Using subsite coupling to predict signal peptides.
Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is a first important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy. Based on a model that takes into account the coupling effect among some key subsites, the so-called [-3, -1, +1] coupling model, a new prediction algorithm is developed. The overall rate of correct prediction for 1939 secretory proteins and 1440 non-secretary proteins was over 92%. It has not escaped our attention that the new method may also serve as a useful tool for helping investigate further many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.
Chou
K C
KC
Computer-Aided Drug Discovery, Pharmacia and Upjohn, Kalamazoo, MI 49007-4940, USA. kuo-chen.chou@am.pnu.com
D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D001331:Automation; D003198:Computer Simulation; D017076:Computer-Aided Design; D005057:Eukaryotic Cells; D006801:Humans; D008565:Membrane Proteins; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011387:Prokaryotic Cells; D021382:Protein Sorting Signals / Q000502:physiology; D021381:Protein Transport / Q000502:physiology; D011506:Proteins / Q000378:metabolism*; D016415:Sequence Alignment; D012697:Serine Endopeptidases / Q000378:metabolism; D013329:Structure-Activity Relationship
D008565:Membrane Proteins; D021382:Protein Sorting Signals; D011506:Proteins; D012697:Serine Endopeptidases; C026515:type I signal peptidase