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Structure based function prediction of proteins using fragment library frequency vectors



Akshay Yadav1* & Valadi Krishnamoorthy Jayaraman2*



138/Adwait, Pooja park, Paud road, Kothrud, Pune 411038; 2Centre for Development of Advanced Computing(C-DAC), Pune


Email;; *Corresponding authors


Article Type

Prediction model


Received Sepetember 14, 2012; Accepted September 19, 2012; Published October 01, 2012



The function of the protein is primarily dictated by its structure. Therefore it is far more logical to find the functional clues of the protein in its overall 3-dimensional fold or its global structure. In this paper, we have developed a novel Support Vector Machines (SVM) based prediction model for functional classification and prediction of proteins using features extracted from its global structure based on fragment libraries. Fragment libraries have been previously used for abintio modelling of proteins and protein structure comparisons. The query protein structure is broken down into a collection of short contiguous backbone fragments and this collection is discretized using a library of fragments. The input feature vector is frequency vector that counts the number of each library fragment in the collection of fragments by all-to-all fragment comparisons. SVM models were trained and optimised for obtaining the best 10-fold Cross validation accuracy for classification. As an example, this method was applied for prediction and classification of Cell Adhesion molecules (CAMs). Thirty-four different fragment libraries with sizes ranging from 4 to 400 and fragment lengths ranging from 4 to 12 were used for obtaining the best prediction model. The best 10-fold CV accuracy of 95.25% was obtained for library of 400 fragments of length 10. An accuracy of 87.5% was obtained on an unseen test dataset consisting of 20 CAMs and 20 NonCAMs. This shows that protein structure can be accurately and uniquely described using 400 representative fragments of length 10.



Support vector machines, Fragment libraries, Protein fragments, Cell Adhesion Molecules, Function prediction



Yadav & Jayaraman, Bioinformation 8(19): 953-956 (2012)

Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.