Literature DB >> 11187152

TRITON: in silico construction of protein mutants and prediction of their activities.

M Prokop1, J Damborský, J Koca.   

Abstract

MOTIVATION: One of the objectives of protein engineering is to propose and construct modified proteins with improved activity for the substrate of interest. Systematic computational investigation of many protein variants requires the preparation and handling of a large number of data files. The type of the data generated during the modelling of protein variants and the estimation of their activities offers the possibility of process automatization.
RESULTS: The graphical program TRITON has been developed for modelling protein mutants and assessment of their activities. Protein mutants are modelled from the wild type structure by homology modelling using the external program MODELLER. Chemical reactions taking place in the mutants active site are modelled using the semi-empirical quantum mechanic program MOPAC. Semi-quantitative predictions of mutants activities can be achieved by evaluating the changes in energies of the system and partial atomic charges of active site residues during the reaction. The program TRITON offers graphical tools for the preparation of the input data files, for calculation and for the analysis of the generated output data. AVAILABILITY: The program TRITON can run under operating systems IRIX, Linux and NetBSD. The software is available at http://www.chemi.muni.cz/lbsd/triton.ht ml.

Mesh:

Substances:

Year:  2000        PMID: 11187152     DOI: 10.1093/bioinformatics/16.9.845

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

1.  Probing the importance of hydrogen bonds in the active site of the subtilisin nattokinase by site-directed mutagenesis and molecular dynamics simulation.

Authors:  Zhong-liang Zheng; Mao-qing Ye; Zhen-yu Zuo; Zhi-gang Liu; Keng-chang Tai; Guo-lin Zou
Journal:  Biochem J       Date:  2006-05-01       Impact factor: 3.857

2.  Mechanism of enhanced conversion of 1,2,3-trichloropropane by mutant haloalkane dehalogenase revealed by molecular modeling.

Authors:  Pavel Banás; Michal Otyepka; Petr Jerábek; Martin Petrek; Jirí Damborský
Journal:  J Comput Aided Mol Des       Date:  2006-10-03       Impact factor: 3.686

Review 3.  Protein Function Analysis through Machine Learning.

Authors:  Chris Avery; John Patterson; Tyler Grear; Theodore Frater; Donald J Jacobs
Journal:  Biomolecules       Date:  2022-09-06

4.  Identification of deleterious nsSNPs in α, μ, π and θ class of GST family and their influence on protein structure.

Authors:  P Yadav; A Chatterjee; A Bhattacharjee
Journal:  Genom Data       Date:  2014-05-09

Review 5.  Computer-Aided Protein Directed Evolution: a Review of Web Servers, Databases and other Computational Tools for Protein Engineering.

Authors:  Rajni Verma; Ulrich Schwaneberg; Danilo Roccatano
Journal:  Comput Struct Biotechnol J       Date:  2012-10-22       Impact factor: 7.271

6.  Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods.

Authors:  Ewy Mathe; Magali Olivier; Shunsuke Kato; Chikashi Ishioka; Pierre Hainaut; Sean V Tavtigian
Journal:  Nucleic Acids Res       Date:  2006-03-06       Impact factor: 16.971

7.  TRITON: a graphical tool for ligand-binding protein engineering.

Authors:  Martin Prokop; Jan Adam; Zdenek Kríz; Michaela Wimmerová; Jaroslav Koca
Journal:  Bioinformatics       Date:  2008-07-04       Impact factor: 6.937

Review 8.  The Association between Gene-Environment Interactions and Diseases Involving the Human GST Superfamily with SNP Variants.

Authors:  Antoinesha L Hollman; Paul B Tchounwou; Hung-Chung Huang
Journal:  Int J Environ Res Public Health       Date:  2016-03-29       Impact factor: 3.390

  8 in total

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