Amino acid composition predicts prion activity

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Abstract
Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, or, as a recent computational analysis suggested, dependent on the presence of short sequence elements with high amyloid-forming potential. The argument for the importance of short sequence elements hinged on the relatively-high accuracy obtained using a method that utilizes a collection of length-six sequence elements with known amyloid-forming potential. We weigh in on this question and demonstrate that when those sequence elements are permuted, even higher accuracy is obtained; we also propose a novel multiple-instance machine learning method that uses sequence composition alone, and achieves better accuracy than all existing prion prediction approaches. While we expect there to be elements of primary sequence that affect the process, our experiments suggest that sequence composition alone is sufficient for predicting protein sequences that are likely to form prions. A web-server for the proposed method is available at http://faculty.pieas.edu.pk/fayyaz/prank.html, and the code for reproducing our experiments is available at http://doi.org/10.5281/zenodo.167136. The determinants of prion formation in proteins that are rich in glutamine and asparagine are still under debate: is the process driven by primary sequence or by amino acid composition? In 2015 Sabate et al. published a paper suggesting that the process is triggered by short amyloid-prone sequences. Their argument was based on the success of their pWALTZ classifier, which uses a database of short peptides with known amyloid forming propensities. To explore the validity of their argument we compared their original scoring matrices with shuffled scoring matrices, and found no decrease in accuracy, suggesting that the success of pWALTZ is the result of the ability of the scoring matrices to capture amino acid composition. Furthermore, we propose a novel machine learning approach with accuracy that is superior to all published prion prediction methods that are currently available, and uses sequence composition alone.
Funding Information
  • National Institutes of Health (US) (GM105991)

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