-indole-2-carboxamide derivatives have been identified as potent fructose-1,6-bisphosphatase (FBPase) inhibitors (FBPIs) with excellent selectivity for the potential therapy of type II diabetes mellitus. To explore the structure–activity relationships (SARs) and the mechanisms of action of these FBPIs, a systematic computational study was performed in the present study, including three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, pharmacophore modeling, molecular dynamics (MD), and virtual screening. The constructed 3D-QSAR models exhibited good predictive ability with reasonable parameters using comparative molecular field analysis (q² = 0.709, R² = 0.979, r_pre² = 0.932) and comparative molecular similarity indices analysis (q² = 0.716, R² = 0.978, r_pre² = 0.890). Twelve hit compounds were obtained by virtual screening using the best pharmacophore model in combination with molecular dockings. Three compounds with relatively higher docking scores and better ADME properties were then selected for further studies by docking and MD analyses. The docking results revealed that the amino acid residues Met18, Gly21, Gly26, Leu30, and Thr31 at the binding site were of great importance for the effective bindings of these FBPIs. The MD results indicated that the screened compounds VS01 and VS02 could bind with FBPase stably as its cognate ligand in dynamic conditions. This work identified several potential FBPIs by modeling studies and might provide important insights into developing novel FBPIs.

Accurate calculation of mutational effects on the thermodynamics of inhibitor binding to p38α MAP kinase: a combined computational and experimental study

A major current challenge for drug design efforts focused on protein kinases is the development of drug resistance caused by spontaneous mutations in the kinase catalytic domain. The ubiquity of this problem means that it would be advantageous to develop fast, effective computational methods that could be used to determine the effects of potential resistance-causing mutations before they arise in a clinical setting. With this long-term goal in mind, we have conducted a combined experimental and computational study of the thermodynamic effects of active-site mutations on a well-characterized and high-affinity interaction between a protein kinase and a small-molecule inhibitor. Specifically, we developed a fluorescence-based assay to measure the binding free energy of the small-molecule inhibitor, SB203580, to the p38α MAP kinase and used it measure the inhibitor's affinity for five different kinase mutants involving two residues (Val38 and Ala51) that contact the inhibitor in the crystal structure of the inhibitor-kinase complex. We then conducted long, explicit-solvent thermodynamic integration (TI) simulations in an attempt to reproduce the experimental relative binding affinities of the inhibitor for the five mutants; in total, a combined simulation time of 18.5 μs was obtained. Two widely used force fields - OPLS-AA/L and Amber ff99SB-ILDN - were tested in the TI simulations. Both force fields produced excellent agreement with experiment for three of the five mutants; simulations performed with the OPLS-AA/L force field, however, produced qualitatively incorrect results for the constructs that contained an A51V mutation. Interestingly, the discrepancies with the OPLS-AA/L force field could be rectified by the imposition of position restraints on the atoms of the protein backbone and the inhibitor without destroying the agreement for other mutations; the ability to reproduce experiment depended, however, upon the strength of the restraints' force constant. Imposition of position restraints in corresponding simulations that used the Amber ff99SB-ILDN force field had little effect on their ability to match experiment. Overall, the study shows that both force fields can work well for predicting the effects of active-site mutations on small molecule binding affinities and demonstrates how a direct combination of experiment and computation can be a powerful strategy for developing an understanding of protein-inhibitor interactions.

An analysis of hydrophobic interactions of thymidylate synthase with methotrexate: free energy calculations involving mutant and native structures bound to methotrexate

Since the human body for many reasons can adapt and become resistant to drugs, it is important to develop and validate computer aided drug design (CADD) methods that could help predict binding affinity changes that can result from these resistant enzymes. The free energy perturbation (FEP) methodology is the most accurate means of estimating relative binding affinities between inhibitors and protein variants. In this paper, we describe the role played by hydrophobic residues lining the active site region, particularly (79)Ile and (176)Phe, in the binding of methotrexate to the Escherichia coli (E. coli) thymidylate synthase (TS) enzyme, using the thermodynamic cycle perturbation (TCP) approach. The computed binding free energy differences on the binding of methotrexate to the native and some mutant E. coli TS structures have been compared with experimental results. Computationally, four different 'mutations' have been simulated on the TS enzyme with methotrexate (MTX): (79)Ile --> (79)Val; (79)Ile --> (79)Ala; (79)Ile --> (79)Leu; and (176)Phe --> (176)Ile. The calculated results indicate that in each of these cases, the native residues ((79) Ile and (176) Phe) interact more favorably with methotrexate than the mutant residues and these results are corroborated by experimental measurements. Binding preference to wild type residues can be rationalized in terms of their better hydrophobic contacts with the phenyl ring of methotrexate.

Application of a polarizable force field to calculations of relative protein-ligand binding affinities

An explicitly polarizable force field based exclusively on quantum data is applied to calculations of relative binding affinities of ligands to proteins. Five ligands, differing by replacement of an atom or functional group, in complexes with three serine proteases-trypsin, thrombin, and urokinase-type plasminogen activator-with available experimental binding data are used as test systems. A special protocol of thermodynamic integration was developed and used to provide sufficiently low levels of systematic error along with high numerical efficiency and statistical stability. The calculated results are in excellent quantitative (rmsd = 1.0 kcal/mol) and qualitative (R(2) = 0.90) agreement with experimental data. The potential of the methodology to explain the observed differences in the ligand affinities is also demonstrated.