Accelerating the Original Profile Kernel.

TitleAccelerating the Original Profile Kernel.
Publication TypeJournal Article
Year of Publication2013
AuthorsHamp, T, Goldberg, T, Rost, B
JournalPLoS One
Date Published2013

One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at Bugs and other issues may be reported at

Alternate JournalPLoS ONE
PubMed ID23825697
PubMed Central IDPMC3688983