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AUTO–MUTE specifications

Information


Unique identifier OMICS_00126
Name AUTO–MUTE
Alternative name AUTOmated server for predicting functional consequences of amino acid MUTations in protEins
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Computer skills Advanced
Version 2.0
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Majid Masso

Information


Unique identifier OMICS_00126
Name AUTO–MUTE
Alternative name AUTOmated server for predicting functional consequences of amino acid MUTations in protEins
Interface Web user interface
Restrictions to use None
Computer skills Basic
Version 2.0
Stability Stable
Maintained Yes

Maintainer


  • person_outline Majid Masso

Publications for AUTOmated server for predicting functional consequences of amino acid MUTations in protEins

AUTO–MUTE citations

 (6)
library_books

Vermont: a multi perspective visual interactive platform for mutational analysis

2017
BMC Bioinformatics
PMCID: 5606220
PMID: 28929973
DOI: 10.1186/s12859-017-1789-3

[…] Multi Descriptor (MD), which complements INPS with a new predictor (INPS-3D) that exploits descriptors derived from the protein structure.iStable, proposed in [], integrates I-Mutant2.0 [], MUPRO [], AUTO-MUTE [], PoPMuSiC2.0 [], and CUPSAT [] through SVM to predict protein stability changes upon single amino acid residue mutations, and it performs better than any single method alone.DUET, present […]

library_books

Analysis of Genetic Variation and Potential Applications in Genome Scale Metabolic Modeling

2015
Front Bioeng Biotechnol
PMCID: 4329917
PMID: 25763369
DOI: 10.3389/fbioe.2015.00013

[…] Section in more detail together with the applications of metabolic modeling.The majority of algorithms (53%) for variant effect prediction listed in Table rely on machine-learning approaches [e.g., AUTO-MUTE (Masso and Vaisman, ), FunSAV (Wang et al., ), or HANSA (Acharya and Nagarajaram, )], which is a practical strategy given the huge amount of data available for human diseases. Regarding the […]

library_books

SNPs in Genes Functional in Starch Sugar Interconversion Associate with Natural Variation of Tuber Starch and Sugar Content of Potato (Solanum tuberosum L.)

2014
PMCID: 4199688
PMID: 25081979
DOI: 10.1534/g3.114.012377

[…] ucture of PHO1a was modeled with SWISS-MODEL (; ) using as template the crystal structure of yeast glycogen phosphorylase (PDB entry 1YGP). Effects of amino acid substitutions were predicted with the AUTO-MUTE algorithm () based on the structure of rabbit glycogen phosphorylase B (PDB entry 6GBP). […]

library_books

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

2012
Comput Struct Biotechnol J
PMCID: 3962222
PMID: 24688649
DOI: 10.5936/csbj.201209008

[…] timates the effect of mutations on the protein stability using protein environment specific mean force potentials. The potentials are derived from statistical analysis of protein structure data sets. AUTO-MUTE [, ] provides either energy based or machine learning methods for the prediction of protein stability by providing protein structure, mutation and experimental condition. SIFT (Sorts Intoler […]

library_books

Analyzing Effects of Naturally Occurring Missense Mutations

2012
PMCID: 3346971
PMID: 22577471
DOI: 10.1155/2012/805827

[…] metabolic pathways in StSNP to examine the likely relationship between the disease-related pathways and particular nsSNPs, and link the disease with the current available molecular structure data []. AUTO-MUTE is a knowledge-based computational mutagenesis used to predict the disease potential of human nsSNPs. In this study, 1790 neutral and disease-associated human nsSNPs on 243 diverse human pro […]

library_books

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

2011
Adv Bioinformatics
PMCID: 3189455
PMID: 22007208
DOI: 10.1155/2011/958129

[…] random preference motivates the benefit of developing the 4-body statistical potential that can be used in evaluating sequence-structure compatibility for the purpose of functional prediction []. The Auto-Mute website [], run by Masso and Vaisman, provides further information on the data. […]


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AUTO–MUTE institution(s)
Laboratory for Structural Bioinformatics, Department of Bioinformatics and Computational Biology, George Mason University, Manassas, VA, USA
AUTO–MUTE funding source(s)
This work was supported in part by a grant from the GMU-INOVA fund.

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