Populating Local Minima in the Protein Conformational Space

Protein Modeling conceptualizes the protein energy landscape as a funnel with the native structure at the low-energy minimum. Current protein structure prediction algorithms seek the global minimum by searching for low-energy conformations in the hope that some of these reside in local minima near the native structure. The search techniques employed, however, fail to explicitly model these local minima. This work proposes a memetic algorithm which combines methods from evolutionary computation with cutting-edge structure prediction protocols. The Protein Local Optima Walk (PLOW) algorithm proposed here explores the space of local minima by explicitly projecting each move in the conformation space to a nearby local minimum. This allows PLOW to jump over local energy barriers and more effectively sample near-native conformations. Analysis across a broad range of proteins shows that PLOW outperforms an MMC-based method and compares favorably against other published abini to structure prediction algorithms.

[1]  D. Baker,et al.  Coupled prediction of protein secondary and tertiary structure , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Eugene Santos,et al.  Local minima-based exploration for off-lattice protein folding , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[3]  Amarda Shehu,et al.  In Search of the protein Native State with a Probabilistic Sampling Approach , 2011, J. Bioinform. Comput. Biol..

[4]  Tanja Kortemme,et al.  Computational design of protein-protein interactions. , 2004, Current opinion in chemical biology.

[5]  Amarda Shehu,et al.  An Ab-initio tree-based exploration to enhance sampling of low-energy protein conformations , 2009, Robotics: Science and Systems.

[6]  Amarda Shehu,et al.  Enhancing Sampling of the Conformational Space Near the Protein Native State , 2010, BIONETICS.

[7]  Amarda Shehu,et al.  Guiding the Search for Native-like Protein Conformations with an Ab-initio Tree-based Exploration , 2010, Int. J. Robotics Res..

[8]  Cecilia Clementi,et al.  Unfolding the fold of cyclic cysteine‐rich peptides , 2008, Protein science : a publication of the Protein Society.

[9]  James E. Fitzgerald,et al.  Mimicking the folding pathway to improve homology-free protein structure prediction , 2009, Proceedings of the National Academy of Sciences.

[10]  L. Kavraki,et al.  Multiscale characterization of protein conformational ensembles , 2009, Proteins.

[11]  K. Dill,et al.  From Levinthal to pathways to funnels , 1997, Nature Structural Biology.

[12]  C. Anfinsen Principles that govern the folding of protein chains. , 1973, Science.

[13]  Madhu Chetty,et al.  Novel Memetic Algorithm for Protein Structure Prediction , 2009, Australasian Conference on Artificial Intelligence.

[14]  P. Bradley,et al.  Toward High-Resolution de Novo Structure Prediction for Small Proteins , 2005, Science.