Title
PhD7Faster: predicting clones propagating faster from the Ph.D.-7 phage display peptide library.
Abstract
Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl.
Year
DOI
Venue
2014
10.1142/S021972001450005X
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
Phage display,target-unrelated peptides,PrTUPs,support vector machine,reduced dipeptide composition
Panning (camera),Matthews correlation coefficient,Biology,Phage display,Support vector machine,Bioinformatics,Phage Display Peptide Library
Journal
Volume
Issue
ISSN
12
1
0219-7200
Citations 
PageRank 
References 
3
0.44
4
Authors
6
Name
Order
Citations
PageRank
Beibei Ru1153.84
Peter A C 't Hoen2273.62
Fulei Nie361.23
Hao Lin415016.08
Feng-Biao Guo5628.49
Jian Huang62608200.50