Parallel Programming & Cluster Computing High Throughput Computing
High Throughput Computing and Protein Structure
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Transcript of High Throughput Computing and Protein Structure
High Throughput Computing and Protein
Structure
Stephen E. Hamby
Overview• Introduction To Protein Structure• Dihedral Angles• Previous Work• Support Vector Regression• Optimisation• Prediction• Results• Conclusions
Introduction To Protein Structure
Molecules with massive biological importance
Structure determination gives insight into ….
• Function, Dynamics, Potential drug targets.
Experimental structure determination is….
• Expensive, Slow, Difficult
Introduction To Protein Structure
Primary Structure:
Order of Amino Acids
Secondary Structure:
Building blocks
Tertiary Structure:
Complete 3D Structure
Introduction To Protein Structure
Secondary Structure Types
α-helix
β-sheet
Random Coil
Dihedral Angles
Dihedral Angles
Dihedral Angles
Finding the secondary structure of a protein is a step towards finding its complete structure
Predicting dihedral angles can help us to get the secondary structure
How Can We Predict Dihedral Angles?
Previous work
Destruct
Multiple neural networks.
Iterative method.
Predicts secondary structure
and dihedral angles.
Previous work
Twin neural networks give a consensus prediction.
Predicts dihedral angles from various amino acid properties amino acid composition and predicted structure.
Real Spine
Support Vector Regression
Kernel machine learning raises the data to a higher dimension so a linear relationship can be found.
Support Vector Regression
Attempts to fit a linear function to the data in a high dimensional feature space
Accurate but…
Slow, needs optimisation, black box.
Support Vector Regression
Kernel Choice
We tested the various kernels available through the PyML package.
These the are linear, polynomial, and gaussian kernels.
We tested them using the CASP4 dataset.
Gaussian kernel produced the best results.
Optimisation
Three interdependent parameters
Grid based optimisation on a the CASP4 dataset
Around 10000 3 hour jobs.
Run in blocks of 10 on Jupiter
Accuracy assessed using the Pearson correlation coefficient
Prediction
Support vector machine using a Gaussian kernel and optimal parameters.
Training on the CB513 dataset.
Tested by 10 fold cross validation
CASP 4 used as a test set.
Results
Destruct Real Spine SVM Prediction
Pearson Correlation Coefficient
0.42 0.62 0.57
CASP4 Test set gives Pearson Correlation Coefficient of 0.56
Results measured by cross validation
Results
Using Secondary structure predictions made by cascade correlation neural networks:
Dihedrals assisted by predicted structure Pearson correlation coefficient 0.582.
Subsequent iterations should lead to better predictions of both structure and dihedral angles.
What Next?
Using further iterations to improve accuracy.
Current method is a black box.
Can we use a program like Trepan to get some definite rules about secondary structure.
Conclusions
• Dihedral Angles define protein secondary structure
• Using Support Vector Machines it is possible to predict dihedral angles
• We (hopefully!) can use predicted dihedral angles to improve the accuracy of secondary structure prediction.
Acknowledgements
Jonathan Hirst
Hirst group members
BBSRC
The University of Nottingham