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

Istvan Ladunga

Research output: Contribution to journalReview articlepeer-review

33 Scopus citations


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
Issue number1
StatePublished - Feb 1 2000

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Biomedical Engineering

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