A System for the Detection and Prediction of Sequence Variations or Genetic Engineering

University of Missouri System: University of Missouri-Kansas City
posted on 02/18/2010

This invention provides systems and methods to produce virtual databases, virtual database entries, or virtual amino acid sequences. This information can be used to improve the identification of unknown proteins, facilitate recognizing engineered proteins, and distinguish between natural and engineered genes and proteins.

Suggested Uses

Predicting novel polypeptides that may be pathogenic.
Determining whether a pathogenic virus or bacterial strain is the result of natural mutation, or deliberate genetically engineering.
Sequencing and identification of proteins from unusual or understudied organisms.
Improving statistically marginal protein identifications generated by automated database searching programs.

Advantages

Provides a statistically weighted "synthetic mutation", which limits the size of databases generated. By using known mutation patterns, this system generates lists of DNA/RNA or peptide sequences that are more likely to occur in nature compared to other strategies. Currently, there are no tools available to generate statistically weighted novel DNA/RNA or protein sequences.

Innovation Details

Detailed Description

The present invention uses variations in known DNA, RNA, and protein sequences to predict and generate virtual DNA, RNA, and amino acid sequences that may not be represented in the current databases but that are likely to occur in nature. Substitution patterns may be derived from either the chemical, physical, and biological patterns of mutation, or the derived, observable patterns of evolutionary fixation of such mutations between or within species. These virtual sequences (or databases/datafiles of such virtual sequences) contain novel, but statistically likely sequences for use in comparing to unknown proteins (peptides) for protein identification. The use of such synthetic sequences and/or databases facilitate the recognition and distinction between naturally occurring and genetically engineered DNA, RNA, and protein sequences.

File Number: 05UMK050

Web site: http://www.umkc.edu/ors/tt

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This innovation currently is not available for online licensing. Please contact James Brazeal at University of Missouri System: University of Missouri-Kansas City for more information.

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