TM PRO

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TM PRO Comparison of Algorithms for Protein
Stability Prediction Upon Mutations

• Madhavi Ganapathiraju
• Graduate student
• Carnegie Mellon University

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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

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Part 1 TM pro
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TMPro Evaluations
Segment Segment Segment Segment Segment Segment Residuelevel Residuelevel
Method ? Qok Qok Qok Segment F Score Segment F Score Segment Recall Segment Recall Segment Precision Segment Precision Q2 Q2 Misclassified as Soluble Misclassified as Soluble
MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins) MPtopo (101 TM proteins)
2a TMHMM 66 91 89 94 84 5
2b TMpro NN 60 ? ? 93 ? 92 ? 94 79 ? 0 ?
PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins) PDBTM (191 TM proteins)
3a TMHMM 68 90 90 89 90 84 84 13
3b TMpro NN 57 ? 93 93 ? 93 ? 93 ? 81 ? 2 ?
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TMPro web-server is fully functional!Competiti
on for TMpro LogoPrize See your logo on
the web!
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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

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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

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Profile generation
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

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Doubts
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?

Even TM regions are having gaps such as shown
above
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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

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Using profile for prediction
Studied independent of TMpro Neural network with
21 input, 21 hidden and 1 output neurons
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Another output
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NN architecture needs to be modified
But instead I did post-processing of Neural
network output
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Some more wavelet outputs
Note that these are from the training data
itself.. Yet to check how it performs overall
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Part 2 Stability upon Mutations
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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

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3 Prediction algorithms

• I mutant 2.0
• Support vector machine
• Features amino acid neighbors in 9nm sphere,
  temperature, pH, relative solvent accessibility
  surface are
• http//gpcr2.biocomp.unibo.it/cgi/predictors/I-Mut
  ant2.0/I-Mutant2.0.cgi
• DFIRE
• Knowledge based statistical potentials
• http//phyyz4.med.buffalo.edu/hzhou/mutation.html
• FOLDX
• Statistical mechanics.. Account for various
  energy terms
• http//fold-x.embl-heidelberg.de1100/

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Authors claims in 3 papers
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Our results
Rhodopsin (PDB 1U19)
Bacteriorhodopsin (PDB 1QM8)
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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
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Correlation with known data
Reported correlations for these methods are quite
large (gt0.7) On data compared here the
correlations are quite low
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Notes ..

• Local installation of blast and netblast are on
  cologne
• /usr1/blast-2.2.13/
• /usr1/netblast-2.2.13/
• Java SDK on Cologne
• /usr1/j2sdk1.4.2_11/

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Acknowledgements

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