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1.
Proc Natl Acad Sci U S A ; 111(44): 15741-5, 2014 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-25331893

RESUMO

There are many transmembrane receptor-like proteins whose ligands have not been identified. A strategy for finding ligands when little is known about their tissue source is to screen each extracellular protein individually expressed in an array format by using a sensitive functional readout. Taking this approach, we have screened a large collection (3,191 proteins) of extracellular proteins for their ability to activate signaling of an orphan receptor, leukocyte tyrosine kinase (LTK). Only two related secreted factors, FAM150A and FAM150B (family with sequence similarity 150 member A and member B), stimulated LTK phosphorylation. FAM150A binds LTK extracellular domain with high affinity (K(D) = 28 pM). FAM150A stimulates LTK phosphorylation in a ligand-dependent manner. This strategy provides an efficient approach for identifying functional ligands for other orphan receptors.


Assuntos
Citocinas/metabolismo , Proteoma/metabolismo , Receptores Proteína Tirosina Quinases/metabolismo , Transdução de Sinais/fisiologia , Citocinas/genética , Feminino , Células HEK293 , Humanos , Masculino , Fosforilação/fisiologia , Ligação Proteica/fisiologia , Estrutura Terciária de Proteína , Proteoma/genética , Proteômica , Receptores Proteína Tirosina Quinases/genética
2.
Sci Transl Med ; 5(178): 178ra39, 2013 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-23536011

RESUMO

The fibroblast growth factor (FGF) pathway promotes tumor growth and angiogenesis in many solid tumors. Although there has long been interest in FGF pathway inhibitors, development has been complicated: An effective FGF inhibitor must block the activity of multiple mitogenic FGF ligands but must spare the metabolic hormone FGFs (FGF-19, FGF-21, and FGF-23) to avoid unacceptable toxicity. To achieve these design requirements, we engineered a soluble FGF receptor 1 Fc fusion protein, FP-1039. FP-1039 binds tightly to all of the mitogenic FGF ligands, inhibits FGF-stimulated cell proliferation in vitro, blocks FGF- and vascular endothelial growth factor (VEGF)-induced angiogenesis in vivo, and inhibits in vivo growth of a broad range of tumor types. FP-1039 antitumor response is positively correlated with RNA levels of FGF2, FGF18, FGFR1c, FGFR3c, and ETV4; models with genetic aberrations in the FGF pathway, including FGFR1-amplified lung cancer and FGFR2-mutated endometrial cancer, are particularly sensitive to FP-1039-mediated tumor inhibition. FP-1039 does not appreciably bind the hormonal FGFs, because these ligands require a cell surface co-receptor, klotho or ß-klotho, for high-affinity binding and signaling. Serum calcium and phosphate levels, which are regulated by FGF-23, are not altered by administration of FP-1039. By selectively blocking nonhormonal FGFs, FP-1039 treatment confers antitumor efficacy without the toxicities associated with other FGF pathway inhibitors.


Assuntos
Fatores de Crescimento de Fibroblastos/antagonistas & inibidores , Imunoglobulina G/uso terapêutico , Neoplasias/tratamento farmacológico , Neoplasias/metabolismo , Proteínas de Fusão Oncogênica/uso terapêutico , Receptor Tipo 1 de Fator de Crescimento de Fibroblastos/uso terapêutico , Cálcio/sangue , Fator de Crescimento de Fibroblastos 23 , Fatores de Crescimento de Fibroblastos/metabolismo , Humanos , Fosfatos/sangue , Proteínas Recombinantes de Fusão
3.
Comb Chem High Throughput Screen ; 12(5): 514-9, 2009 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-19519331

RESUMO

Feature selection is an important challenge in many classification problems, especially if the number of features greatly exceeds the number of examples available. We have developed a procedure--GenForest--which controls feature selection in random forests of decision trees by using a genetic algorithm. This approach was tested through our entry into the Comparative Evaluation of Prediction Algorithms 2006 (CoEPrA) competition (accessible online at: http://www.coepra.org). CoEPrA was a modeling competition organized to provide an objective testing for various classification and regression algorithms via the process of blind prediction. In the competition GenForest ranked 10/23, 5/16 and 9/16 on CoEPrA classification problems 1, 3 and 4, respectively, which involved the classification of type I MHC nonapeptides i.e. peptides containing nine amino acids. These problems each involved the classification of different sets of nonapeptides. Associated with each amino acid was a set of 643 features for a total of 5787 features per peptide. The method, its application to the CoEPrA datasets, and its performance in the competition are described.


Assuntos
Algoritmos , Árvores de Decisões , Genes MHC Classe I , Antígenos de Histocompatibilidade Classe I/genética , Peptídeos/química , Aminoácidos/química , Aminoácidos/imunologia , Humanos
4.
Science ; 320(5877): 807-11, 2008 May 09.
Artigo em Inglês | MEDLINE | ID: mdl-18467591

RESUMO

To understand the system of secreted proteins and receptors involved in cell-cell signaling, we produced a comprehensive set of recombinant secreted proteins and the extracellular domains of transmembrane proteins, which constitute most of the protein components of the extracellular space. Each protein was tested in a suite of assays that measured metabolic, growth, or transcriptional responses in diverse cell types. The pattern of responses across assays was analyzed for the degree of functional selectivity of each protein. One of the highly selective proteins was a previously undescribed ligand, designated interleukin-34 (IL-34), which stimulates monocyte viability but does not affect responses in a wide spectrum of other assays. In a separate functional screen, we used a collection of extracellular domains of transmembrane proteins to discover the receptor for IL-34, which was a known cytokine receptor, colony-stimulating factor 1 (also called macrophage colony-stimulating factor) receptor. This systematic approach is thus useful for discovering new ligands and receptors and assessing the functional selectivity of extracellular regulatory proteins.


Assuntos
Espaço Extracelular/química , Interleucinas/isolamento & purificação , Receptores de Interleucina/isolamento & purificação , Animais , Clonagem Molecular , DNA Complementar , Humanos , Interleucinas/metabolismo , Interleucinas/fisiologia , Proteínas de Membrana/isolamento & purificação , Proteínas de Membrana/fisiologia , Estrutura Terciária de Proteína , Proteoma , Receptores de Interleucina/fisiologia
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