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2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05)
Protein Structure Prediction Using Physical-Based Global Optimization and Knowledge-Guided Fragment Packing
Stanford, California
August 08-August 11
ISBN: 0-7695-2442-7
Jinhui Ding, UC Berkeley
Elizabeth Eskow, Univ. of Colo., Boulder
Nelson Max, UC Davis
Silvia Crivelli, QB3 Institute,UC Berkeley

We describe a new method to predict the tertiary structure of new-fold proteins. Our two-phase approach combines the knowledge-based fragmentpacking with the minimization of a physics-based energy function. The method is one of the few attempts to use an all-atom physics-based energy function throughout all stages of the optimization. Information from the known proteins is utilized to guide the search through the vast conformational space. We tested this method in CASP6 and it produced the best prediction on one of the new-fold targets T238, alpha-helical protein. After CASP6, we carried out a series of experiments to test and improve our method and we found that our method performed well on alpha-helical proteins.

Citation:
Jinhui Ding, Elizabeth Eskow, Nelson Max, Silvia Crivelli, "Protein Structure Prediction Using Physical-Based Global Optimization and Knowledge-Guided Fragment Packing," csbw, pp.211-212, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05), 2005
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