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Titolo:
Predicting protein function from structure: Unique structural features of proteases
Autore:
Stawiski, EW; Baucom, AE; Lohr, SC; Gregoret, LM;
Indirizzi:
Univ Calif Santa Cruz, Dept Biol, Grad Program Mol Cellular & Dev Biol, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz Santa Cruz CA USA 95064 l, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz, Dept Comp Sci, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz Santa Cruz CA USA 95064 i, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz, Dept Chem & Biochem, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz Santa Cruz CA USA 95064 m, Santa Cruz, CA 95064 USA
Titolo Testata:
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
fascicolo: 8, volume: 97, anno: 2000,
pagine: 3954 - 3958
SICI:
0027-8424(20000411)97:8<3954:PPFFSU>2.0.ZU;2-3
Fonte:
ISI
Lingua:
ENG
Soggetto:
LIMITED PROTEOLYTIC SITES; DATABASE; SURFACES; ENZYMES;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
26
Recensione:
Indirizzi per estratti:
Indirizzo: Gregoret, LM Univ Calif Santa Cruz, Dept Biol, Grad Program Mol Cellular &Dev Biol, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz Santa Cruz CA USA 95064 , CA 95064 USA
Citazione:
E.W. Stawiski et al., "Predicting protein function from structure: Unique structural features of proteases", P NAS US, 97(8), 2000, pp. 3954-3958

Abstract

We have noted consistent structural similarities among unrelated proteases. In comparison with other proteins of similar size, proteases have smallerthan average surface areas, smaller radii of gyration, and higher C-alpha densities. These findings imply that proteases are, as a group, more tightly packed than other proteins. There are also notable differences in secondary structure content between these two groups of proteins: proteases have fewer helices and more loops. We speculate that both high packing density and low cu-helical content coevolved in proteases to avoid autolysis. By using the structural parameters that seem to show some separation between proteases and nonproteases, a neural network has been trained to predict protease function with over 86% accuracy. Moreover, it is possible to identify proteases whose folds were not represented during training. Similar structuralanalyses may be useful for identifying other classes of proteins and may be of great utility for categorizing the flood of structures soon to flow from structural genomics initiatives.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 11/08/20 alle ore 14:59:34