Evolving Conformation Paths to Model Protein Structural Transitions

Proteins are dynamic biomolecules. A structure-by-structure characterization of a protein's transition between two different functional structures is central to elucidating the role of dynamics in modulating protein function and designing therapeutic drugs. Characterizing transitions challenges both dry and wet laboratories. Some computational methods compute discrete representations of the energy landscape that organizes structures of a protein by their potential energies. The representations support queries for paths (series of structures) connecting start and goal structures of interest. Here we address the problem of modeling protein structural transitions under the umbrella of stochastic optimization and propose a novel evolutionary algorithm (EA). The EA evolves paths without reconstructing the energy landscape, addressing two competing optimization objectives, energetic cost and structural resolution. Rather than seek one path, the EA yields an ensemble of paths to represent a transition. Preliminary applications suggest the EA is effective while operating under a reasonable computational budget.

[1]  Thierry Siméon,et al.  Sampling-Based Path Planning on Configuration-Space Costmaps , 2010, IEEE Transactions on Robotics.

[2]  Amarda Shehu,et al.  A stochastic roadmap method to model protein structural transitions , 2015, Robotica.

[3]  Amarda Shehu,et al.  From Optimization to Mapping: An Evolutionary Algorithm for Protein Energy Landscapes , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[4]  Kenneth A. De Jong,et al.  Off-lattice protein structure prediction with homologous crossover , 2013, GECCO '13.

[5]  Haruki Nakamura,et al.  Announcing the worldwide Protein Data Bank , 2003, Nature Structural Biology.

[6]  Timothy D Craggs,et al.  Alternating-laser excitation: single-molecule FRET and beyond. , 2014, Chemical Society reviews.

[7]  Amarda Shehu,et al.  Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method , 2013, BMC Structural Biology.

[8]  Erion Plaku,et al.  A Survey of Computational Treatments of Biomolecules by Robotics-Inspired Methods Modeling Equilibrium Structure and Dynamic , 2016, J. Artif. Intell. Res..

[9]  Jens Meiler,et al.  ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. , 2011, Methods in enzymology.

[10]  Amarda Shehu,et al.  Computing energy landscape maps and structural excursions of proteins , 2016, BMC Genomics.

[11]  Amarda Shehu,et al.  Efficient basin hopping in the protein energy surface , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

[12]  Amarda Shehu,et al.  A Data-Driven Evolutionary Algorithm for Mapping Multibasin Protein Energy Landscapes , 2015, J. Comput. Biol..

[13]  Ruth Nussinov,et al.  Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics , 2016, PLoS Comput. Biol..

[14]  Ruth Nussinov,et al.  Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm , 2015, PLoS Comput. Biol..

[15]  Kenneth A. De Jong,et al.  A Novel EA-based Memetic Approach for Efficiently Mapping Complex Fitness Landscapes , 2016, GECCO.

[16]  J. Onuchic,et al.  Funnels, pathways, and the energy landscape of protein folding: A synthesis , 1994, Proteins.

[17]  P. Wolynes,et al.  The energy landscapes and motions of proteins. , 1991, Science.

[18]  A. D. McLachlan,et al.  A mathematical procedure for superimposing atomic coordinates of proteins , 1972 .

[19]  Amarda Shehu,et al.  Interleaving Global and Local Search for Protein Motion Computation , 2015, ISBRA.

[20]  Mingjun Yang,et al.  Protein Conformational Dynamics , 2014, Advances in Experimental Medicine and Biology.

[21]  W. Greenleaf,et al.  High-resolution, single-molecule measurements of biomolecular motion. , 2007, Annual review of biophysics and biomolecular structure.

[22]  D. Kern,et al.  Dynamic personalities of proteins , 2007, Nature.

[23]  Mark A. Wilson,et al.  Intrinsic motions along an enzymatic reaction trajectory , 2007, Nature.

[24]  Bruno Robert,et al.  Conformational Switching in a Light-Harvesting Protein as Followed by Single-Molecule Spectroscopy , 2015, Biophysical journal.

[25]  Erion Plaku,et al.  Structure-Guided Protein Transition Modeling with a Probabilistic Roadmap Algorithm , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  De-Shuang Huang,et al.  Guest Editorial for Special Section on the 10th International Conference on Intelligent Computing (ICIC) , 2016, TCBB.

[27]  Brian S. Olson,et al.  Multi-Objective Optimization Techniques for Conformational Sampling in Template-Free Protein Structure Prediction , 2014 .

[28]  Erion Plaku,et al.  Computing transition paths in multiple-basin proteins with a probabilistic roadmap algorithm guided by structure data , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[29]  Amarda Shehu,et al.  Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface , 2012, Proteome Science.