Literature DB >> 29722450

Crius: A novel fragment-based algorithm of de novo substrate prediction for enzymes.

Zhiqiang Yao1, Shuiqin Jiang1, Lujia Zhang1,2,3, Bei Gao1, Xiao He2,3, John Z H Zhang2,3, Dongzhi Wei1.   

Abstract

The study of enzyme substrate specificity is vital for developing potential applications of enzymes. However, the routine experimental procedures require lot of resources in the discovery of novel substrates. This article reports an in silico structure-based algorithm called Crius, which predicts substrates for enzyme. The results of this fragment-based algorithm show good agreements between the simulated and experimental substrate specificities, using a lipase from Candida antarctica (CALB), a nitrilase from Cyanobacterium syechocystis sp. PCC6803 (Nit6803), and an aldo-keto reductase from Gluconobacter oxydans (Gox0644). This opens new prospects of developing computer algorithms that can effectively predict substrates for an enzyme.
© 2018 The Protein Society.

Entities:  

Keywords:  enzyme selectivity; fragment-based algorithm; substrate prediction; substrate specificity

Mesh:

Substances:

Year:  2018        PMID: 29722450      PMCID: PMC6153407          DOI: 10.1002/pro.3437

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  23 in total

1.  The computer program LUDI: a new method for the de novo design of enzyme inhibitors.

Authors:  H J Böhm
Journal:  J Comput Aided Mol Des       Date:  1992-02       Impact factor: 3.686

2.  Predicting substrates by docking high-energy intermediates to enzyme structures.

Authors:  Johannes C Hermann; Eman Ghanem; Yingchun Li; Frank M Raushel; John J Irwin; Brian K Shoichet
Journal:  J Am Chem Soc       Date:  2006-12-13       Impact factor: 15.419

3.  Computer simulations of enzyme catalysis: finding out what has been optimized by evolution.

Authors:  A Warshel; J Florián
Journal:  Proc Natl Acad Sci U S A       Date:  1998-05-26       Impact factor: 11.205

4.  Characterization of a novel NADPH-dependent oxidoreductase from Gluconobacter oxydans.

Authors:  Minmin Chen; Jinping Lin; Yushu Ma; Dongzhi Wei
Journal:  Mol Biotechnol       Date:  2010-10       Impact factor: 2.695

5.  Characterization of two aldo-keto reductases from Gluconobacter oxydans 621H capable of regio- and stereoselective alpha-ketocarbonyl reduction.

Authors:  Paul Schweiger; Harald Gross; Uwe Deppenmeier
Journal:  Appl Microbiol Biotechnol       Date:  2010-04-23       Impact factor: 4.813

6.  Assignment of pterin deaminase activity to an enzyme of unknown function guided by homology modeling and docking.

Authors:  Hao Fan; Daniel S Hitchcock; Ronald D Seidel; Brandan Hillerich; Henry Lin; Steven C Almo; Andrej Sali; Brian K Shoichet; Frank M Raushel
Journal:  J Am Chem Soc       Date:  2013-01-02       Impact factor: 15.419

7.  Cloning of a nitrilase gene from the cyanobacterium Synechocystis sp. strain PCC6803 and heterologous expression and characterization of the encoded protein.

Authors:  Ute Heinemann; Dirk Engels; Sibylle Bürger; Christoph Kiziak; Ralf Mattes; Andreas Stolz
Journal:  Appl Environ Microbiol       Date:  2003-08       Impact factor: 4.792

8.  Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases.

Authors:  Nir London; Jeremiah D Farelli; Shoshana D Brown; Chunliang Liu; Hua Huang; Magdalena Korczynska; Nawar F Al-Obaidi; Patricia C Babbitt; Steven C Almo; Karen N Allen; Brian K Shoichet
Journal:  Biochemistry       Date:  2015-01-05       Impact factor: 3.162

Review 9.  Biochemical functional predictions for protein structures of unknown or uncertain function.

Authors:  Caitlyn L Mills; Penny J Beuning; Mary Jo Ondrechen
Journal:  Comput Struct Biotechnol J       Date:  2015-02-18       Impact factor: 7.271

10.  A large-scale evaluation of computational protein function prediction.

Authors:  Predrag Radivojac; Wyatt T Clark; Tal Ronnen Oron; Alexandra M Schnoes; Tobias Wittkop; Artem Sokolov; Kiley Graim; Christopher Funk; Karin Verspoor; Asa Ben-Hur; Gaurav Pandey; Jeffrey M Yunes; Ameet S Talwalkar; Susanna Repo; Michael L Souza; Damiano Piovesan; Rita Casadio; Zheng Wang; Jianlin Cheng; Hai Fang; Julian Gough; Patrik Koskinen; Petri Törönen; Jussi Nokso-Koivisto; Liisa Holm; Domenico Cozzetto; Daniel W A Buchan; Kevin Bryson; David T Jones; Bhakti Limaye; Harshal Inamdar; Avik Datta; Sunitha K Manjari; Rajendra Joshi; Meghana Chitale; Daisuke Kihara; Andreas M Lisewski; Serkan Erdin; Eric Venner; Olivier Lichtarge; Robert Rentzsch; Haixuan Yang; Alfonso E Romero; Prajwal Bhat; Alberto Paccanaro; Tobias Hamp; Rebecca Kaßner; Stefan Seemayer; Esmeralda Vicedo; Christian Schaefer; Dominik Achten; Florian Auer; Ariane Boehm; Tatjana Braun; Maximilian Hecht; Mark Heron; Peter Hönigschmid; Thomas A Hopf; Stefanie Kaufmann; Michael Kiening; Denis Krompass; Cedric Landerer; Yannick Mahlich; Manfred Roos; Jari Björne; Tapio Salakoski; Andrew Wong; Hagit Shatkay; Fanny Gatzmann; Ingolf Sommer; Mark N Wass; Michael J E Sternberg; Nives Škunca; Fran Supek; Matko Bošnjak; Panče Panov; Sašo Džeroski; Tomislav Šmuc; Yiannis A I Kourmpetis; Aalt D J van Dijk; Cajo J F ter Braak; Yuanpeng Zhou; Qingtian Gong; Xinran Dong; Weidong Tian; Marco Falda; Paolo Fontana; Enrico Lavezzo; Barbara Di Camillo; Stefano Toppo; Liang Lan; Nemanja Djuric; Yuhong Guo; Slobodan Vucetic; Amos Bairoch; Michal Linial; Patricia C Babbitt; Steven E Brenner; Christine Orengo; Burkhard Rost; Sean D Mooney; Iddo Friedberg
Journal:  Nat Methods       Date:  2013-01-27       Impact factor: 28.547

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