This work was applied to studying the
enantioselectivity of various proteins.
Enantiomers are molecules that are non-superimposable mirror images of each other. Although they contain the same atom types and connectivity, enantiomers of a given chemical cannot be interconverted without breaking and reforming bonds. Molecules may contain multiple sites where this kind of asymmetry exists, in which case the molecule may exist as a whole family of
diasteroemers . Most biological molecules have at least one asymmetric center, and are therefore said to be
chiral , and in most cases, only one diastereomer or enantiomer exists in appreciable quantity. The chemistry of a pair of enantiomers is identical
except when they are interacting with other chiral molecules, in which case it is important that the correct diastereomer is present for the desired interaction.
Enantioselectivity is the ability of a protein to distinguish between the two enantiomers of a molecule. Since proteins are chiral, they exhibit enantioselectivity for enantiomeric ligands. In the tree-based method of Cortes et al, the amount of time it takes their planner to find an unbound state for a ligand turns out to be correlated with the difficulty of maneuvering the ligand into and out of the binding pocket. Thus, computation times for finding a path out of the binding pocket are much less for the preferred enantiomer of the ligand than for the other enatiomer, often by a factor of 10 or more.
Recommended reading
- A PRM-Based Approach
- Amato, N. M. and G. Song.
PDF . Using motion planning to study protein folding pathways. Journal of Computational Biology 9:149-168, 2002.
- Amato, N. M., K. A. Dill, and G. Song.
PDF . Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures. Journal of Computational Biology 10:239-255, 2003.
- Thomas, Shawna, Guang Song and Nancy M. Amato.
PDF . Protein Folding by Motion Planning. Physical Biology 2:S148-S155, 2005.
- Thomas, Shawna L., Xinyu Tang, Lydia Tapia, and Nancy M. Amato.
PDF . Simulating Protein Motions with Rigidity Analysis. Proceedings of the 2006 ACM International Conference on Research in Computational Molecular Biology (RECOMB), pp. 394-409.
- Strochastic Roadmap Simulations
- Apaydin, M.S., A. P. Singh, D. L. Brutlag and J.-C. Latombe.
PDF . Capturing Molecular Energy Landscapes with Probabilistic Conformational Roadmaps. Proceedings of the 2001 IEEE International Conference on Robotics and Automation, pp. 932-939.
- Apaydin, M. S., C.E. Guestrin, C. Varma, D.L. Brutlag, and J.-C. Latombe.
PDF . Stochastic roadmap simulation for the study of ligand-protein interactions. Bioinformatics, 18(s2):18-26, 2002.
- Apaydin, M. S., D. L. Brutlag, C. Guestrin, D. Hsu, J.-C. Latombe and C. Varma.
PDF . Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion. Journal of Computational Biology 10:257-281, 2003.
- Chiang, Tsung-Han, Mehmet Serkan Apaydin, Douglas L. Brutlag, David Hsu and Jean-Claude Latombe.
PDF . Predicting Experimental Quantities in Protein Folding Kinetics using Stochastic Roadmap Simulation. Proceedings of the 2006 ACM International Conference on Research in Computational Molecular Biology (RECOMB), pp. 410-424.
- Markovian State Models
- Singhal, N., C. D. Snow and V. S. Pande.
HTML . Using path sampling to build better Markovian state models: predicting the folding rate and mechanism of a tryptophan zipper beta hairpin. Journal of Chemical Physics 121:415-425, 2004.
- Singhal, Nina and Vijay S. Pande.
HTML . Error analysis and efficient sampling in Markovian state models for molecular dynamics. Journal of Chemical Physics 123:204909, 2005.
- Docking Pathways and Kinetics
- Cortes, J, T. Simeon, V. Ruiz de Angulo, D. Guieysse, M. Remauld-Simeon and V. Tran.
PDF . A Path Planning Approach for Computing Large-Amplitude Motions of Flexible Molecules. Bioinformatics 21(s1): i116-i125, 2005.
