Fast Classification of Protein Structures by an Alignment-Free Kernel

  • Taku OnoderaEmail author
  • Tetsuo Shibuya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9954)


Alignment is the most fundamental algorithm that has been widely used in numerous research in bioinformatics, but its computation cost becomes too expensive in various modern problems because of the recent explosive data growth. Hence the development of alignment-free algorithms, i.e., alternative algorithms that avoid the computationally expensive alignment, has become one of the recent hot topics in algorithmic bioinformatics.

Analysis of protein structures is a very important problem in bioinformatics. We focus on the problem of predicting functions of proteins from their structures, as the functions of proteins are the keys of everything in the understandings of any organisms and moreover these functions are said to be determined by their structures. But the previous best-known (i.e., the most accurate) method for this problem utilizes alignment-based kernel method, which suffers from the high computation cost of alignments.

For the problem, we propose a new kernel method that does not employ alignments. Instead of alignments, we apply the two-dimensional suffix tree and the contact map graph to reduce kernel-related computation cost dramatically. Experiments show that, compared to the previous best algorithm, our new method runs about 16 times faster in training and about 37 times faster in prediction while preserving comparatively high accuracy.


Feature Vector Kernel Function Structural Alignment Adjacency Matrice Suffix Array 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by JSPS KAKENHI Grant Numbers 25280002 and 24106007. The super-computing resource was provided by Human Genome Center (the Univ. of Tokyo).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Human Genome Center, Institute of Medical Science, The University of TokyoMinato-kuJapan
  2. 2.CREST, JST TokyoJapan

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