Protein-Protein interactions discovered by the existing high-throughput techniques contain very high amount of false positives. Here we present an SVM based approach to generate a model that is built on sequence and non-sequence based information of the interacting proteins. This model is used to assess the reliability of given protein-protein interactions. It was run on the interaction data of a pathogenic bacterium; Treponema pallidum (causes Syphilis in humans) obtained from Yeast two hybrid experiments. Various kernels were used for building the model and of all, Sigmoid kernel performed well when used with all the features combined with area under the receiver operating curve (ROC) as 0.53.

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SVM based approach
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to assess the reliability of
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protein-protein interactions
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Meher Preethi Boorgula, Ronak Shah,
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Neerja Katiyar
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2. Motivation:
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Protein interactions play a key role in many
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cellular processes.
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Distortion of protein interfaces may lead to
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development of many diseases.
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Reliable Protein-protein interactions (PPIs)
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conserved among different species and that are
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involved in diseases would be very helpful for
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researchers.

3. Problem Statement:
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Protein-Protein Interactions (PPIs) are very
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helpful in functional annotation of proteins. It
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is important that the PPI data is reliable.
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Thus, we try to predict the reliability of PPIs
with respect to a disease causing bacterium.
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4. Objective:
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To create a prediction model based on Kernel
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method (SVM) to assess the reliability of PPIs
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in Treponema pallidum obtained from Yeast
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Two Hybrid (Y2H) system.
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To classify the interactions as reliable and not
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reliable.
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5. Introduction:
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Protein-protein interactions can be identified
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with the help of high-throughput techniques like
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the Yeast-two Hybrid (Y2H) and Mass
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Spectrometry (MS).
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The main disadvantage with these existing
techniques is the amount of false-positives in the
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data obtained.
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So, assessing the reliability of PPIs is necessary.

6. Methodology:
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Preparation of data sets
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Extract the attributes
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Create & test model using SVM light
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Evaluate the performance of the model
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Analyze the reliability of PPI data sets

7. Datasets:
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Raw data of interactions was obtained from
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Y2H experiments performed at J.Craig Venter
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Institute.
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This data was then organized into train and
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test sets by considering equal number of
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positive and negative examples.
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Positive – High Confidence data
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Negative – Low Confidence data

8. Dataset (Contd…)
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All Interactions = 2993
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High Confidence = 721
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Common Interactions = 66
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Total (excluding common) = 3648
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Train & Test datasets were made by taking
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1824 interactions.
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9. Extracting Attributes:
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Attributes chosen include:
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- Sequence based:
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i. occurrence of 5-mers in the sequence data
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ii. Hydrophobicity
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- Non-sequence based:
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i. Jaccard coefficient
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ii. GO Annotation

10. Hydrophobicity:
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Protein interaction depends on the nature of the
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active/binding site.
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Hydrophobicity profile was used in order to extract
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this feature.
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Average Hydropathy was calculated for a sequence
based on the hydrophobicity of each amino acid
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residue.
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This was obtained using the tool “ProteinGRAVY”.

11. Jaccard coefficient:
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In a PPI network, the neighbors of interacting
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proteins also tend to interact.
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Jaccard coefficient:
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|N(v) U N(u)| / |N(v) ∩ N(u)|
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where u, v are the interacting proteins
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N(X) = set of neighbors of protein X in the PPI
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network
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12. GO Annotations:
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Proteins that are present in the same cellular
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component or that participate in same biological
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processes are more likely to interact.
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This was captured with the help of extracting
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identical GO IDs for the interacting proteins.
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Interacting proteins with atleast one common GO
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ID was considered reliable.
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13. Occurrence of 5-mers
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Spectrum kernel models a sequence in the
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space of all k-mers (5-mers).
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All possible 5-mers in the protein sequences
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were obtained for the data.
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Number of times each 5-mer appears in the
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sequence data for both bait and prey proteins
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was extracted.
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14. Creating & Testing Model:
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SVM Light was used to create a classification
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model based on linear & sigmoid kernel.
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Test data was applied to the model in order to
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classify it.
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The performance of the model was evaluated
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based on Accuracy, Precision and Recall
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values.

15. Experiments Performed:
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1) Model generated using the attribute
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Hydrophobicity.
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2) Model generated using the attribute JC
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3) Model generated using both of these
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attributes.
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4) Model generated using both these attributes
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on a different data set (equal number of
positive and negative examples).

16. Results for Linear Kernel:
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Exp-1 Exp-2 Exp-3 Exp-4
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Accuracy 79.99 79.99 79.88 51.23
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(%)
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Precision - - - -
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(%)
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Recall 0.0 0.0 0.0 0.0
(%)

17. Results for Sigmoid Kernel:
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Exp-1 Exp-2 Exp-3 Exp-4
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Accuracy - - 79.88 57.26
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(%)
Precision - - 0.0 57.80
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(%)
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Recall - - 0.0 45.79
(%)

18. Observation:
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Results obtained were not reliable as the
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model was built using only two attributes.
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This would not be efficient in discriminating
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the positive & negative examples.
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Also, it was observed that there was no
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significance of the positive examples while
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creating the model.
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19. To Be done:
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Extracting the attribute “occurrence of 5-
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mers” for the protein pairs and perform all the
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experiments.
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Obtain data from INTACT database to
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increase the number of positive examples and
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to overcome the number of false positives in
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the data since it is from Y2H experiments.
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Compare the performance with the existing
model based on “Logistic Regression”.

20. Problems:
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The major problem for extracting attributes
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which were dependent on the annotation was
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that Treponema is not fully annotated.
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The interaction data for Treponema is also not
reliable.
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21. Future Work:
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We would like to apply this model to
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Streptococcus Pneumoniae.
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Using PSSM scores by performing PSI-Blast
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would be helpful.
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Analyze for the biological relevance of our
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predictions and then test experimentally the
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interactions predicted to be reliable by the
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model.

22. References:
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Dr.Peter Uetz et al (J.Craig Venter Institute)
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Kernel methods for predicting protein–protein
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interactions by Asa Ben-Hur & William Stafford
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Noble
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SVM Light: http://svmlight.joachims.org/
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Protein GRAVY:
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http://www.bioinformatics.org/sms2/protein_gravy.html
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PIR: http://pir.georgetown.edu/