Motion Planning and Biology
Table of Contents:
Introduction
One of my interests is statistical mechanics and large scale material simulations. One of the fundamental challenges of computational biology is that of sampling. The two conventional approaches are molecular dynamics and monte carlo simulations. This project uses a randomized sampling algorithm known as Probabilistic Roadmaps from robot motion planning to dock ligands to active sites. The docking action can be considered as the path of a robot through a narrow passage in high dimensional space.
Implementation
This project was done under the supervision of Dr. Kamal Gupta from the Robotic Algorithms and Motion Planning Laboratory (RAMP). They have a full featured motion planning library called Motion Planning Toolkit (MPK) which I modified for my purposes. Above you can see MPK being used for motion planning for a 7-dof robot at the International Space Station.
Biology
The motion planning approach is superior to existing approaches because it can estimate the motion of the ligand during the binding action. This corresponds to the “thermodynamic difficulty” of traversing that particular path. My test case was that of Oxamate with the protein 3PTB. The symmetry in the ligand makes the planning significantly easier.
Future
I think doing this project has given me an interesting perspective into the difficulty of docking algorithms. Even though I used a global minima trick from NMR Spectrography (read the documents,) the planning still proved to be challenging.
Various groups are doing active research on motion planning and computational biology. A good place to start is “A Motion Planning Approach to Flexible Ligand Binding” and look for papers that cite it. The ideas presented here were extended using Markov Chain Theory in Stochastic Roadmap Simulation (initial paper and other work.) The Folding@Home project uses some similar ideas for large trajectory analysis. Amato’s group does work on protein folding, ligand binding, RNA folding and neuroscience. Kavaraki’s group tries to identify motifs in databases, docking, drug discovery and other structural work.