Prm based protein folding
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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

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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???


Protein folding articulated robot

Protein Folding & Articulated Robot


Prm based protein folding

Protein Folding & Articulated Robot


Prm based protein folding

Importance of map reduction


Prm based protein folding

Importance of map reduction


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 sampling formula

Node Sampling Formula


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)


Prm based protein folding

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


Node connection formula

Node Connection Formula


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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