Substrate interaction inhibits γ-secretase production of amyloid-β peptides

Combining NMR, mass spectrometry, AlphaLISA and cell assays, we discovered a compound C1 that binds C-terminal juxtamembrane lysines at the transmembrane domain of the amyloid precursor protein (APPTM) and inhibits γ-secretase production of amyloid-β with μM IC50. Our work suggests that targeting APPTM is a novel and viable strategy in AD drug discovery.This work was supported by a grant from the Warren Alpert Foundation (to C. W.), the NIH grant R21-NS109926 (to C. W.), NIH grants R01-AG008200 and RF1-NS047229 (to N. K. R.), and the NIH grant R35-GM127040 (to Y. Z.)Peer reviewe

Structural insight into TIPE1 functioning as a lipid transfer protein

As a member of the tumor necrosis factor-α-induced protein 8 (TNFAIP8/TIPE) family, TIPE1 has been found to be associated with many cellular signaling pathways in regulating apoptosis, autophagy, and tumorigenesis. However, the position of TIPE1 in the signaling network remains elusive. Here we present the crystal structure of zebrafish TIPE1 in complex with phosphatidylethanolamine (PE) at a resolution of 1.38 Å. By comparison with structures of other three TIPE family proteins, a universal phospholipid-binding mode was proposed. Namely, the hydrophobic cavity binds to fatty acid tails, while ‘X-R-R’ triad nearby the entrance of cavity recognizes the phosphate group head. Using molecular dynamics (MD) simulations, we further elaborated the mechanism of how the lysine-rich N-terminal domain assisting TIPE1 to favorably bind to phosphatidylinositol (PI). Beside small molecule substrate, we identified Gαi3 as a direct-binding partner of TIPE1 using GST pull-down assay and size-exclusion chromatography. Analyses of key-residue mutations and predicted complex structure revealed that the binding mode of TIPE1 to Gαi3 could be non-canonical. In summary, our findings narrowed down TIPE1’s position in Gαi3-related and PI-inducing signaling pathways. Communicated by Ramaswamy H. Sarma</p

DataSheet1_Development of QSRR model for hydroxamic acids using PCA-GA-BP algorithm incorporated with molecular interaction-based features.pdf

As a potent zinc chelator, hydroxamic acid has been applied in the design of inhibitors of zinc metalloenzyme, such as histone deacetylases (HDACs). A series of hydroxamic acids with HDAC inhibitory activities were subjected to the QSRR (Quantitative Structure–Retention Relationships) study. Experimental data in combination with calculated molecular descriptors were used for the development of the QSRR model. Specially, we employed PCA (principal component analysis) to accomplish dimension reduction of descriptors and utilized the principal components of compounds (16 training compounds, 4 validation compounds and 7 test compounds) to execute GA (genetic algorithm)-BP (error backpropagation) algorithm. We performed double cross-validation approach for obtaining a more convincing model. Moreover, we introduced molecular interaction-based features (molecular docking scores) as a new type of molecular descriptor to represent the interactions between analytes and the mobile phase. Our results indicated that the incorporation of molecular interaction-based features significantly improved the accuracy of the QSRR model, (R2 value is 0.842, RMSEP value is 0.440, and MAE value is 0.573). Our study not only developed QSRR model for the prediction of the retention time of hydroxamic acid in HPLC but also proved the feasibility of using molecular interaction-based features as molecular descriptors.</p

Protein Flexibility in Docking-Based Virtual Screening: Discovery of Novel Lymphoid-Specific Tyrosine Phosphatase Inhibitors Using Multiple Crystal Structures

Incorporating protein flexibility is a major challenge for docking-based virtual screening. With an increasing number of available crystal structures, ensemble docking with multiple protein structures is an efficient approach to deal with protein flexibility. Herein, we report the successful application of a docking-based virtual screen using multiple crystal structures to discover novel inhibitors of lymphoid-specific tyrosine phosphatase (LYP), a potential drug target for autoimmune diseases. The appropriate use of multiple protein structures allowed a better enrichment than a single structure in the recovery of known inhibitors. Subsequently, an optimal ensemble of LYP structures was selected and used in docking-based virtual screening. Eight novel LYP inhibitors (IC₅₀ ranging from 7.95 to 56.6 μM) were identified among 23 hit compounds. Further studies demonstrated that the most active compound <b>B15</b> possessed some selectivity over other protein phosphatases and could effectively up-regulate TCR (T cell receptor)-mediated signaling in Jurkat T cells. These novel hits not only provided good starting points for the development of therapeutic agents useful in autoimmune diseases but also demonstrated the advantages of choosing an appropriate ensemble of protein structures in docking-based virtual screening over using a single protein conformation

Protein Flexibility in Docking-Based Virtual Screening: Discovery of Novel Lymphoid-Specific Tyrosine Phosphatase Inhibitors Using Multiple Crystal Structures

Incorporating protein flexibility is a major challenge for docking-based virtual screening. With an increasing number of available crystal structures, ensemble docking with multiple protein structures is an efficient approach to deal with protein flexibility. Herein, we report the successful application of a docking-based virtual screen using multiple crystal structures to discover novel inhibitors of lymphoid-specific tyrosine phosphatase (LYP), a potential drug target for autoimmune diseases. The appropriate use of multiple protein structures allowed a better enrichment than a single structure in the recovery of known inhibitors. Subsequently, an optimal ensemble of LYP structures was selected and used in docking-based virtual screening. Eight novel LYP inhibitors (IC₅₀ ranging from 7.95 to 56.6 μM) were identified among 23 hit compounds. Further studies demonstrated that the most active compound <b>B15</b> possessed some selectivity over other protein phosphatases and could effectively up-regulate TCR (T cell receptor)-mediated signaling in Jurkat T cells. These novel hits not only provided good starting points for the development of therapeutic agents useful in autoimmune diseases but also demonstrated the advantages of choosing an appropriate ensemble of protein structures in docking-based virtual screening over using a single protein conformation

How to Improve Docking Accuracy of AutoDock4.2: A Case Study Using Different Electrostatic Potentials

Molecular docking, which is the indispensable emphasis in predicting binding conformations and energies of ligands to receptors, constructs the high-throughput virtual screening available. So far, increasingly numerous molecular docking programs have been released, and among them, AutoDock 4.2 is a widely used docking program with exceptional accuracy. It has heretofore been substantiated that the calculation of partial charge is very fundamental for the accurate conformation search and binding energy estimation. However, no systematic comparison of the significances of electrostatic potentials on docking accuracy of AutoDock 4.2 has been determined. In this paper, nine different charge-assigning methods, including AM1-BCC, Del-Re, formal, Gasteiger–Hückel, Gasteiger–Marsili, Hückel, Merck molecular force field (MMFF), and Pullman, as well as the ab initio Hartree–Fock charge, were sufficiently explored for their molecular docking performance by using AutoDock4.2. The results clearly demonstrated that the empirical Gasteiger–Hückel charge is the most applicable in virtual screening for large database; meanwhile, the semiempirical AM1-BCC charge is practicable in lead compound optimization as well as accurate virtual screening for small databases

Enhancing the Sensitivity of Pharmacophore-Based Virtual Screening by Incorporating Customized ZBG Features: A Case Study Using Histone Deacetylase 8

As key regulators of epigenetic regulation, human histone deacetylases (HDACs) have been identified as drug targets for the treatment of several cancers. The proper recognition of zinc-binding groups (ZBGs) will help improve the accuracy of virtual screening for novel HDAC inhibitors. Here, we developed a high-specificity ZBG-based pharmacophore model for HDAC8 inhibitors by incorporating customized ZBG features. Subsequently, pharmacophore-based virtual screening led to the discovery of three novel HDAC8 inhibitors with low micromole IC₅₀ values (1.8–1.9 μM). Further studies demonstrated that compound <b>H8-A5</b> was selective for HDAC8 over HDAC 1/4 and showed antiproliferation activity in MDA-MB-231 cancer cells. Molecular docking and molecular dynamic studies suggested a possible binding mode for <b>H8-A5</b>, which provides a good starting point for the development of HDAC8 inhibitors in cancer treatment