Affiliations: Department of Electronic and Information System
Engineering, Faculty of Science and Tech-nology, Hirosaki University, Hirosaki
036-8561, Japan | Science of Bioresources Program, United Graduate
School of Agricultural Sciences (UGAS), Iwate University, 18-8, Ueda 3-chome,
Morioka, Iwate 020-8550, Japan
Note: [] Corresponding author: Toshio Shimizu, Department of Electronic
and Information System Engineering, Faculty of Science and Technology, Hirosaki
University, Hirosaki 036-8561, Japan; Tel./Fax: +81 172 39 3638, E-mail:
[email protected].
Abstract: Reported performance of existing transmembrane (TM) topology
prediction methods were often based on evaluations which neglected the risk of
signal peptides (SP) being predicted as putative TM as well. Here, we evaluated
12 selected TM topology prediction methods (TMpred, TopPred II, DAS, TMAP,
MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0, HMMTOP 2.0, SPLIT 3.5, TM Finder, and
MPEx) for the effect of SP in prediction performance considering three SP
treatments, namely: remain (untreated), removed
first, and removed later. The results showed that the
presence of SP significantly affected the prediction performance of the 12
selected TM topology prediction methods for all three predicted attributes (the
number of transmembrane segments (TMSs), the number of TMSs plus position, and
the N-tail location) and for the predicted topology (combined predictions of
three attributes) by causing a reduction in prediction accuracy. In particular,
lower prediction accuracies were obtained if SP is left untreated (remain)
while significant increases were observed if SP is removed either first or
later. However, between removed first and removed
later SP treatments, the difference was statistically insignificant. In
addition, we found that machine learning-based prediction methods were less
affected by the presence of SP than hydropathy-based methods, but still the
potential risk of degrading the prediction performance is there however to a
lesser degree. Thus, when performing genome-wide analysis, the SP issue should
be addressed during TM topology prediction.
Keywords: signal peptide, transmembrane, evaluation, transmembrane topology prediction,
