Large-scale predictions of secretory proteins from mammalian genomic and EST sequences

Istvan Ladunga

Research output: Contribution to journalReview article

31 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)13-18
Number of pages6
JournalCurrent Opinion in Biotechnology
Volume11
Issue number1
DOIs
StatePublished - Feb 1 2000

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Expressed Sequence Tags
Proteins
Blood Coagulation Factors
Peptide Hormones
Protein Transport
Protein Sorting Signals
Endoplasmic Reticulum
Sequence Analysis
Carcinogenesis
Hidden Markov models
Coagulation
Pharmacology
Learning systems
Blood
Membranes
Neural networks

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Biomedical Engineering

Cite this

Large-scale predictions of secretory proteins from mammalian genomic and EST sequences. / Ladunga, Istvan.

In: Current Opinion in Biotechnology, Vol. 11, No. 1, 01.02.2000, p. 13-18.

Research output: Contribution to journalReview article

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