| Title: | Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters |
| Author(s): | Berger BM; Yeung W; Goyal A; Zhou Z; Hildebrandt ER; Kannan N; Schmidt WK; |
| Address: | "Department of Biochemistry and Molecular Biology, University of Georgia, Athens, Georgia, United States of America. Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America. Department of Computer Science, University of Georgia, Athens, Georgia, United States of America" |
| DOI: | 10.1371/journal.pone.0270128 |
| ISSN/ISBN: | 1932-6203 (Electronic) 1932-6203 (Linking) |
| Abstract: | "Protein prenylation by farnesyltransferase (FTase) is often described as the targeting of a cysteine-containing motif (CaaX) that is enriched for aliphatic amino acids at the a1 and a2 positions, while quite flexible at the X position. Prenylation prediction methods often rely on these features despite emerging evidence that FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with the goal of determining the full scope of prenylation potential among the 8000 possible Cxxx sequence combinations. Further, this study aims to subdivide the prenylated sequences as either shunted (i.e., uncleaved) or cleaved (i.e., canonical). Predictions were determined for Saccharomyces cerevisiae FTase and compared to results derived using currently available prenylation prediction methods. In silico predictions were further evaluated using in vivo methods coupled to two yeast reporters, the yeast mating pheromone a-factor and Hsp40 Ydj1p, that represent proteins with canonical and shunted CaaX motifs, respectively. Our machine learning-based approach expands the repertoire of predicted FTase targets and provides a framework for functional classification" |
| Keywords: | *Alkyl and Aryl Transferases/genetics Farnesyltranstransferase/genetics/metabolism Machine Learning Protein Prenylation *Saccharomyces cerevisiae/genetics/metabolism Substrate Specificity; |
| Notes: | "MedlineBerger, Brittany M Yeung, Wayland Goyal, Arnav Zhou, Zhongliang Hildebrandt, Emily R Kannan, Natarajan Schmidt, Walter K eng R01 GM117148/GM/NIGMS NIH HHS/ R01 GM132606/GM/NIGMS NIH HHS/ R35 GM139656/GM/NIGMS NIH HHS/ Research Support, N.I.H., Extramural 2022/06/25 PLoS One. 2022 Jun 24; 17(6):e0270128. doi: 10.1371/journal.pone.0270128. eCollection 2022" |
