Modeling protein structural transitions as a multiobjective optimization problem

Proteins of importance to human biology can populate significantly different three-dimensional (3d) structures at equilibrium. By doing so, a protein is able to interface with different molecules in the cell and so modulate its function. A structure-by-structure characterization of a protein's transition between two structures is central to elucidate the role of structural dynamics in regulating molecular interactions, understand the impact of sequence mutations on function, and design molecular therapeutics. Much wet- and dry-laboratory research is devoted to characterizing structural transitions. Computational approaches rely on constructing a full or partial, structured representation of the energy landscape that organizes structures by potential energy. The representation readily yields one or more paths that consist of series of structures connecting start and goal structures of interest. In this paper, we propose instead to cast the problem of computing transition paths as a multiobjective optimization one. We identify two desired characteristics of computed paths, energetic cost and structural resolution, and propose a novel evolutionary algorithm (EA) to compute low-cost and highresolution paths. The EA evolves paths representing a specific structural excursion without a priori constructing the energy landscape. Preliminary applications suggest the EA is effective while operating under a reasonable computational budget.

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

[2]  Michael Palmgren,et al.  Purifying selection acts on coding and non-coding sequences of paralogous genes in Arabidopsis thaliana , 2016, BMC Genomics.

[3]  Richard P. Feynman There's plenty of room at the bottom [data storage] , 1992, Journal of Microelectromechanical Systems.

[4]  Carla Mattos,et al.  A comprehensive survey of Ras mutations in cancer. , 2012, Cancer research.

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

[6]  H. Chan,et al.  Biophysics of protein evolution and evolutionary protein biophysics , 2014, Journal of The Royal Society Interface.

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

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

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

[10]  Yuko Okamoto,et al.  Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. , 2003, Journal of molecular graphics & modelling.

[11]  R. Feynman There’s plenty of room at the bottom , 2011 .

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

[13]  Elizabeth J. Denning,et al.  Zipping and unzipping of adenylate kinase: atomistic insights into the ensemble of open<-->closed transitions. , 2009, Journal of molecular biology.

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

[15]  Dinesh Manocha,et al.  Kinematic Manipulation of Molecular Chains Subject to Rigid Constraint , 1994, ISMB.

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

[17]  M. Karplus,et al.  Molecular dynamics and protein function. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

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

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

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

[22]  J. Mccammon,et al.  Exploring the role of receptor flexibility in structure-based drug discovery. , 2014, Biophysical chemistry.

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

[24]  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..

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

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

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

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

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

[30]  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..

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

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

[33]  Hans Frauenfelder,et al.  Protein dynamics and function: Insights from the energy landscape and solvent slaving , 2007, IUBMB life.

[34]  Samuel L. DeLuca,et al.  Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You , 2010, Biochemistry.

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

[36]  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).