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Each approach uniquely employs evolutionary, sequence, or structural information to characterize residue substitutions in proteins, and predictions of functional effects are obtained via mathematical, rule-based, or statistical learning methods. A number of in silico mutagenesis methodologies have been developed in recent years, yielding efficient computational analogues to complement experimental methods from the wet laboratory at a fraction of the time and cost, as well as reliable and immediate predictions for functional effects of single residue replacements. Site-directed mutagenesis experiments provide researchers with opportunities to evaluate their effects on protein stability, activity, or disease potential, to annotate structural or functional roles of residues, to gain insights into mechanisms of protein folding, and to accumulate data needed for engineering new proteins with desired thermodynamic and physicochemical properties. Included among these upgrades is the ability to perform three highly requested tasks: to run “big data” batch jobs to generate predictions using modified protein data bank (PDB) structures, and unpublished personal models prepared using standard PDB file formatting and to utilize NMR structure files that contain multiple models.
#Automute protein portable#
Nevertheless, all the codes have been rewritten and substantially altered for the new portable software, and they incorporate several new features based on user feedback. These five command-line driven tools, as well as all the supporting programs, complement those that run our AUTO-MUTE web-based server. Two additional classifiers are available, one for predicting activity changes due to residue replacements and the other for determining the disease potential of mutations associated with nonsynonymous single nucleotide polymorphisms (nsSNPs) in human proteins. Three of the predictors evaluate changes to protein stability upon mutation, each complementing a distinct experimental approach.
#Automute protein software#
Implementation of the random forest algorithm, for classifying mutant activity as either unaffected or affected relative to the native protein, yields 84% accuracy based on tenfold cross-validation.The AUTO-MUTE 2.0 stand-alone software package includes 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.
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The proteins are diverse with respect to host organism (viral, bacterial, human) and function (enzymatic, nucleic acid binding, signaling), the structures span all four major SCOP classifications, and the mutations occur at positions well distributed throughout the seven structures. A computational mutagenesis methodology founded upon a structure-dependent and knowledge-based four-body statistical potential is utilized in generating feature vectors that characterize over 8500 individual amino acid substitutions occurring in seven proteins, each mutant having been experimentally ascertained for its relative effect on native protein activity.