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AUTOmated server for predicting functional consequences of amino acid MUTations in protEins AUTO–MUTE

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A collection of programs for predicting functional changes to proteins upon single residue substitutions, developed by combining structure-based features with trained statistical learning models. For each type of function prediction, a variety of classification and regression models have been developed and are available for researchers. These include Random Forest, Support Vector Machine (SVM), AdaBoostM1 combined with the C4.5 Decision Tree algorithm, as well as Tree and SVM regression. The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and/or evolutionary information.

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AUTO–MUTE classification

AUTO–MUTE specifications

Software type:
Package/Module
Restrictions to use:
None
Computer skills:
Advanced
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux
Version:
2.0
Maintained:
Yes

AUTO–MUTE distribution

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AUTO–MUTE classification

AUTO–MUTE specifications

Interface:
Web user interface
Computer skills:
Basic
Stability:
Stable
Restrictions to use:
None
Version:
2.0
Maintained:
Yes

AUTO–MUTE distribution

AUTO–MUTE support

Maintainer

  • Majid Masso <>
  • Majid Masso <>

Credits

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Publications

Institution(s)

Laboratory for Structural Bioinformatics, Department of Bioinformatics and Computational Biology, George Mason University, Manassas, VA, USA

Funding source(s)

This work was supported in part by a grant from the GMU-INOVA fund.

Link to literature

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