Succinimides

Molecular mdelingThe chemical structures of inhibitors, shown in Table 1 were designed using Hyperchem software (version 7, Hypercube Inc.). Conformational analysis of the desired compounds was performed through Semi-empirical molecular orbital calculations (PM3) method using HYPERCHEM software. The molecular structures were optimized using Polak-Ribiere (conjugate gradient) algorithm until the root mean square (RMS) gradient was 0.01 kcal mol^-1. Among all energy minima conformers, the global minimum of compounds were used in docking calculations and the resulted geometry was transferred into Autodock (version 4.2) program package, which was developed by Arthur J. Olson Chemometrics Group (12 (link)). The structure of docked N-phenyl substituent of phthalimide (1 (link)-16 (link)), N-[3-methyl-(2-pyridinyl)] phthalimide (19) and N-(3-amino-2-methylphenyl) succinimide (20) are shown in Table 1.
DockingDocking calculations were performed using Autodock software (version 4.2). A model of Na channel open pore was used as a receptor. This open pore model was developed based on homology model of the crystal structures of K channels (11 (link)). The model constructed by homology with potassium channel structures was reasonably successful in accounting for inner pore residue interactions with local anesthetics and anticonvulsant drugs like phenytoin. Desired compounds were docked into the active site as well as phenytoin which were acting as our reference drug and validation of our technique.
Docking was done using AutoDock4.2, in order to assign the perfect grid of each ligand, grid box values were obtained from trial and error and previous studies (13 (link)-15 (link)). Grid maps with 60×60×60 points were constructed and the grid point spacing was 0.375 Å (16 (link)). The implementing Lamarckian Genetic Algorithm (LGA), considered as one of the best docking methods available in AutoDock, was adopted to perform the molecular docking studies. The parameters for LGA were defined as follows: a maximum number of 250,000 energy evaluations; a maximum number of generations of 27,000; and mutation and crossover rates of 0.02 and 0.8, respectively. Pseudo-Solis & Wets parameters were used for local search, and 300 iterations of Solis & Wets local search were imposed. Both Autogrid and Autodock computations were performed on Cygwin and ten independent docking runs were performed for each phthalimide. Final docked conformations were clustered using a tolerance of 1 A ˚ root mean square deviation (RMSD) and the docking log (dlg) files were analyzed using the AutoDock Tools, graphical user interface of Autodock. The docked conformations of each ligand were ranked into clusters based on the binding energy and the top ranked conformations were visually analyzed. Hydrogen bonding and hydrophobic interactions between docked potent agents and macromolecule were analyzed using Auto Dock Tools (version1.50).

The peptides J8 (QAEDKVKQSREAKKQVEKALKQLEDKVQ) and K4S2 (KKKKNSDNIKENQFEDFDEDWENF) were synthesized by China Peptides Co. Ltd. (Shanghai, China). Each peptide was conjugated to DT via a C-terminal cysteine residue using 6′-maleimido-caproyl N-hydroxy succinimide, as described elsewhere (44 ).
The vaccine formulations were prepared fresh before each immunization by adsorbing DT-conjugated antigen with Alhydrogel (Alum; Brenntag Biosector, Denmark) as previously described (19 (link)). BALB/c mice were immunized with J8CombiVax (J8-DT+K4S2-DT/Alum) on days 0, 21, and 28 as previously described (11 (link)). Each mouse received 60 μg total vaccine formulation (30 μg J8 and 30 μg K4S2) per immunization. Control mice received adjuvant in PBS. Two weeks after the final immunization, mice were infected with Strep A via the superficial skin infection method.

We used causal mediation analysis to evaluate whether our previously reported body composition-^{30 (link)} and habitual food intake-related metabolites^{31 (link)} mediate the association of body composition and habitual food intake with clinical biomarkers. For the first, BMI and BF were the exposure and the clinical biomarker (BP, IL-6, IL-18, CRP, Adiponectin, leptin, total cholesterol, HDL, LDL, and triglyceride levels) were the outcomes. The 19 (5-dodecenoylcarnitine (C12:1), 7-hydroxyindole sulfate, decanoylcarnitine (C10), formiminoglutamate, glucuronide of C10H18O2 (12), guanidinosuccinate, isobutyrylglycine (C4), isovalerylglycine, nicotinamide N-oxide, proline, succinimide, thymine, tigloylglycine, X—12839, X—21441, X—21851, X—24469, X—24801, and X—25003) BMI-associated metabolites and 20 (3-methylcrotonylglycine, glucuronide of C10H18O2 (12), glutamine conjugate of C8H12O2 (1), glycine conjugate of C10H14O2 (1), guanidinosuccinate, isobutyrylglycine (C4), isovalerylglutamine, isovalerylglycine, nicotinamide N-oxide, succinimide, tigloylglycine, X—11261, X—15486, X—17676, X—21851, X—24345, X—24350, X—24469, X—24801, X—25442, and X—25464) BF-associated metabolites were considered as mediators. For the second, habitual food intake was the exposure, the aforementioned clinical biomarker markers were the outcomes, and the six (eggs: indole-3-acetamide, N6-methyladenosine; vegetables: hippurate, citraconate/glutaconate, X—12111; processed and other meat: vanillylmandelate (VMA)) food group-associated metabolites were considered as mediators. We used the ‘mediate()’-function in the R package ‘mediation’^{38 (link)} for the analysis. We used 1000 simulations (the recommended default) and quasi-Bayesian approximation to estimate the standard errors. We used the model-based approach^{38 (link)}. The mediator model is the linear regression model that regresses the metabolites on BMI, BF, or habitual food intake adjusted for age at sample collection. The habitual food intake models were additionally adjusted for BMI at sample collection. The outcome model is a linear regression model that regresses clinical biomarker on BMI, BF, or habitual food intake, the mediator (metabolites), and adjustment variables. From these models the causal mediation analysis is performed as described by Imai et al.^{39 (link)}. Briefly, the model estimates the average causal mediation effect (ACME), which is a numeric measure of how much influence the presence of the mediator has on the total effect of the exposure-outcome association, as well as the average direct effect, the average total effect, and the proportion mediated. We corrected for multiple testing by holding the false discovery rate at 5%.