1. Madhavi Ganapathiraju Graduate student Carnegie Mellon University
TM PRO & Comparison of Algorithms for “Protein Stability Prediction Upon Mutations”
Madhavi Ganapathiraju
Graduate student
Carnegie Mellon University

2. Overview TMpro evaluations on PDBTM, TMPDB and MPTOPO are complete
Additional inputs to TMPro are being studied
Yule values (not successful)
Evolutionary Profile (promising)
TMPro website has been completed
Evaluation of algorithms to predict protein stability changes upon mutations

3. Part 1: TM pro

4. TMPro Evaluations Segment Residue level Method  Qok F Score Recall
Precision Q2
Misclassified as
Soluble
MPtopo (101 TM proteins) 2a
TMHMM 66 91 89 94 84 5 2b
TMpro NN 60  93  92 79
PDBTM (191 TM proteins) 3a 68 90 13 3b 57 81 2

5. is fully functional! Competition
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is fully functional! Competition
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6. Attempts to overcome confusion with globular soluble helices (1)
Yule value features to be added
Yule value features that discriminate amino acid neighbor propensities between TM and nonTM helices were computed earlier
Tried to add these features as input to NN predictor, but could not achieve quantitative improvement
I will discuss this in future when I have any results to present

7. Attempts to overcome confusion with globular soluble helices (2)
Evolutionary profile information
It is known that knowledge of evolutionary profile of a protein can improve prediction accuracy to a great extent
TMPro is capable of predicting TMs without requiring knowledge of profile
Useful when you cannot extract sequence alignments from known proteins
But where profile is known, we would like to use that additional information

8. Profile generation Get multiple sequence alignments
Those of you who have worked
with evolutionary analysis before, please give feedback
Get multiple sequence alignments
Compute position specific scoring matrix for each protein
21 rows (20 amino acids, and 1 row for gaps)
Profile is generated for each protein in the training and test sets
PSSM (i,j) = log(C(i,j)/total counts at position j)
log(C(i,j)/unigram count of i in the protein)

9. Doubts We have labels for training sequences
What labels to assign to gaps?
We have labels for training sequences
But when original sequence has gaps when aligned, how to interpret the labels of the gaps?
--n------n----n------nnn-----n------n M-----
2a D------E----L------KLS-----R------K H
2A65_A
AAC
YP_ E------S----F------G.K T
-M------M------M------M M M MM
2a A------V------L------W T A AI
2A65_A
AAC
YP_ S------C IL
Even TM regions are having gaps such as shown above

10. What do with missing segment info for some sequences
Doubts
What do with missing segment info for some sequences
When nothing is shown (gap/alignment) for some sequences, I am counting those as gaps
XP_ L K KAP----RSNQV.-..FVAGTMGLASAVGA.AT 86
AAW A..A KNP----NTTRNV-..FMVGALGALGASSV.ST 136
CAB N.RP.-A..VIGSARFAYMAWTRVA 83
XP_ SKRA.-A.FVLSGGRFIYASLLRLL 130
AAA SKRA.-A.FVLTGGRFVYASLVRLL 126

11. Using profile for prediction
Studied independent of TMpro
Neural network with 21 input, 21 hidden and 1 output neurons
Residue Number
(nonmembrane=0, membrane =1)
Predicted output
Experimental
observed locations
of TM helices

12. Another output

13. NN architecture needs to be modified
But instead I did post-processing of Neural network output
Computed Wavelet Transform
Mexican hat wavelet, scale = 10

14. Some more wavelet outputs
Note that these are from the training data itself..
Yet to check how it performs overall

15. Part 2: Stability upon Mutations

16. Evaluation of predictions of protein stability changes upon mutations
Effects of mutations on 2 TM proteins are available in our group
The two proteins are rhodopsin and bacteriorhodopsin
Data available for how much mis-folding occurs
How stability of protein is affected
There are algorithms that can also predict these changes
We compared how accurate or reliable the prediction methods are, by comparing their results with our experimental data

17. 3 Prediction algorithms
I mutant 2.0
Support vector machine
Features: amino acid neighbors in 9nm sphere, temperature, pH, relative solvent accessibility surface are
DFIRE
Knowledge based statistical potentials
FOLDX
Statistical mechanics.. Account for various energy terms

18. Authors’ claims in 3 papers

19. Our results
Rhodopsin (PDB: 1U19)
Bacteriorhodopsin (PDB: 1QM8)

20. Bias in # of mutations that increase/decrease stability
Database bias affects apparent accuracies of algorithms
I-mutant for example, predicts decrease in stability for a majority of the mutations.
Whether the mutations studied through experiments preserve the natural bias of decreasing stability mutations, affects the apparent accuracy of the prediction algorithms

21. Correlation with known data
Reported correlations for these methods are quite large (>0.7)
On data compared here the correlations are quite low

22. Notes .. Local installation of blast and netblast are on cologne:
/usr1/blast /
/usr1/netblast /
Java SDK on Cologne
/usr1/j2sdk1.4.2_11/

23. Acknowledgements Judith Klein-Seetharaman Christopher Jon Jursa
Pitt Information sciences
(for developing web interface)