Large-scale predictions of secretory proteins from mammalian genomic and EST sequences.
Machine learning techniques have improved predictions of secretory proteins from protein, genomic and expressed sequence tag (EST) sequences. Artificial neural networks, physical sequence analysis using high-performance optimization, and hidden Markov models identify extremely variable signal peptides (the vehicles of protein transport across the endoplasmic reticulum membrane), transmembrane segments, and specific extracellular and intracellular domains as indicators of possible roles in the intercellular and intracellular chemical signaling pathways. The major role of peptide hormones, blood coagulation factors, carcinogenesis agents, and other secretory proteins in orchestrating multicellular life indicates pharmacological potential in the cure of major diseases and numerous biotechnological applications.
Ladunga
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Bioinformatics Department, SmithKline Beecham Pharmaceuticals, King of Prussia, PA 19406-0939, USA. Steve_Ladunga@sbphrd.co
D000818:Animals; D019295:Computational Biology; D020224:Expressed Sequence Tags; D016678:Genome; D006801:Humans; D008390:Markov Chains; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals / Q000737:chemistry / Q000235:genetics / Q000502:physiology*; D011506:Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism*
D021382:Protein Sorting Signals; D011506:Proteins