Supplementary Table 1 from (3-Chloroacetyl)-indole, a Novel Allosteric AKT Inhibitor, Suppresses Colon Cancer Growth In Vitro and In Vivo

This file contains 7 Supplementary Figures and associated legends: Supplementary Figure 1. Characterization of the antibody to detect phosphorylated Ser250of PRPK (related to Figure 1B, upper). Supplementary Figure 2. PRPK induces metastasis of colon cancer cells to the liver. Supplementary Figure 3. PRPK induces lung metastasis of human HCT116 or mouse CT26 colon cancer cells. Supplementary Figure 4. EGF induces phosphorylation of PRPK and surviving ex vivo and the combination of 5-FU and FA show synergistic effects against colon cancer growth. Supplementary Figure 5. Pre-treatment with FA combined with 5-FU suppresses colon lung metastasis in mice. Supplementary Figure 6. FA combined with 5-FU suppresses colon lung metastasis in mice Supplementary Figure 7. PRPK regulated colon cancer metastasis independently of p53 status.

Recently, the Markov state model has been applied for kinetic analysis of molecular dynamics simulations. However, discretization of the conformational space remains a primary challenge in model building, and it is not clear how the space decomposition by distinct clustering strategies exerts influence on the model output. In this work, different clustering algorithms are employed to partition the conformational space sampled in opening and closing of fatty acid binding protein 4 as well as inactivation and activation of the epidermal growth factor receptor. Various classifications are achieved, and Markov models are set up accordingly. On the basis of the models, the total net flux and transition rate are calculated between two distinct states. Our results indicate that geometric and kinetic clustering perform equally well. The construction and outcome of Markov models are heavily dependent on the data traits. Compared to other methods, a combination of Bayesian and hierarchical clustering is feasible in identification of metastable states.

BACKGROUND:Multiple sclerosis (MS) is an autoimmune disease in which dysregulated immune cells attack myelin in the central nervous system (CNS), leading to irreversible neuronal degeneration. Our previous studies have demonstrated that epidermal fatty acid binding protein (E-FABP), widely expressed in immune cells, in particular in dendritic cells (DCs) and T lymphocytes, fuels the overactive immune responses in the mouse model of experimental autoimmune encephalomyelitis (EAE).METHODS:In the present study, we conducted an intensive computational docking analysis to identify novel E-FABP inhibitors for regulation of immune cell functions and for treatment of EAE.RESULTS:We demonstrate that compound [2-(4-acetylphenoxy)-9,10-dimethoxy-6,7-dihydropyrimido[6,1-a]isoquinolin-4-one; designated as EI-03] bound to the lipid binding pocket of E-FABP and enhanced the expression of peroxisome proliferator-activating receptor (PPAR) γ. Further in vitro experiments showed that EI-03 regulated DC functions by inhibition of TNFα production while promoting IL-10 secretion. Moreover, EI-03 treatment counterregulated T cell balance by decreasing effector T cell differentiation (e.g. Th17, Th1) while increasing regulatory T cell development. Most importantly, mice treated with this newly identified compound exhibited reduced clinical symptoms of EAE in mouse models.CONCLUSIONS:Taken together, we have identified a new compound which displays a potential therapeutic benefit for treatment of MS by targeting E-FABP.

Our previous studies have demonstrated that expression of epidermal fatty acid binding protein (E-FABP) in tumor associated macrophages (TAMs) promotes macrophage anti-tumor activity by enhancing IFNβ responses in tumor models. Thus, E-FABP represents a new protective factor in enhancing tumor immune surveillance against tumor development. Herein, we report the compound 5-(benzylamino)-2-(3-methylphenyl)-1,3-oxazole-4-carbonitrile (designated EI-05) as a novel E-FABP activator for inhibition of mammary tumor growth. EI-05 was selected from the ZINC compound library using molecular docking analysis based on the crystal structure of E-FABP. Although EI-05 is unable to bind E-FABP directly, it significantly increases E-FABP expression in macrophages during inflammation. Stimulation of macrophages with EI-05 remarkably enhances lipid droplet formation and IFNβ production, which further promotes the anti-tumor activity of macrophages. Importantly, administering EI-05 in vivo significantly inhibits mammary tumor growth in a syngeneic mouse model. Altogether, these results suggest that EI-05 may represent a promising drug candidate for anti-tumor treatment through enhancing E-FABP activity and IFNβ responses in macrophages.

In this work, we investigate the dynamic motions of fatty acid binding protein 4 (FABP4) in the absence and presence of a ligand by explicitly solvated all-atom molecular dynamics simulations. The dynamics of one ligand-free FABP4 and four ligand-bound FABP4s is compared via multiple 1.2 μs simulations. In our simulations, the protein interconverts between the open and closed states. Ligand-free FABP4 prefers the closed state, whereas ligand binding induces a conformational transition to the open state. Coupled with opening and closing of FABP4, the ligand adopts distinct binding modes, which are identified and compared with crystal structures. The concerted dynamics of protein and ligand suggests that there may exist multiple FABP4-ligand binding conformations. Thus, this work provides details about how ligand binding affects the conformational preference of FABP4 and how ligand binding is coupled with a conformational change of FABP4 at an atomic level.

The epidermal growth factor receptor (EGFR) is aberrantly activated in various cancer cells and an important target for cancer treatment. Deep understanding of EGFR conformational changes between the active and inactive states is of pharmaceutical interest. Here we present a strategy combining multiply targeted molecular dynamics simulations, unbiased molecular dynamics simulations, and Bayesian clustering to investigate transition pathways during the activation/inactivation process of EGFR kinase domain. Two distinct pathways between the active and inactive forms are designed, explored, and compared. Based on Bayesian clustering and rough two-dimensional free energy surfaces, the energy-favorable pathway is recognized, though DFG-flip happens in both pathways. In addition, another pathway with different intermediate states appears in our simulations. Comparison of distinct pathways also indicates that disruption of the Lys745-Glu762 interaction is critically important in DFG-flip while movement of the A-loop significantly facilitates the conformational change. Our simulations yield new insights into EGFR conformational transitions. Moreover, our results verify that this approach is valid and efficient in sampling of protein conformational changes and comparison of distinct pathways.