1. Prediction of RNA Binding Protein Using Machine Learning Technique
Avdesh Mishra
Reecha Khanal
Md Tamjidul Hoque
Date: 4/07/2018

2. Overview: Importance of RNA-Binding Proteins (RBPs) Dataset Collection
Features Extraction
Feature Encoding Techniques
Feature Ranking
Machine Learning
Results
Conclusions

3. Why RNA Binding Protein Prediction?
RNA-binding proteins play important roles in many biological functions
mRNA stability
Stress response
Cell cycle
Tumor differentiation
Apoptosis
Gene regulation at post-transcriptional levels

4. non-RBP Protein Chains
Dataset Preparation:
Collection of validation dataset:
PISCES
UniProt Database
Sequence Identity : 25%
68084 RBPs
X-ray resolution: 3Å
Sequence Length: 50 – 10,000 amino acids
14389
non-RBP Protein Chains

5. CD-HIT: 14389 Protein Chains 68084 RBPs
Sequence Identity Cutoff >= 25%
Protein Length: 50 – 10,000 amino acids
7077 NonRBPs
2770 RBPs

6. Final balanced dataset consists of:
Previously established dataset:
2780 RBPs
7077 NRBPs
We prepared a balanced dataset by taking a subset of previously established dataset.
Proteins with non-standard amino acids removed
Redundancy removed
Final balanced dataset consists of:
1700 NonRBPs
1700 RBPs

7. Feature Set for ASA Prediction
Feature Set For RNA BINDING PROTEIN PREDICTION
Hydrophobicity
Polarity
Polarizability
Van Der Waals Volume SA SS
PSSM
The property in which molecules repel water molecues
Separation of electric charge leading to a molecule or a chemical group having electric dipole or multipole moment.
A measure of how easily an electron cloud is distorted by an electric field.
The Volume
occupied by
an individual atom or an molecule.
(Solvent Accessibility)
The measure of surface area accessible to a solvent
(Secondary Structure)
The prediction of structure of an Amino Acid.
(Position Specific Scoring Matrix)
Evolutionary information obtained from sequence alignment computed using PSI-BLAST

8. Sequence and Feature Vector encoding.
Feature Set For RNA BINDING PROTEIN PREDICTION (2518 Features)
Hydrophobicity
Polarity
Polarizability
Van Der Waals Volume SA SS
PSSM
Solvent
Accessibility
(13)
Two different types of amino acids (buried, exposed ) probabilities predicted using ACCPro. Then C-T-D is applied
Secondary Structure
Probabilities
(21)
Three different secondary structure (helix, beta and coil) probabilities predicted using SSPro. Then C-T-D is applied
(1900 PSSM-DDT +
100
PSSM-SDT
400
PSSM-EDT)
2400 Features extracted by PSSM distance transformation
(21)
20 Amino Acids Divided into three different Groups and later C-T-D is applied.

9. C-T-D (Composition, Transition, and Distribution)
Composition: composition of a particular group of amino acid in the sequence
Transition: change of amino acids from one group to other as we go linearly through the sequence
Distribution: how one amino acid group is distributed throughout the protein sequence
C-T-D (Composition, Transition, and Distribution)

10. C-T-D(Composition, Transition, and Distribution)
Property
Group 1
Group 2
Group 3
Hydrophobicity
Polar
R, K, E, D, Q, N
Neutral
G, A, S, T, P, H, Y
Hydrophobic
C, V, L, I, M, F, W
Normalized van der Waals Volume
0 – 0.278
G, A, S, C, T, P, D
2.95 – 4.0
N, V, E, Q, I, L
4.43 – 8.08
M, H, K, F, R, Y, W
Polarity
4.92 – 6.2
L, I, F, W, C, M, V, Y
8.0 – 9.2
P, A, T, S
10.4 – 13.0
H, Q, R, K, N, E, D
Polarizability
0 – 0.108
G, A, S, D, T
0.128 – 0.186
C, P, N, V, E, Q, I, L
0.219 – 0.409
K, M, H, F, R, Y, W

11. Example: A E AAA E A EE AAAAA E A EEE AA EE A EEE AA E
Number of A’s (n1) = 16 | Number of E’s (n2) = 12
Composition for n1 = 16/28
Composition for n2 = 12/28
Transition : (15/29*100)
since there are 15 transitions from A to E or E to A
Distribution: For A: first position of sequence 1st  (1/30*100)
25%  5
50%  12
75%  20
100%  21
Using this approach we obtain 21 dimensional vector.

12. PSSM-DT (position specific scoring matrix- distance transformation)
PSSM-DDT: PSSM-DDT measures the occurrence probabilities of pairs of different amino acids separated by a distance of d in a protein from the PSSM profile.
Distance Between Two pairs of Amino Acids
** i1, i2  the two pairs of different amino acids
* L  Length of protein sequence

13. PSSM-DT (position specific scoring matrix- distance transformation)
PSSM-SDT: Measures the occurrence probabilities of a pair of same amino acids separated by a distance d in a protein from the PSSM profile
Distance Between Two pairs of Amino Acids
** i  individual amino acid
* L  Length of protein sequence

14. PSSM-DT (position specific scoring matrix- distance transformation)
PSSM-EDT: Measures non-co-occurrence probability for two amino acids separated by a certain distance d in a protein from the PSSM profile
Distance Between Two pairs of Amino Acids
** Ax, Ay  the two pairs of different amino acids
* L  Length of protein sequence

15. Final Training set of 1001 Features obtained
Feature Ranking:
Minimum redundancy maximum relevance (mrmr) feature selection technique
Final Training set of 1001 Features obtained

16. Machine Learning Approach:
Training Features for Base-classifiers X = {f1, f2, f3, …, f2518}
KNN
Classifier
GBC
LOGREG
Training Features for Meta-classifiers
X = {PKNN-bind , PKNN-non-bind , PGBC-bind , PGBC-non-bind, PLOGREG-bind , PLOGREG-non-bind, f1, f2, f3, …, f2518}
SVM
Classifier
StackRBPPrediction

17. Results:
Accuracy, Sensitivity, and Specificity was calculated using 10-fold cross validation.
Model
Prediction of RNA-Binding-Protein using Stacking
OVERALL ACCURACY (ACC)
91.24%

18. Comparing different machine learning Approaches:
Accuracy
Logistic Regression (LOGREG)
88.52
Support Vector Machine (SVM)
90.53%
Stacking
91.24%

19. Fig: Comparison with a recently proposed similar predictor (RBPPred):
Model
RBPPred
Our Predictor
Accuracy
67.82%
91.25%
Fig: Comparison with a recently proposed similar predictor (RBPPred):

20. Thank you for your attention.