Literature DB >> 9273846

Protein engineering from a bioindustrial point of view.

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Abstract

Work with proteins, particularly enzymes, is a rapidly growing segment of the biotechnology industry. Directed evolution promises to become an increasingly important strategy in their development as it allows one to sidestep some of the difficult questions relating the structural and functional properties of such proteins to their industrial utility. It is also clear, however, that greater understanding of how to engineer certain basic enzyme properties, such as stability, activity, and surface properties, is beginning to emerge, and this understanding will make rational design more efficient. To engineer a commercially useful protein many properties need to be changed, and frequently these changes are interdependent. Recent protein engineering studies on protease, amylase, lipase and cellulase illustrate some of the progress in this area.

Year:  1997        PMID: 9273846     DOI: 10.1016/s0958-1669(97)80062-6

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  6 in total

1.  Conversion of a maltose receptor into a zinc biosensor by computational design.

Authors:  J S Marvin; H W Hellinga
Journal:  Proc Natl Acad Sci U S A       Date:  2001-04-24       Impact factor: 11.205

2.  Rational design of enantioselective enzymes requires considerations of entropy.

Authors:  J Ottosson; J C Rotticci-Mulder; D Rotticci; K Hult
Journal:  Protein Sci       Date:  2001-09       Impact factor: 6.725

3.  C-terminal flanking peptide stabilized the catalytic domain of a recombinant Bacillus subtilis endo-β-1, 4-glucanase.

Authors:  Yujuan Wang; Jun Wang
Journal:  Protein J       Date:  2013-04       Impact factor: 2.371

4.  Simultaneous enhancement of thermostability and catalytic activity of phospholipase A(1) by evolutionary molecular engineering.

Authors:  J K Song; J S Rhee
Journal:  Appl Environ Microbiol       Date:  2000-03       Impact factor: 4.792

5.  Predicting virus mutations through statistical relational learning.

Authors:  Elisa Cilia; Stefano Teso; Sergio Ammendola; Tom Lenaerts; Andrea Passerini
Journal:  BMC Bioinformatics       Date:  2014-09-19       Impact factor: 3.169

6.  Applications of the class II lanthipeptide protease LicP for sequence-specific, traceless peptide bond cleavage.

Authors:  Weixin Tang; Shi-Hui Dong; Lindsay M Repka; Chang He; Satish K Nair; Wilfred A van der Donk
Journal:  Chem Sci       Date:  2015-09-02       Impact factor: 9.825

  6 in total

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