Membrane and Protein Interactions of the Pleckstrin Homology Domain Superfamily.

The human genome encodes about 285 proteins that contain at least one annotated pleckstrin homology (PH) domain. As the first phosphoinositide binding module domain to be discovered, the PH domain recruits diverse protein architectures to cellular membranes. PH domains constitute one of the largest protein superfamilies, and have diverged to regulate many different signaling proteins and modules such as Dbl homology (DH) and Tec homology (TH) domains. The ligands of approximately 70 PH domains have been validated by binding assays and complexed structures, allowing meaningful extrapolation across the entire superfamily. Here the Membrane Optimal Docking Area (MODA) program is used at a genome-wide level to identify all membrane docking PH structures and map their lipid-binding determinants. In addition to the linear sequence motifs which are employed for phosphoinositide recognition, the three dimensional structural features that allow peripheral membrane domains to approach and insert into the bilayer are pinpointed and can be predicted ab initio. The analysis shows that conserved structural surfaces distinguish which PH domains associate with membrane from those that do not. Moreover, the results indicate that lipid-binding PH domains can be classified into different functional subgroups based on the type of membrane insertion elements they project towards the bilayer

Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles

Nuclear receptors (NRs) are ligand dependent transcriptional factors and play a key role in reproduction, development, and homeostasis of organism. NRs are potential targets for treatment of cancer and other diseases such as inflammatory diseases, and diabetes. In this study, we present a comprehensive library of pocket conformational ensembles of thirteen human nuclear receptors (NRs), and test the ability of these ensembles to recognize their ligands in virtual screening, as well as predict their binding geometry, functional type, and relative binding affinity. 157 known NR modulators and 66 structures were used as a benchmark. Our pocket ensemble library correctly predicted the ligand binding poses in 94% of the cases. The models were also highly selective for the active ligands in virtual screening, with the areas under the ROC curves ranging from 82 to a remarkable 99%. Using the computationally determined receptor-specific binding energy offsets, we showed that the ensembles can be used for predicting selectivity profiles of NR ligands. Our results evaluate and demonstrate the advantages of using receptor ensembles for compound docking, screening, and profiling

Tyrosine-Based Signals Regulate the Assembly of Daple⋅PARD3 Complex at Cell-Cell Junctions.

Polarized distribution of organelles and molecules inside a cell is vital for a range of cellular processes and its loss is frequently encountered in disease. Polarization during planar cell migration is a special condition in which cellular orientation is triggered by cell-cell contact. We demonstrate that the protein Daple (CCDC88C) is a component of cell junctions in epithelial cells which serves like a cellular "compass" for establishing and maintaining contact-triggered planar polarity. Furthermore, these processes may be mediated through interaction with the polarity regulator PARD3. This interaction, mediated by Daple's PDZ-binding motif (PBM) and the third PDZ domain of PARD3, is fine-tuned by tyrosine phosphorylation on Daple's PBM by receptor and non-receptor tyrosine kinases, such as Src. Hypophosphorylation strengthens the interaction, whereas hyperphosphorylation disrupts it, thereby revealing an unexpected role of Daple as a platform for signal integration and gradient sensing for tyrosine-based signals within the planar cell polarity pathway

Lapatinib-binding protein kinases in the African trypanosome: identification of cellular targets for kinase-directed chemical scaffolds.

Human African trypanosomiasis is caused by the eukaryotic microbe Trypanosoma brucei. To discover new drugs against the disease, one may use drugs in the clinic for other indications whose chemical scaffolds can be optimized via a medicinal chemistry campaign to achieve greater potency against the trypanosome. Towards this goal, we tested inhibitors of human EGFR and/or VEGFR as possible anti-trypanosome compounds. The 4-anilinoquinazolines canertinib and lapatinib, and the pyrrolopyrimidine AEE788 killed bloodstream T. brucei in vitro with GI(50) in the low micromolar range. Curiously, the genome of T. brucei does not encode EGFR or VEGFR, indicating that the drugs recognize alternate proteins. To discover these novel targets, a trypanosome lysate was adsorbed to an ATP-sepharose matrix and washed with a high salt solution followed by nicotinamide adenine dinucleotide (NAD(+)). Proteins that remained bound to the column were eluted with drugs, and identified by mass spectrometry/bioinformatics. Lapatinib bound to Tb927.4.5180 (termed T. brucei lapatinib-binding protein kinase-1 (TbLBPK1)) while AEE788 bound Tb927.5.800 (TbLBPK2). When the NAD(+) wash was omitted from the protocol, AEE788, canertinib and lapatinib eluted TbLBPK1, TbLBPK2, and Tb927.3.1570 (TbLBPK3). In addition, both canertinib and lapatinib eluted Tb10.60.3140 (TbLBPK4), whereas only canertinib desorbed Tb10.61.1880 (TbCBPK1). Lapatinib binds to a unique conformation of protein kinases. To gain insight into the structural basis for lapatinib interaction with TbLBPKs, we constructed three-dimensional models of lapatinib•TbLBPK complexes, which confirmed that TbLBPKs can adopt lapatinib-compatible conformations. Further, lapatinib, AEE788, and canertinib were docked to TbLBPKs with favorable scores. Our studies (a) present novel targets of kinase-directed drugs in the trypanosome, and (b) offer the 4-anilinoquinazoline and pyrrolopyrimidines as scaffolds worthy of medicinal chemistry and structural biology campaigns to develop them into anti-trypanosome drugs

Structural basis for activation of trimeric Gi proteins by multiple growth factor receptors via GIV/Girdin.

A long-standing issue in the field of signal transduction is to understand the cross-talk between receptor tyrosine kinases (RTKs) and heterotrimeric G proteins, two major and distinct signaling hubs that control eukaryotic cell behavior. Although stimulation of many RTKs leads to activation of trimeric G proteins, the molecular mechanisms behind this phenomenon remain elusive. We discovered a unifying mechanism that allows GIV/Girdin, a bona fide metastasis-related protein and a guanine-nucleotide exchange factor (GEF) for Gαi, to serve as a direct platform for multiple RTKs to activate Gαi proteins. Using a combination of homology modeling, protein-protein interaction, and kinase assays, we demonstrate that a stretch of ∼110 amino acids within GIV C-terminus displays structural plasticity that allows folding into a SH2-like domain in the presence of phosphotyrosine ligands. Using protein-protein interaction assays, we demonstrated that both SH2 and GEF domains of GIV are required for the formation of a ligand-activated ternary complex between GIV, Gαi, and growth factor receptors and for activation of Gαi after growth factor stimulation. Expression of a SH2-deficient GIV mutant (Arg 1745→Leu) that cannot bind RTKs impaired all previously demonstrated functions of GIV-Akt enhancement, actin remodeling, and cell migration. The mechanistic and structural insights gained here shed light on the long-standing questions surrounding RTK/G protein cross-talk, set a novel paradigm, and characterize a unique pharmacological target for uncoupling GIV-dependent signaling downstream of multiple oncogenic RTKs

Nilotinib, an approved leukemia drug, inhibits smoothened signaling in Hedgehog-dependent medulloblastoma.

Dysregulation of the seven-transmembrane (7TM) receptor Smoothened (SMO) and other components of the Hedgehog (Hh) signaling pathway contributes to the development of cancers including basal cell carcinoma (BCC) and medulloblastoma (MB). However, SMO-specific antagonists produced mixed results in clinical trials, marked by limited efficacy and high rate of acquired resistance in tumors. Here we discovered that Nilotinib, an approved inhibitor of several kinases, possesses an anti-Hh activity, at clinically achievable concentrations, due to direct binding to SMO and inhibition of SMO signaling. Nilotinib was more efficacious than the SMO-specific antagonist Vismodegib in inhibiting growth of two Hh-dependent MB cell lines. It also reduced tumor growth in subcutaneous MB mouse xenograft model. These results indicate that in addition to its known activity against several tyrosine-kinase-mediated proliferative pathways, Nilotinib is a direct inhibitor of the Hh pathway. The newly discovered extension of Nilotinib's target profile holds promise for the treatment of Hh-dependent cancers

Homology modeling and ligand docking of Mitogen-activated protein kinase-activated protein kinase 5 (MK5)

Background: Mitogen-activated protein kinase-activated protein kinase 5 (MK5) is involved in one of the major signaling pathways in cells, the mitogen-activated protein kinase pathway. MK5 was discovered in 1998 by the groups of Houng Ni and Ligou New, and was found to be highly conserved throughout the vertebrates. Studies, both in vivo and in vitro, have shown that it is implicated in tumor suppression as well as tumor promotion, embryogenesis, anxiety, locomotion, cell motility and cell cycle regulation. Methods: In order to obtain a molecular model of MK5 that can be used as a working tool for development of chemical probes, three MK5 models were constructed and refined based on three different known crystal structures of the closely related MKs; MK2 [PDB: 2OZA and PDB: 3M2W] and MK3 [PDB: 3FHR]. The main purpose of the present MK5 molecular modeling study was to identify the best suited template for making a MK5 model. The ability of the generated models to effectively discriminate between known inhibitors and decoys was analyzed using receiver operating characteristic (ROC) curves. Results: According to the ROC curve analyzes, the refined model based on 3FHR was most effective in discrimination between known inhibitors and decoys. Conclusions: The 3FHR-based MK5 model may serve as a working tool for development of chemical probes using computer aided drug design. The biological function of MK5 still remains elusive, but its role as a possible drug target may be elucidated in the near future

Pocketome: an encyclopedia of small-molecule binding sites in 4D

The importance of binding site plasticity in protein–ligand interactions is well-recognized, and so are the difficulties in predicting the nature and the degree of this plasticity by computational means. To assist in understanding the flexible protein–ligand interactions, we constructed the Pocketome, an encyclopedia of about one thousand experimentally solved conformational ensembles of druggable binding sites in proteins, grouped by location and consistent chain/cofactor composition. The multiplicity of pockets within the ensembles adds an extra, fourth dimension to the Pocketome entry data. Within each ensemble, the pockets were carefully classified by the degree of their pairwise similarity and compatibility with different ligands. The core of the Pocketome is derived regularly and automatically from the current releases of the Protein Data Bank and the Uniprot Knowledgebase; this core is complemented by entries built from manually provided seed ligand locations. The Pocketome website (www.pocketome.org) allows searching for the sites of interest, analysis of conformational clusters, important residues, binding compatibility matrices and interactive visualization of the ensembles using the ActiveICM web browser plugin. The Pocketome collection can be used to build multi-conformational docking and 3D activity models as well as to design cross-docking and virtual ligand screening benchmarks

PIER: protein interface recognition for structural proteomics

Abstract Recent advances in structural proteomics call for development of fast and reliable automatic methods for prediction of functional surfaces of proteins with known three-dimensional structure, including binding sites for known and unknown protein partners as well as oligomerization interfaces. Despite significant progress the problem is still far from being solved. Most existing methods rely, at least partially, on evolutionary information from multiple sequence alignments (MSA) projected on protein surface. The common drawback of such methods is their limited applicability to the proteins with a sparse set of sequential homologs, as well as inability to detect interfaces in evolutionary variable regions. In this study, we developed an improved method for predicting interfaces from a single protein structure, that is based on local statistical properties of the protein surface derived at the level of atomic groups. It was also demonstrated that the evolutionary conservation signal only marginally influenced the overall prediction performance on a diverse benchmark; moreover, for certain classes of proteins, using this signal actually resulted in a deteriorated prediction. The proposed Protein IntErface Recognition method (PIER) yielded improved performance as compared to several alignment-free or alignment-dependent predictions. PIER achieved the overall precision of 60% at the recall threshold of 50% at the residue level on a benchmark of 490 homodimeric, 62 heterodimeric and 196 transient interfaces. For 696 of 748 proteins (93%) the binding patch residues were successfully detected with precision exceeding 25% at 50% recall; for 524 proteins (70%) the corresponding precision was above 50%. The calculation only took seconds for an average 300-residue protein. The accuracy, efficiency, and dependence on structure alone make PIER a suitable tool for automated high-throughput annotation of protein structures emerging from structural proteomics projects