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2006 Progress Report: Optimization Tools for In Silico Structural Proteomics
EPA Grant Number: R832721C005Subproject: 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 , Androulakis, Ioannis , Georgopoulos, Panos G. , Ierapetritou, Marianthi , Rabitz, Herschel , Tong, Weida , Welsh, William J.
Institution: Princeton University
Current Institution: Princeton University , Rutgers University , U.S. Food and Drug Administration , University of Medicine and Dentistry of New Jersey
EPA Project Officer: Mustra, David
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)
Research Category: Computational Toxicology
Description:
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 cα—cα 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 cα—cα 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.
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
| Other subproject views: | All 7 publications | 3 publications in selected types | All 2 journal articles |
| Other center views: | All 103 publications | 24 publications in selected types | All 20 journal articles |
| Type | Citation | ||
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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) |
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Rajgaria R, McAllister SR, Floudas CA. A novel high resolution Cα—Cα distance dependent force field based on a high quality decoy set. Proteins: Structure, Function, and Bioinformatics 2006;65(3):726-741. |
R832721 (2007) R832721C005 (2006) |
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computational toxicology, toxicogenomics, bioinformatics, toxicoinformatics, proteomics, metabonomics, physiomics, cytomics, genomics, transcriptomics, enviroinformatics, cheminformatics, biologically based dose-response modeling, cross-species extrapolation, risk assessment,
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ENVIRONMENTAL MANAGEMENT, Scientific Discipline, Health, Risk Assessment, Biology, Risk Assessments, Biochemistry, exposure assessment, biochemical research, chemical composition, ecological risk assessment, toxicologic assessment, bioinformatics, human health risk, biopollution, toxicology, environmental risks, risk, computational toxicology
Relevant Websites:
Progress and Final Reports:
Original Abstract
2007 Progress Report
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