title: Amino acid composition predicts prion activity.
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.
authors.affiliation: Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
authors.forename: Fayyaz Ul Amir
authors.identifier: 0000-0001-9129-1189
authors.initials: FUA
authors.lastname: Afsar Minhas
authors.affiliation: Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Eric D
authors.initials: ED
authors.lastname: Ross
authors.affiliation: Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
authors.forename: Asa
authors.identifier: 0000-0001-8269-6942
authors.initials: A
authors.lastname: Ben-Hur
mesh_terms: D000595:Amino Acid Sequence; D000682:Amyloid / Q000737:chemistry; D001216:Asparagine / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D005973:Glutamine / Q000737:chemistry*; D006801:Humans; D000069550:Machine Learning; D011328:Prions / Q000737:chemistry* / Q000378:metabolism; D015003:Yeasts
chemical_list: D000682:Amyloid; D011328:Prions; D005973:Glutamine; D001216:Asparagine