NDLI: A model based on minimotifs for classification of stable protein-protein complexes

Content Provider	IEEE Xplore Digital Library
Author	Rueda, L. Pandit, M.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci., Univ. of Windsor, Windsor, ON, Canada (Rueda, L.; Pandit, M.)
Abstract	Prediction of protein-protein interactions (PPIs) is an important problem in biology, since interactions play key role in most biological processes and functions in living cells. PPIs have been studied from many perspectives. Of these, an important problem is prediction of different complex types such as obligate vs. non obligate and transient vs. permanent, among others. We focus on prediction of obligate protein complexes, which are more stable and perform a specific function, as opposed to transient and non-obligate complexes which last for a short period of time. We have modeled the prediction problem using minimotifs, aka short-linear motifs, to extract information contained in the protein sequences to distinguish between obligate and non-obligate PPIs. Incorporating different classifiers such as the k-nearest neighbor (k-NN), the support vector machine (SVM) and linear dimensionality reduction (LDR) yields a very powerful scheme for prediction. On two well-known datasets, the model delivers classification accuracies as high as 99%. Analysis and cross-dataset validation show that the information contained in the training sequences is crucial for prediction and determination of stability in PPIs.
Starting Page	1
Ending Page	6
File Size	230302
Page Count	6
File Format	PDF
ISBN	9781479945368
DOI	10.1109/CIBCB.2014.6845508
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Accuracy Support vector machines Training Amino acids Predictive models linear dimensionality reduction protein-protein interaction short-linear motifs classification
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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