1. Feature Extraction Introduction Features Algorithms Methods
Prediction of Protein Secondary Structure from Sequence using Machine Learning Approach Michael Flot, Avdesh Mishra, Md Tamjidul Hoque {mflot, amishra, Department of Computer Science, University of New Orleans, New Orleans, LA, USA
Feature Extraction
Introduction
Features
Algorithms
Accurate prediction of SS from sequence is still an unsolved problem and is widely researched in bioinformatics
SS has three different major components, helix (H), beta (E) and coil (C)
This is further broken into 8
Most predictors predict H and C accurately , while having low accuracy in E predictions
We propose a machine learning based predictor for three and eight class secondary structure prediction, based on comprehensive feature encoding
PSSM scores
A 20-dimensional Position-Specific Scoring Matrix (PSSM) was generated from PSI-BLAST.
Spider 3
Half sphere exposure (HSE up and down)
Phi and Psi angles
SS Probabilites
Dispredict 2.0 Features
Most features
Monogram and bigram
PSEE
Physiochemical properties
Extra Tree Classifier: The extremely randomized tree or extra-tree classifier (ET) works by constructing randomized decision trees from the original learning sample. The best split is determined randomly from the range of values at each split .
Gradient Boosting Classifier: The gradient boosting classifier (GBC) works by combining weak learners into a single learner in an iterative fashion. We applied 1,000 boosting stages where a regression tree was fit on the negative gradient of the deviance loos function. The learning rate was set to 0.1 and the maximum depth of each regression tree was set to 3.
Logistic Regression: The logistic classifier estimates the probability of interacting versus non-interacting residues based on the confidence or probability distributions produced by multiple independent base-learners.
Random Decision Forest: The random decision forest operates by constructing a multitude of decision trees on various sub-samples of the dataset and outputting the mean prediction of the decision trees. Therefore, the trees of RDF work on the subspaces of the full data. We used bootstrap samples to construct 1,000 tress in the forest
Class id
Amino acid
physiochemical properties
Position specific scoring matrix (PSSM)
Secondary structure
Accessible surface area
Torsion angle (φ, ψ) fluctuation
Monogram
Bigram
PSEE
Disorder Probability
Half Sphere Exposures
phi and psi angles
SS probabilities 
310-helix
alpha-helix
pi-helix
beta-strand
beta-bridge
beta-turn
high curvature loop
irregular
Methods
Dataset creation
PDB website
X-ray resolution b/w 0.0 and 1.5 a
chain length >= 40
sequence identity =30 %
blastclust to cutoff similarity to 25%
Filter out bad data
PSSM
DSSP
Spider 3
Generate 3 class Data set
Widowing
Machine Learning Algorithms
Logistic regression
Svc with Coarse Fine Search
Gradient Boosting Classifier
Extra Tree
Random Decision Forest
Future Work
Results
Many algorithms are still running
Would like to evaluate each one’s accuracy
Determine best window size
Use best methods for experiments with stacking
Algorithm3
Accuracy Window Size 1
Accuracy Window Size 3
Accuracy Window Size 5
Accuracy Window Size 7
Logistic Regression
70.5%
73.91%
Acknowledgements
We gratefully acknowledge the Louisiana Board of Regents through the Board of Regents Support Fund, LEQSF ( )-RD-B-07