Grantee Research Project Results

2006 Progress Report: Optimization Tools for In Silico Structural Proteomics

EPA Grant Number: R832721C005
Subproject: this is subproject number 005 , established and managed by the Center Director under grant R832721
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: New Jersey Research Center for Environmental Bioinformatics and Computational Toxicology
Center Director: Welsh, William J.
Title: Optimization Tools for In Silico Structural Proteomics
Investigators: Floudas, Christodoulos
Current Investigators: Floudas, Christodoulos , Georgopoulos, Panos G. , Welsh, William J. , Rabitz, Herschel , Androulakis, Ioannis , Ierapetritou, Marianthi , Tong, Weida
Institution: Princeton University
Current Institution: Princeton University , Rutgers , U.S. Food and Drug Administration , University of Medicine and Dentistry of New Jersey
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 2005 through September 30, 2010
Project Period Covered by this Report: October 1, 2005 through September 30, 2006
RFA: Computational Toxicology: Environmental Bioinformatics Research Center (2004) RFA Text | Recipients Lists
Research Category: Computational Toxicology , Human Health , Safer Chemicals

Objective:

The aims of the project have not changed from the original application; original objectives for the report period have been met.

Progress Summary:

Developed a new force field, based on Ca-Ca Editor’s note: Per the title of one of the journal articles below, I believe this should be c^α—c^α distances, and its effectiveness was demonstrated with a comparison to existing force fields. This force field is of major importance in de novo protein design and high resolution protein structure determination.

Developed a new method for the prediction of interhelical restraints and applied it to alpha helical proteins.

Developed a novel mixed-integer linear optimization approach for the peptide identification based on tandem mass spectrometry.

Results to Date

A new force field was developed, based on Ca-Ca Editor’s note: Per the title of one of the journal articles below, I believe this should be c^α—c^α distances, and its effectiveness was demonstrated with a comparison to existing force fields. This force field is of major importance in de novo protein design and high resolution protein structure determination.

A new method for the prediction of inter-helical restraints was developed and applied to alpha helical proteins.

A novel mixed-integer linear optimization approach has been developed for the peptide identification based on tandem mass spectrometry.

The aforementioned findings will allow for first principles protein structure prediction, new de novo discoveries, and de novo methods for high throughput proteomics.

Future Activities:

The planned activities focus on the topics of: (1) protein structure prediction; (2) de novo protein design; (3) peptide and protein identification; and (4) topology prediction in signal transduction networks.

Journal Articles on this Report : 2 Displayed | Download in RIS Format

Publications Views
Other subproject views:	All 7 publications	3 publications in selected types	All 2 journal articles
Other center views:	All 330 publications	132 publications in selected types	All 118 journal articles

Publications
Type	Citation	Sub Project	Document Sources
Journal Article	DiMaggio Jr PA, Floudas CA. A mixed-integer optimization framework for de novo peptide identification. AIChE Journal 2007;53(1):160-173.	R832721 (2007) R832721C005 (2006)	Full-text from PubMed Abstract from PubMed Associated PubMed link
Journal Article	Rajgaria R, McAllister SR, Floudas CA. A novel high resolution C^α—C^α distance dependent force field based on a high quality decoy set. Proteins 2006;65(3):726-741.	R832721 (2007) R832721C005 (2006) R832721C005 (2007)	Abstract from PubMed Full-text: ResearchGate - Abstract & Full Text PDF Exit Abstract: Wiley-Abstract Exit

Supplemental Keywords:

computational toxicology, toxicogenomics, bioinformatics, toxicoinformatics, proteomics, metabonomics, physiomics, cytomics, genomics, transcriptomics, enviroinformatics, cheminformatics, biologically based dose-response modeling, cross-species extrapolation, risk assessment,, Health, Scientific Discipline, ENVIRONMENTAL MANAGEMENT, Risk Assessments, Biology, Biochemistry, Risk Assessment, ecological risk assessment, environmental risks, human exposure, chemical composition, risk, bioinformatics, biopollution, toxicology, human health risk, toxicologic assessment, computational toxicology, biochemical research

Relevant Websites:

http://ebctc.org Exit

Progress and Final Reports:

Original Abstract

Main Center Abstract and Reports:

R832721 New Jersey Research Center for Environmental Bioinformatics and Computational Toxicology

Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R832721C001 Development and Application of the DORIAN (Dose-Response Information Analysis) System
R832721C002 Hepatocyte Metabolism Model for Xenobiotics
R832721C003 Development of Computational Tools for Optimal Identification of Biological Networks
R832721C004 Cheminformatics Tools for Toxicant Characterization
R832721C005 Optimization Tools for In Silico Structural Proteomics

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The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.