Randomized Subspace Learning for Proline Cis-Trans Isomerization Prediction

Omar Y. Al-Jarrah, Paul D. Yoo, Kamal Taha, Sami Muhaidat, Abdallah Shami, Nazar Zaki

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

Original languageEnglish
Article number6951423
Pages (from-to)763-769
Number of pages7
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
Publication statusPublished - Jul 1 2015


  • ensemble methods
  • machine learning
  • proline cis-trans isomerization
  • subspace learning

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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