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Motion planning algorithms for molecular simulations: a survey. (English) Zbl 1448.92004

Summary: Motion planning is a fundamental problem in robotics that has motivated research since more than three decades ago. A large variety of algorithms have been proposed to compute feasible motions of multi-body systems in constrained workspaces. In recent years, some of these algorithms have surpassed the frontiers of robotics, finding applications in other domains such as industrial manufacturing, computer animation and computational structural biology. This paper concerns the latter domain, providing a survey on motion planning algorithms applied to molecular modeling and simulation. Both the algorithmic and application sides are discussed, as well as the different issues to be taken into consideration when extending robot motion planning algorithms to deal with molecules. From an algorithmic perspective, the paper gives a general overview of the different extensions to sampling-based motion planners. From the point of view of applications, the survey deals with problems involving protein folding and conformational transitions, as well as protein-ligand interactions.

MSC:

92-08 Computational methods for problems pertaining to biology
92C40 Biochemistry, molecular biology
92C42 Systems biology, networks
92-02 Research exposition (monographs, survey articles) pertaining to biology

Software:

AutoDock; OBBTree

References:

[1] Rapaport, D. C., The Art of Molecular Dynamics Simulation (2007), Academic Press · Zbl 1098.81009
[2] Landau, D. P.; Binder, K., A Guide to Monte Carlo Simulations in Statistical Physics (2005), Cambridge University Press · Zbl 1086.82001
[3] Woolfson, M. M., An Introduction to X-ray Crystallography (1997), Cambridge University Press
[4] Cavanagh, J.; Fairbrother, W. J.; Palmer, A. G.; Skleton, N. J., Protein NMR Spectroscopy: Principles and Practices (2006), Royal Society of Chemistry
[5] Robustelli, P.; Kohlhoff, K.; Cavalli, A.; Vendruscolo, M., Using NMR chemical shifts as structural restraints in molecular dynamics simulations of proteins, Structure, 18, 8, 923-933 (2010)
[6] Bonneau, R.; Baker, D., Ab initio protein structure prediction: progress and prospects, Annual Review of Biophysics and Biomolecular Structure, 30, 1, 173-189 (2001)
[7] Lengauer, T.; Rarey, M., Computational methods for biomolecular docking, Current Opinion in Structural Biology, 6, 3, 402-406 (1996)
[8] Pain, R. H., (Mechanisms of Protein Folding. Mechanisms of Protein Folding, Frontiers in Molecular Biology (2000), Oxford University Press)
[9] Muñoz, V., (Protein Folding, Misfolding and Aggregation: Classical Themes and Novel Approaches. Protein Folding, Misfolding and Aggregation: Classical Themes and Novel Approaches, RSC Biomolecular Sciences (2008), Royal Society of Chemistry)
[10] Sugita, Y.; Okamoto, Y., Replica-exchange molecular dynamics method for protein folding, Chemical Physics Letters, 314, 1-2, 141-151 (1999)
[11] Marinari, E.; Parisi, G., Simulated tempering: a new Monte Carlo scheme, Europhysics Letters, 19, 6, 451-458 (1992)
[12] Laio, A.; Parrinello, M., Escaping free-energy minima, Proceedings of the National Academy of Sciences of the United States of America, 99, 20, 12562-12566 (2002)
[13] Shaw, D. E.; Maragakis, P.; Lindorff-Larsen, K.; Piana, S.; Dror, R. O.; Eastwood, M. P.; Bank, J. A.; Jumper, J. M.; Salmon, J. K.; Shan, Y.; Wriggers, W., Atomic-level characterization of the structural dynamics of proteins, Science, 330, 6002, 341-346 (2010)
[14] LaValle, S. M., Planning Algorithms (2006), Cambridge University Press · Zbl 1100.68108
[15] Choset, H.; Lynch, K. M.; Hutchinson, S.; Kantor, G.; Burgard, W.; Kavraki, L. E.; Thrun, S., (Principles of Robot Motion: Theory, Algorithms, and Implementation. Principles of Robot Motion: Theory, Algorithms, and Implementation, Intelligent Robotics and Autonomous Agents (2005), MIT Press) · Zbl 1081.68700
[16] Tsianos, K. I.; Sucan, I. A.; Kavraki, L. E., Sampling-based robot motion planning: towards realistic applications, Computer Science Review, 1, 2-11 (2007)
[17] D. Parsons, J.F. Canny, Geometric problems in molecular biology and robotics, in: Proceedings of the International Conference on Intelligent Systems for Molecular Biology, 1994, pp. 322-330.; D. Parsons, J.F. Canny, Geometric problems in molecular biology and robotics, in: Proceedings of the International Conference on Intelligent Systems for Molecular Biology, 1994, pp. 322-330.
[18] Selkoe, D. J., Folding proteins in fatal ways, Nature, 426, 6968, 900-904 (2003)
[19] Moll, M.; Schwarz, D.; Kavraki, L. E., (Roadmap Methods for Protein Folding. Roadmap Methods for Protein Folding, Protein Structure Prediction: Methods and Protocols (2007), Humana Press)
[20] L.E. Kavraki, Geometric methods in structural computational biology. URL: http://cnx.org/content/col10344/1.6/; L.E. Kavraki, Geometric methods in structural computational biology. URL: http://cnx.org/content/col10344/1.6/
[21] Gipson, B.; Hsu, D.; Kavraki, L. E.; Latombe, J.-C., Computational models of protein kinematics and dynamics: beyond simulation, Annual Review of Analytical Chemistry, 5, 273-291 (2012)
[22] Kavraki, L. E.; Svestka, P.; Latombe, J.-C.; Overmars, M. H., Probabilistic roadmaps for path planning in high-dimensional configuration spaces, IEEE Transactions on Robotics and Automation, 12, 4, 566-580 (1996)
[23] S.M. LaValle, J.J. Kuffner, Rapidly-exploring random trees: progress and prospects, in: Algorithmic and Computational Robotics: New Directions: The Fourth Workshop on the Algorithmic Foundations of Robotics, 2001, pp. 293-308.; S.M. LaValle, J.J. Kuffner, Rapidly-exploring random trees: progress and prospects, in: Algorithmic and Computational Robotics: New Directions: The Fourth Workshop on the Algorithmic Foundations of Robotics, 2001, pp. 293-308. · Zbl 0986.68151
[24] Latombe, J.-C., Robot Motion Planning (1990), Kluwer Academic Publishers
[25] Schwartz, J. T.; Sharir, M., On the piano movers’ problem I. The case of a two-dimensional rigid polygonal body moving amidst polygonal barriers, Communications on Pure and Applied Mathematics, 36, 3, 345-398 (1983) · Zbl 0554.51007
[26] Lozano-Peréz, T., Spatial planning: a configuration space approach, IEEE Transactions on Computers, 32, 2, 108-120 (1983) · Zbl 0513.68081
[27] Goldberg, K., Completeness in robot motion planning, (Proceedings of the Workshop on Algorithmic Foundations of Robotics (1995), A.K. Peters, Ltd.: A.K. Peters, Ltd. Natick, MA, USA), 419-429 · Zbl 0830.70003
[28] Reif, J. H., Complexity of the mover’s problem and generalizations, (Proceedings of the 20th Annual Symposium on Foundations of Computer Science (1979), IEEE Computer Society), 421-427
[29] Canny, J. F., The Complexity of Robot Motion Planning (1988), MIT Press: MIT Press Cambridge, MA, USA
[30] Lindemann, S. R.; LaValle, S. M., Current issues in sampling-based motion planning, Robotics Research, 36-54 (2005)
[31] Geraerts, R.; Overmars, M., A comparative study of probabilistic roadmap planners, (Algorithmic Foundations of Robotics V (2004)), 43-58
[32] LaValle, S. M.; Kuffner, J. J., Randomized kinodynamic planning, The International Journal of Robotics Research, 20, 5, 378-400 (2001)
[33] Dijkstra, E. W., A note on two problems in connection with graphs, Numerische Mathematik, 1, 1, 269-271 (1959) · Zbl 0092.16002
[34] Hart, P. E.; Nilsson, N. J.; Raphael, B., Correction to “A formal basis for the heuristic determination of minimum cost paths”, ACM SIGART Bulletin, 37, 28-29 (1972)
[35] N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, D. Vallejo, OBPRM: an obstacle-based PRM for 3D workspaces, in: Robotics: The Algorithmic Perspective: 1998 Workshop on the Algorithmic Foundations of Robotics, 1998, pp. 155-168.; N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, D. Vallejo, OBPRM: an obstacle-based PRM for 3D workspaces, in: Robotics: The Algorithmic Perspective: 1998 Workshop on the Algorithmic Foundations of Robotics, 1998, pp. 155-168. · Zbl 0949.70508
[36] Simeon, T.; Laumond, J. P.; Nissoux, C., Visibility-based probabilistic roadmaps for motion planning, Advanced Robotics, 14, 6, 477-493 (2000)
[37] S.A. Wilmarth, N.M. Amato, P.F. Stiller, MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, 2002, pp. 1024-1031.; S.A. Wilmarth, N.M. Amato, P.F. Stiller, MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, 2002, pp. 1024-1031.
[38] Sánchez, G.; Latombe, J.-C., A single-query bi-directional probabilistic roadmap planner with lazy collision checking, Robotics Research, 403-417 (2003)
[39] J.J. Kuffner, S.M. LaValle, RRT-connect: an efficient approach to single-query path planning, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, 2000, pp. 995-1001.; J.J. Kuffner, S.M. LaValle, RRT-connect: an efficient approach to single-query path planning, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2, 2000, pp. 995-1001.
[40] J. Bruce, M. Veloso, Real-time randomized path planning for robot navigation, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, 2002, pp. 2383-2388.; J. Bruce, M. Veloso, Real-time randomized path planning for robot navigation, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, 2002, pp. 2383-2388.
[41] P. Cheng, S.M. LaValle, Resolution complete rapidly-exploring random trees, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, 2002, pp. 267-272.; P. Cheng, S.M. LaValle, Resolution complete rapidly-exploring random trees, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, 2002, pp. 267-272.
[42] S. Rodriguez, X. Tang, J.M. Lien, N.M. Amato, An obstacle-based rapidly-exploring random tree, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2006, pp. 895-900.; S. Rodriguez, X. Tang, J.M. Lien, N.M. Amato, An obstacle-based rapidly-exploring random tree, in: Proceedings of the IEEE International Conference on Robotics and Automation, 2006, pp. 895-900.
[43] D. Hsu, J.-C. Latombe, R. Motwani, Path planning in expansive configuration spaces, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, 1997, pp. 2719-2726.; D. Hsu, J.-C. Latombe, R. Motwani, Path planning in expansive configuration spaces, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, 1997, pp. 2719-2726.
[44] Ladd, A. M.; Kavraki, L. E., (Fast Tree-based Exploration of State Space for Robots with Dynamics. Fast Tree-based Exploration of State Space for Robots with Dynamics, Algorithmic Foundations of Robotics VI (2005), Springer), 297-312
[45] Şucan, I. A.; Kavraki, L. E., Kinodynamic motion planning by interior-exterior cell exploration, (Algorithmic Foundation of Robotics VIII, vol. 57 (2009)), 449-464 · Zbl 1215.93104
[46] Leach, A. R., Molecular Modelling: Principles and Applications (2001), Pearson Education
[47] Koliński, A., Multiscale Approaches to Protein Modeling (2010), Springer Verlag
[48] Berman, H. M.; Battistuz, T.; Bhat, T. N.; Bluhm, W. F.; Bourne, P. E.; Burkhardt, K.; Feng, Z.; Gilliland, G. L.; Iype, L.; Jain, S., The protein data bank, Acta Crystallographica Section D: Biological Crystallography, 58, 6, 899-907 (2002)
[49] Schlick, T., Molecular Modeling and Simulation: An Interdisciplinary Guide, vol. 21 (2010), Springer Verlag · Zbl 1320.92007
[50] Scott, R. A.; Scheraga, H. A., Conformational analysis of macromolecules. II. The rotational isomeric states of the normal hydrocarbons, Journal of Chemical Physics, 44, 3054 (1966)
[51] Manocha, D.; Zhu, Y.; Wright, W., Conformational analysis of molecular chains using nano-kinematics, Computer Applications in the Biosciences: CABIOS, 11, 1, 71-86 (1995)
[52] LaValle, S. M.; Finn, P. W.; Kavraki, L. E.; Latombe, J.-C., A randomized kinematics-based approach to pharmacophore-constrained conformational search and database screening, Journal of Computational Chemistry, 21, 9, 731-747 (2000)
[53] Zhang, M.; Kavraki, L. E., A new method for fast and accurate derivation of molecular conformations, Journal of Chemical Information and Computer Sciences, 42, 1, 64-70 (2002)
[54] Noonan, K.; O’Brien, D.; Snoeyink, J., Probik: protein backbone motion by inverse kinematics, The International Journal of Robotics Research, 24, 11, 971-982 (2005)
[55] Xie, M., (Fundamentals of Robotics: Linking Perception to Action. Fundamentals of Robotics: Linking Perception to Action, Series in Machine Perception and Artificial Intelligence (2003), World Scientific Pub.)
[56] Angeles, J., (Fundamentals of Robotic Mechanical Systems: Theory, Methods, and Algorithms. Fundamentals of Robotic Mechanical Systems: Theory, Methods, and Algorithms, Mechanical Engineering Series (2007), Springer) · Zbl 1140.70001
[57] Sciavicco, L.; Siciliano, B., (Modelling and Control of Robot Manipulators. Modelling and Control of Robot Manipulators, Advanced Textbooks in Control and Signal Processing (2001), Springer) · Zbl 0944.70001
[58] Spong, M. W.; Hutchinson, S.; Vidyasagar, M., Robot Modeling and Control (2006), John Wiley & Sons
[59] M.L. Teodoro, G.N. Phillips Jr., L.E. Kavraki, Molecular docking: a problem with thousands of degrees of freedom, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, 2001, pp. 960-965.; M.L. Teodoro, G.N. Phillips Jr., L.E. Kavraki, Molecular docking: a problem with thousands of degrees of freedom, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, 2001, pp. 960-965.
[60] Cavasotto, C. N.; Orry, A. J.W.; Abagyan, R. A., The challenge of considering receptor flexibility in ligand docking and virtual screening, Current Computer-Aided Drug Design, 1, 4, 423-440 (2005)
[61] Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R., Development and validation of a genetic algorithm for flexible docking, Journal of Molecular Biology, 267, 3, 727-748 (1997)
[62] Apostolakis, J.; Plückthun, A.; Caflisch, A., Docking small ligands in flexible binding sites, Journal of Computational Chemistry, 19, 1, 21-37 (1998)
[63] Pak, Y.; Wang, S., Application of a molecular dynamics simulation method with a generalized effective potential to the flexible molecular docking problems, The Journal of Physical Chemistry B, 104, 2, 354-359 (2000)
[64] Thomas, S.; Tang, X.; Tapia, L.; Amato, N. M., Simulating protein motions with rigidity analysis, Journal of Computational Biology, 14, 6, 839-855 (2007)
[65] Thorpe, M. F.; Duxbury, P. M., (Rigidity Theory and Applications (1999), Springer: Springer US)
[66] Wells, S.; Menor, S.; Hespenheide, B.; Thorpe, M. F., Constrained geometric simulation of diffusive motion in proteins, Physical Biology, 2, 127-136 (2005)
[67] Cortés, J.; Le, D. T.; Iehl, R.; Siméon, T., Simulating ligand-induced conformational changes in proteins using a mechanical disassembly method, Physical Chemistry Chemical Physics, 12, 29, 8268-8276 (2010)
[68] I.K. Fodor, A survey of dimension reduction techniques, Tech. Rep. UCRL-ID-148494, Lawrence Livermore National Lab, June 2002.; I.K. Fodor, A survey of dimension reduction techniques, Tech. Rep. UCRL-ID-148494, Lawrence Livermore National Lab, June 2002.
[69] L.J.P. van der Maaten, E.O. Postma, H.J. van den Herik, Dimensionality reduction: a comparative review, Tech. Rep. TiCC-TR 2009-005, Tilburg University, 2009.; L.J.P. van der Maaten, E.O. Postma, H.J. van den Herik, Dimensionality reduction: a comparative review, Tech. Rep. TiCC-TR 2009-005, Tilburg University, 2009.
[70] Jolliffe, I. T., Principal Component Analysis (2002), Springer Verlag · Zbl 1011.62064
[71] Das, P.; Moll, M.; Stamati, H.; Kavraki, L. E.; Clementi, C., Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction, Proceedings of the National Academy of Sciences, 103, 26, 9885-9890 (2006)
[72] Altis, A.; Nguyen, P. H.; Hegger, R.; Stock, G., Dihedral angle principal component analysis of molecular dynamics simulations, The Journal of Chemical Physics, 126, 24, 244111 (2007)
[73] Mu, Y.; Nguyen, P. H.; Stock, G., Energy landscape of a small peptide revealed by dihedral angle principal component analysis, Proteins: Structure, Function, and Bioinformatics, 58, 1, 45-52 (2005)
[74] Tenenbaum, J. B.; Silva, V.; Langford, J. C., A global geometric framework for nonlinear dimensionality reduction, Science, 290, 5500, 2319-2323 (2000)
[75] Plaku, E.; Stamati, H.; Clementi, C.; Kavraki, L. E., Fast and reliable analysis of molecular motion using proximity relations and dimensionality reduction, Proteins: Structure, Function, and Bioinformatics, 67, 4, 897-907 (2007)
[76] Cui, Q.; Bahar, I., (Normal Mode Analysis: Theory and Applications to Biological and Chemical Systems. Normal Mode Analysis: Theory and Applications to Biological and Chemical Systems, Chapman and Hall/CRC Mathematical and Computational Biology Series (2006), Chapman & Hall, CRC)
[77] Hinsen, K., Analysis of domain motions by approximate normal mode calculations, Proteins: Structure, Function, and Bioinformatics, 33, 3, 417-429 (1998)
[78] Tama, F.; Sanejouand, Y. H., Conformational change of proteins arising from normal mode calculations, Protein Engineering, 14, 1, 1-6 (2001)
[79] Kirillova, S.; Cortés, J.; Stefaniu, A.; Siméon, T., An NMA-guided path planning approach for computing large-amplitude conformational changes in proteins, Proteins: Structure, Function, and Bioinformatics, 70, 1, 131-143 (2008)
[80] Rao, S. T.; Rossmann, M. G., Comparison of super-secondary structures in proteins, Journal of Molecular Biology, 76, 2, 241-256 (1973)
[81] Rossmann, M. G.; Argos, P., Exploring structural homology of proteins, Journal of Molecular Biology, 105, 1, 75-95 (1976)
[82] Falicov, A.; Cohen, F. E., A surface of minimum area metric for the structural comparison of proteins, Journal of Molecular Biology, 258, 5, 871-892 (1996)
[83] Holm, L.; Sander, C., Protein structure comparison by alignment of distance matrices, Journal of Molecular Biology, 233, 1, 123-138 (1993)
[84] Wallin, S.; Farwer, J.; Bastolla, U., Testing similarity measures with continuous and discrete protein models, Proteins: Structure, Function, and Bioinformatics, 50, 1, 144-157 (2003)
[85] Lotan, I.; Schwarzer, F., Approximation of protein structure for fast similarity measures, Journal of Computational Biology, 11, 2-3, 299-317 (2004)
[86] Shehu, A.; Olson, B., Guiding the search for native-like protein conformations with an ab-initio tree-based exploration, International Journal of Robotics Research, 29, 8, 1106-1127 (2010)
[87] J. Cortés, L. Jaillet, T. Siméon, Molecular disassembly with RRT-like algorithms, in: IEEE International Conference on Robotics and Automation, 2007, pp. 3301-3306.; J. Cortés, L. Jaillet, T. Siméon, Molecular disassembly with RRT-like algorithms, in: IEEE International Conference on Robotics and Automation, 2007, pp. 3301-3306.
[88] Jiménez, P.; Thomas, F.; Torras, C., 3D collision detection: a survey, Computers & Graphics, 25, 2, 269-285 (2001)
[89] Lin, M. C.; Manocha, D., Collision and proximity queries, (Handbook of Discrete and Computational Geometry (2003))
[90] Gottschalk, S.; Lin, M. C.; Manocha, D., OBBTree: a hierarchical structure for rapid interference detection, (Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (1996), ACM), 171-180
[91] van den Bergen, G., Efficient collision detection of complex deformable models using AABB trees, Journal of Graphics Tools, 2, 4, 1-13 (1998) · Zbl 0927.68100
[92] Cohen, J. D.; Lin, M. C.; Manocha, D.; Ponamgi, M., \(I\)-COLLIDE: an interactive and exact collision detection system for large-scale environments, (Proceedings of the Symposium on Interactive 3D Graphics (1995), ACM), 189-196
[93] Soss, M.; Erickson, J.; Overmars, M., Preprocessing chains for fast dihedral rotations is hard or even impossible, Computational Geometry, 26, 3, 235-246 (2003) · Zbl 1151.82446
[94] Agarwal, P.; Guibas, L.; Nguyen, A.; Russel, D.; Zhang, L., Collision detection for deforming necklaces, Computational Geometry, 28, 2-3, 137-163 (2004) · Zbl 1077.68105
[95] Lotan, I.; Schwarzer, F.; Halperin, D.; Latombe, J.-C., Efficient maintenance and self-collision testing for kinematic chains, (Proceedings of the Eighteenth Annual Symposium on Computational Geometry (2002), ACM), 43-52 · Zbl 1414.68132
[96] Ruiz de Angulo, V.; Cortés, J.; Siméon, T., BioCD: an efficient algorithm for self-collision and distance computation between highly articulated molecular models, (Robotics: Science and Systems I (2005), MIT Press), 241-248
[97] Rangwala, H.; Karypis, G., (Protein Structure Methods and Algorithms. Protein Structure Methods and Algorithms, Wiley Series in Bioinformatics: Computational Techniques and Engineering, vol. 14 (2010), John Wiley & Sons)
[98] Coutsias, E. A.; Seok, C.; Jacobson, M. P.; Dill, K. A., A kinematic view of loop closure, Journal of Computational Chemistry, 25, 4, 510-528 (2004)
[99] Kolodny, R.; Guibas, L.; Levitt, M.; Koehl, P., Inverse kinematics in biology: the protein loop closure problem, International Journal of Robotics Research, 24, 2-3, 151-163 (2005)
[100] Cortés, J.; Siméon, T., Sampling-based motion planning under kinematic loop-closure constraints, (Algorithmic Foundations of Robotics VI (2005)), 75-90
[101] Cortés, J.; Siméon, T.; Ruiz de Angulo, V.; Guieysse, D.; Remaud-Siméon, M.; Tran, V., A path planning approach for computing large-amplitude motions of flexible molecules, Bioinformatics, 21, Suppl. 1, i116-i125 (2005)
[102] Yao, P.; Dhanik, A.; Marz, N.; Propper, R.; Kou, C.; Liu, G.; van den Bedem, H.; Latombe, J.-C.; Halperin-Landsberg, I.; Altman, R. B., Efficient algorithms to explore conformation spaces of flexible protein loops, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 5, 534-545 (2008)
[103] Canutescu, A. A.; Dunbrack, R. L., Cyclic coordinate descent: a robotics algorithm for protein loop closure, Protein Science, 12, 5, 963-972 (2003)
[104] Griffiths, D. J., Introduction to Quantum Mechanics (2005), Pearson Prentice Hall
[105] Burkert, U.; Allinger, N. L., Molecular Mechanics (1982), American Chemical Society
[106] Ponder, J. W.; Case, D. A., Force fields for protein simulations, Advances in Protein Chemistry, 66, 27-85 (2003)
[107] Mackerell, A. D., Empirical force fields for biological macromolecules: overview and issues, Journal of Computational Chemistry, 25, 13, 1584-1604 (2004)
[108] Tozzini, V., Coarse-grained models for proteins, Current Opinion in Structural Biology, 15, 2, 144-150 (2005)
[109] Monticelli, L.; Kandasamy, S. K.; Periole, X.; Larson, R. G.; Tieleman, D. P.; Marrink, S. J., The MARTINI coarse-grained force field: extension to proteins, Journal of Chemical Theory and Computation, 4, 5, 819-834 (2008)
[110] Derreumaux, P., From polypeptide sequences to structures using Monte Carlo simulations and an optimized potential, Journal of Chemical Physics, 111, 5, 2301-2310 (1999)
[111] Singh, A. P.; Latombe, J.-C.; Brutlag, D. L., A motion planning approach to flexible ligand binding, (Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology (1999), AAAI Press), 252-261
[112] M.S. Apaydin, A.P. Singh, D.L. Brutlag, J.-C. Latombe, Capturing molecular energy landscapes with probabilistic conformational roadmaps, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, 2001, pp. 932-939.; M.S. Apaydin, A.P. Singh, D.L. Brutlag, J.-C. Latombe, Capturing molecular energy landscapes with probabilistic conformational roadmaps, in: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, 2001, pp. 932-939.
[113] Apaydin, M. S.; Guestrin, C. E.; Varma, C.; Brutlag, D. L.; Latombe, J.-C., Stochastic roadmap simulation for the study of ligand-protein interactions, Bioinformatics, 18, Suppl. 2, S18-S26 (2002)
[114] Apaydin, M. S.; Brutlag, D. L.; Guestrin, C.; Hsu, D.; Latombe, J.-C.; Varma, C., Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion, Journal of Computational Biology, 10, 3-4, 257-281 (2003)
[115] Apaydin, M. S.; Brutlag, D. L.; Hsu, D.; Latombe, J.-C., Stochastic conformational roadmaps for computing ensemble properties of molecular motion, (Algorithmic Foundations of Robotics V (2004)), 131-147
[116] Chiang, T. H.; Apaydin, M.; Brutlag, D.; Hsu, D.; Latombe, J.-C., Predicting experimental quantities in protein folding kinetics using stochastic roadmap simulation, (Research in Computational Molecular Biology (2006), Springer), 410-424 · Zbl 1302.92031
[117] Chiang, T. H.; Apaydin, M. S.; Brutlag, D. L.; Hsu, D.; Latombe, J.-C., Using stochastic roadmap simulation to predict experimental quantities in protein folding kinetics: folding rates and phi-values, Journal of Computational Biology, 14, 5, 578-593 (2007)
[118] Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; Teller, E., Equation of state calculations by fast computing machines, Journal of Chemical Physics, 21, 6, 1087 (1953) · Zbl 1431.65006
[119] Frenkel, D.; Smit, B., Understanding Molecular Simulations: From Algorithms to Applications (2002), Academic Press
[120] Amato, N. M.; Song, G., Using motion planning to study protein folding pathways, Journal of Computational Biology, 9, 2, 149-168 (2002)
[121] G. Song, S. Thomas, K.A. Dill, J.M. Scholtz, N.M. Amato, A path planning-based study of protein folding with a case study of hairpin formation in protein \(GL\); G. Song, S. Thomas, K.A. Dill, J.M. Scholtz, N.M. Amato, A path planning-based study of protein folding with a case study of hairpin formation in protein \(GL\) · Zbl 1253.92023
[122] Amato, N. M.; Dill, K. A.; Song, G., Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures, Journal of Computational Biology, 10, 3-4, 239-255 (2003)
[123] Thomas, S.; Song, G.; Amato, N. M., Protein folding by motion planning, Physical Biology, 2, 148-155 (2005)
[124] Tang, X.; Kirkpatrick, B.; Thomas, S.; Song, G.; Amato, N. M., Using motion planning to study RNA folding kinetics, Journal of Computational Biology, 12, 6, 862-881 (2005)
[125] Tapia, L.; Tang, X.; Thomas, S.; Amato, N. M., Kinetics analysis methods for approximate folding landscapes, Bioinformatics, 23, 13, 539-548 (2007)
[126] Tang, X.; Thomas, S.; Tapia, L.; Giedroc, D. P.; Amato, N. M., Simulating RNA folding kinetics on approximated energy landscapes, Journal of Molecular Biology, 381, 4, 1055-1067 (2008)
[127] Tapia, L.; Thomas, S.; Amato, N. M., A motion planning approach to studying molecular motions, Communications and Information Systems, 10, 1, 53-68 (2010) · Zbl 1185.92007
[128] Yang, H.; Wu, H.; Li, D.; Han, L.; Huo, S., Temperature-dependent probabilistic roadmap algorithm for calculating variationally optimized conformational transition pathways, Journal of Chemical Theory and Computation, 3, 1, 17-25 (2007)
[129] Li, D.; Yang, H.; Han, L.; Huo, S., Predicting the folding pathway of engrailed homeodomain with a probabilistic roadmap enhanced reaction-path algorithm, Biophysical Journal, 94, 5, 1622-1629 (2008)
[130] Cortés, J.; Siméon, T.; Remaud-Siméon, M.; Tran, V., Geometric algorithms for the conformational analysis of long protein loops, Journal of Computational Chemistry, 25, 7, 956-967 (2004)
[131] Enosh, A.; Raveh, B.; Furman-Schueler, O.; Halperin, D.; Ben-Tal, N., Generation, comparison, and merging of pathways between protein conformations: gating in \(K\)-channels, Biophysical Journal, 95, 8, 3850-3860 (2008)
[132] Raveh, B.; Enosh, A.; Schueler-Furman, O.; Halperin, D., Rapid sampling of molecular motions with prior information constraints, PLoS Computational Biology, 5, 2, e1000295 (2009)
[133] Cortés, J.; Jaillet, L.; Siméon, T., Disassembly path planning for complex articulated objects, IEEE Transactions on Robotics, 24, 2, 475-481 (2008)
[134] Jaillet, L.; Cortés, J.; Siméon, T., Sampling-based path planning on configuration-space costmaps, IEEE Transactions on Robotics, 26, 4, 635-646 (2010)
[135] Jaillet, L.; Corcho, F. J.; Pérez, J. J.; Cortés, J., Randomized tree construction algorithm to explore energy landscapes, Journal of Computational Chemistry, 32, 16, 3464-3474 (2011)
[136] R. Iehl, J. Cortés, T. Siméon, Costmap planning in high dimensional configuration spaces, in: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, August 2012 (in press).; R. Iehl, J. Cortés, T. Siméon, Costmap planning in high dimensional configuration spaces, in: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, August 2012 (in press).
[137] Haspel, N.; Moll, M.; Baker, M.; Chiu, W.; Kavraki, L. E., Tracing conformational changes in proteins, BMC Structural Biology, 10, Suppl. 1, S1 (2010)
[138] Barbe, S.; Cortés, J.; Siméon, T.; Monsan, P.; Remaud-Siméon, M.; André, I., A mixed molecular modelling—robotics approach to investigate lipase large molecular motions, Proteins: Structure, Function, and Bioinformatics, 79, 8, 2517-2529 (2011)
[139] Maragakis, P.; Karplus, M., Large amplitude conformational change in proteins explored with a plastic network model: adenylate kinase, Journal of Molecular Biology, 352, 807-822 (2005)
[140] Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P., Optimization by simulated annealing, Science, 220, 4598, 671-680 (1983) · Zbl 1225.90162
[141] Dobson, C. M., Protein folding and misfolding, Nature, 426, 6968, 884-890 (2003)
[142] Zaki, M. J.; Bystroff, C., (Protein Structure Prediction. Protein Structure Prediction, Methods in Molecular Biology (2008), Humana Press)
[143] Balbach, J.; Forge, V.; van Nuland, N. A.J.; Winder, S. L.; Hore, P. J.; Dobson, C. M., Following protein folding in real time using NMR spectroscopy, Nature Structural & Molecular Biology, 2, 10, 865-870 (1995)
[144] Dyson, H. J.; Wright, P. E., Unfolded proteins and protein folding studied by NMR, Chemical Reviews, 104, 8, 3607-3622 (2004)
[145] Chan, C. K.; Hu, Y.; Takahashi, S.; Rousseau, D. L.; Eaton, W. A.; Hofrichter, J., Submillisecond protein folding kinetics studied by ultrarapid mixing, Proceedings of the National Academy of Sciences of the United States of America, 94, 5, 1779-1784 (1997)
[146] Jones, C. M.; Henry, E. R.; Hu, Y.; Chan, C. K.; Luck, S. D.; Bhuyan, A.; Roder, H.; Hofrichter, J.; Eaton, W. A., Fast events in protein folding initiated by nanosecond laser photolysis, Proceedings of the National Academy of Sciences of the United States of America, 90, 24, 11860-11864 (1993)
[147] Unger, R.; Moult, J., Genetic algorithms for protein folding simulations, Journal of Molecular Biology, 231, 1, 75-81 (1993)
[148] Onuchic, J. N.; Wolynes, P. G., Theory of protein folding, Current Opinion in Structural Biology, 14, 1, 70-75 (2004)
[149] Dill, K. A.; Ozkan, S. B.; Shell, M. S.; Weikl, T. R., The protein folding problem, Annual Review of Biophysics, 37, 289-316 (2008)
[150] Bryngelson, J. D.; Onuchic, J. N.; Socci, N. D.; Wolynes, P. G., Funnels, pathways, and the energy landscape of protein folding: a synthesis, Proteins: Structure, Function, and Bioinformatics, 21, 3, 167-195 (1995)
[151] Goodsell, D. S.; Morris, G. M.; Olson, A. J., Automated docking of flexible ligands: applications of autodock, Journal of Molecular Recognition, 9, 1, 1-5 (1996)
[152] Lang, P. T.; Brozell, S. R.; Mukherjee, S.; Pettersen, E. F.; Meng, E. C.; Thomas, V.; Rizzo, R. C.; Case, D. A.; James, T. L.; Kuntz, I. D., DOCK 6: combining techniques to model RNA—small molecule complexes, RNA, 15, 6, 1219-1230 (2009)
[153] Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G., A fast flexible docking method using an incremental construction algorithm, Journal of Molecular Biology, 261, 3, 470-489 (1996)
[154] Jones, G.; Willett, P.; Glen, R. C.; Leach, A. R.; Taylor, R., Development and validation of a genetic algorithm for flexible docking, Journal of Molecular Biology, 267, 3, 727-748 (1997)
[155] Abagyan, R.; Totrov, M.; Kuznetsov, D., ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation, Journal of Computational Chemistry, 15, 5, 488-506 (1994)
[156] Goldberg, D. E., Genetic Algorithms in Search, Optimization, and Machine Learning (1989), Addison-Wesley · Zbl 0721.68056
[157] Hajduk, P. J.; Greer, J., A decade of fragment-based drug design: strategic advances and lessons learned, Nature Reviews Drug Discovery, 6, 3, 211-219 (2007)
[158] Sousa, S. F.; Fernandes, P. A.; Ramos, M. J., Protein-ligand docking: current status and future challenges, Proteins: Structure, Function, and Bioinformatics, 65, 1, 15-26 (2006)
[159] Guieysse, D.; Cortés, J.; Puech-Guenot, S.; Barbe, S.; Lafaquière, V.; Monsan, P.; Siméon, T.; André, I.; Remaud-Siméon, M., A structure-controlled investigation of lipase enantioselectivity by a path-planning approach, ChemBioChem, 9, 8, 1308-1317 (2008)
[160] Lafaquière, V.; Barbe, S.; Puech-Guenot, S.; Guieysse, D.; Cortés, J.; Monsan, P.; Siméon, T.; André, I.; Remaud-Siméon, M., Control of lipase enantioselectivity by engineering the substrate binding site and access channel, ChemBioChem, 10, 17, 2760-2771 (2009)
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