Floudas Lab Participating in WeFold CASP10 Collaborative Protein Folding Effort

Posted May 30, 2012

The WeFold collaborative folding effort leverages the expertise of a number of groups in the area of protein folding. Each group has a unique approach to the problem, and combining expertise and insight should yield more fruitful results than working alone. The protein folding problem has always been elusive, but through this collaboration the WeFold team aims to synergistically contribute their collective strengths towards this effort.

In the Floudas lab, team members George Khoury, James Smadbeck, and Professor Christodoulos Floudas are contributing to various stages of the pipeline utilizing portions of the hybrid deterministic and stochastic global optimization [1-3] method ASTRO-FOLD 2.0 [4-6]. We are contributing our secondary structures predicted by CONCORD [7], our beta-sheet topologies predicted by BeST [8], and our contact predictions [9]. The constraints made by the methods are used as inputs to various conformational search algorithms that other members of the WeFold collaboration have developed and are using to generate structures. The predictions can also be used to build a consensus with other contributors to the pipeline.

Another way the Floudas lab contributes is by analyzing and clustering structures generated by human players in the game Foldit. For a typical target, an ensemble of hundreds of thousands of structures are generated. While it is not possible to analyze each one, it is possible to identify and analyze all of the unique structures. From those unique structures, extended conformations and structures without any secondary structure can be filtered out. The remaining dataset is one to two orders of magnitude smaller, and more amenable to analysis. The resulting dataset is clustered using ICON [10], an iterative, traveling salesman problem-based clustering method for identifying near-native protein structures in an ensemble of conformers.

For more information, please see the WeFold Website.

[1] Klepeis, J. L.; Floudas, C. A. Ab Initio Tertiary Structure Prediction of Proteins. Journal of Global Optimization 2003, 25, 113-140.

[2] Klepeis, J. L.; Pieja, M.; Floudas, C. A. A New Class of Hybrid Global Optimization Algorithms for Peptide Structure Prediction: Integrated Hybrids. Computer Physics Communications 2003, 151, 121-140.

[3] Klepeis, J. L.; Pieja, M.; Floudas, C. A. A New Class of Hybrid Global Optimization Algorithms for Peptide Structure Prediction: Alternating Hybrids and Application fo Met-Enkephalin and Melittin. Biophysical Journal 2003, 84, 869-882.

[4] Klepeis, J. L.; Floudas, C. A. ASTRO-FOLD: a combinatorial and global optimization framework for ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. Biophysical Journal 2003, 85, 2119-2146.

[5] Subramani, A.; Wei, Y.; Floudas, C. A. ASTRO-FOLD 2.0: An enhanced framework for protein structure prediction. AIChE Journal 2012, 58 (5), 1619-1637.

[6] Subramani, A.; Floudas, C. A. Structure Prediction of Loops with Fixed and Flexible Stems. J. Phys. Chem. B. DOI: 10.1021/jp2113957

[7] Wei, Y.; Thompson, J.; Floudas, C. A. CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 2012, 468 (2139), 831-850.

[8] Subramani, A.; Floudas, C. A. β-sheet Topology Prediction with High Precision and Recall for β and Mixed α/β Proteins. PLoS ONE 2012, 7 (3), e32461.

[9] Rajgaria, R.; Wei, Y.; Floudas, C. A. Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3d structure prediction method ASTRO-FOLD. Proteins 2010, 78, 1825-1846.

[10] Subramani, A.; DiMaggio, P. A.; Floudas, C. A. Selecting high quality protein structures from diverse conformational ensembles. Biophysical J. 2009, 97, 1728-1736.

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