The long-term goals of this proposal are to uncover the fundamental mechanisms of protein folding and/or misfolding, and, eventually, to predict protein structures from sequences using bioinformatic, theoretical, and computational methods. This proposal addresses the challenge of developing a computational model that is detailed enough to capture the specific folding behavior of a given protein, but is simple enough to permit efficient calculations. We propose to develop an all-atom model based on simplified potentials. Preliminary studies show that this new all-atom model allows practical folding simulations using regular PCs, and that it yields unprecedented accuracy in predicting the folding pathway(s) of a given protein. This initial success and productivity provide strong incentives for the further development and validation of this method. The new model will be used to examine the extent to which the tertiary structure encodes the varieties of folding mechanisms and the effect of non-native hydrogen bonding and nonnative hydrophobic interactions. In this regard, various all-atom models of the Pin WW domain will be tested. Results will be compared against available experimental data. The knowledge gained from the proposed studies will, not only advance our understanding of how specific proteins fold, but also should be useful for designing mutants that are optimized for folding and stability. The analytic tools and computational methods developed in the proposal have the potential to be widely used by those who are interested in determining the preferred folding pathways.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM066049-03
Application #
6892909
Study Section
Molecular and Cellular Biophysics Study Section (BBCA)
Program Officer
Wehrle, Janna P
Project Start
2003-06-01
Project End
2008-05-31
Budget Start
2005-06-01
Budget End
2006-05-31
Support Year
3
Fiscal Year
2005
Total Cost
$231,510
Indirect Cost
Name
State University of New York at Buffalo
Department
Physiology
Type
Schools of Medicine
DUNS #
038633251
City
Buffalo
State
NY
Country
United States
Zip Code
14260
Liang, Shide; Wang, Guangce; Zhou, Yaoqi (2009) Refining near-native protein-protein docking decoys by local resampling and energy minimization. Proteins 76:309-16
Xue, Bin; Faraggi, Eshel; Zhou, Yaoqi (2009) Predicting residue-residue contact maps by a two-layer, integrated neural-network method. Proteins 76:176-83
Liang, Shide; Meroueh, Samy O; Wang, Guangce et al. (2009) Consensus scoring for enriching near-native structures from protein-protein docking decoys. Proteins 75:397-403
Xu, Beisi; Yang, Yuedong; Liang, Haojun et al. (2009) An all-atom knowledge-based energy function for protein-DNA threading, docking decoy discrimination, and prediction of transcription-factor binding profiles. Proteins 76:718-30
Faraggi, Eshel; Xue, Bin; Zhou, Yaoqi (2009) Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 74:847-56
Luo, Zhonglin; Ding, Jiandong; Zhou, Yaoqi (2008) Folding mechanisms of individual beta-hairpins in a Go model of Pin1 WW domain by all-atom molecular dynamics simulations. J Chem Phys 128:225103
Lei, Hongxing; Wu, Chun; Wang, Zhi-Xiang et al. (2008) Folding processes of the B domain of protein A to the native state observed in all-atom ab initio folding simulations. J Chem Phys 128:235105
Liang, Shide; Zhang, Chi; Liu, Song et al. (2006) Protein binding site prediction using an empirical scoring function. Nucleic Acids Res 34:3698-707
Zhou, Hongyi; Zhou, Yaoqi (2005) Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 58:321-8
Zhou, Hongyi; Zhou, Yaoqi (2004) Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition. Proteins 55:1005-13

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