PRM based Protein Folding

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Presentation transcript:

PRM based Protein Folding CS365:Artificial Intelligence Era Jain (Y9209) Romil Gadia (Y9496)

Problem Statement Motivation???

Protein Folding & Articulated Robot

Protein Folding & Articulated Robot

Importance of map reduction

Importance of map reduction

Map Reduction 2 step process: 1)Sampling of nodes 2)Connection of nodes

Node Sampling Sampled States Angles(phi,psi) perturbed Native State (coordinates) Native State (angles) Sampled States Angles(phi,psi) perturbed (Coordinates) Corresponding sampled states as nodes Filtered Energies Energies (Sampled States)

Node Sampling Sampled States Angles(phi,psi) perturbed Native State (coordinates) Native State (angles) Sampled States Angles(phi,psi) perturbed (Coordinates) Corresponding sampled states as nodes Filtered Energies Energies (Sampled States)

Node Sampling Sampled States Angles(phi,psi) perturbed Native State (coordinates) Native State (angles) Sampled States Angles(phi,psi) perturbed (Coordinates) Corresponding sampled states as nodes Filtered Energies Energies (Sampled States)

Node Sampling Formula

Node Sampling Sampled States Angles(phi,psi) perturbed Native State (coordinates) Native State (angles) Sampled States Angles(phi,psi) perturbed (Coordinates) Corresponding sampled states as nodes Filtered Energies Energies (Sampled States)

Energies Filtered Energies

Node Sampling Sampled States Angles(phi,psi) perturbed Native State (coordinates) Native State (angles) Sampled States Angles(phi,psi) perturbed (Coordinates) Corresponding sampled states as nodes Filtered Energies Energies (Sampled States)

Node Connection Generating intermediate nodes between neighbors Sampled Nodes(Nodes) (Angles) k-nearest neighbors for each node Energies of intermediate nodes Transition probabilities between intermediate nodes and original nodes Graph with edges (weights as per energetic feasibilty) Weights of edges

Node Connection Formula

Querying the Roadmap Protein Folding – Stochastic Process Dijkstra’s Algorithm v/s Monte-Carlo Simulation

Our Progress so far... Generated torsional angles from the native state pdb file Generated about 6000 nodes (conformations) via Gaussian Sampling Calculated energies for each of these conformations. Filtered the nodes based on their energies In short we are done with sampling. We have to work on node connection (edge weight calculation) For parts 2, 3, 4 we wrote the code. For part 1, we are using a python library[4]

References [1 ] A Motion Planning Approach to Studying Molecular Motions, Lydia Tapia, Shawna Thomas, Nancy M. Amato, Communications in Information and Systems, 10(1):53-68, 2010. Also, Technical Report, TR08-006, Parasol Laboratory, Department of Computer Science, Texas A&M University, Nov 2008. [2] Intelligent Motion Planning and Analysis with Probabilistic Roadmap Methods for the Study of Complex and High-Dimensional Motions, Lydia Tapia, Ph.D. Thesis, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, Texas, Dec 2009. [3] Image Sources: https://parasol-www.cse.tamu.edu/groups/amatogroup/foldingserver/ [4] Code Sources: http://code.google.com/p/pdb-tools/ https://sites.google.com/site/crankite/