11
Protein
Fold Identification Using Support Vector Machine--Environmental
Research Technologies (a DBA of Business Numerics, LLC),
Dr.
Igor Antoshechklin, Ph.D, Principal Investigator,
Mr.
Chuck Ray, Business Official,
DOE
Grant No. DE-FG02-03ER83649
Amount:
$99,773
Prediction
of the structure and function of proteins, based on their amino acid sequences,
is the major challenge in the post genomic era.
The major determinant of protein function is their three-dimensional
structure, knowledge of which could be used to model protein-protein
interactions and simulate complex signaling networks inside the cell.
Knowledge of protein structure is also essential for rational
structure-based drug design. Novel
pathogen detection tools also could be developed based on knowledge of
pathogenic protein structures. This
project will develop an automated computer system, based on a machine-learning
algorithm in combination with an extensive database of naturally occurring and
computer-designed amino acid sequences, to predict the molecular structure of
proteins from their amino acid sequences. Phase
I will design and implement a preliminary prototype of a genetically applicable
set of machine learning bioinformatics tools for detection of remote protein
homologies and protein fold classification.
Commercial Applications and Other
Benefits
as described by awardee: An
automated computer system for protein structure prediction should greatly
facilitate genome analyses as well as protein function prediction.
The system also could facilitate the development of structure-based
methods of pathogen identification and impact our ability to discover new
treatments for human disorders.