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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.

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Prm based protein folding

PRM based Protein Folding

CS365:Artificial Intelligence

Era Jain (Y9209)

Romil Gadia (Y9496)


Problem statement
Problem Statement

Motivation???






Map reduction
Map Reduction

2 step process:

1)Sampling of nodes

2)Connection of nodes


Node sampling
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 sampling1
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 sampling2
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 sampling3
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 sampling4
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
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



Querying the roadmap
Querying the Roadmap

Protein Folding – Stochastic Process

Dijkstra’s Algorithm v/s Monte-Carlo Simulation


Our progress so far
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
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/


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