Comparison of two quantitative matrices for in silico prediction of major histocompatibility complex class I peptide loading

  • Adrian Sabariaga Remigio School of Science, Royal Melbourne Institute of Technology, Australia
  • Ruby Anne King Department of Biochemistry and Molecular Biology, University of the Philippines Manila
  • Paul Gabriel Escalera Lerona Department of Chemical Engineering, University of the Philippines Diliman
  • Dominic Albao National Institute of Molecular Biology and Biotechnology, University of the Philippines Diliman
  • Denise Noelle Bascos National Institute of Molecular Biology and Biotechnology, University of the Philippines Diliman

Abstract

Epitope prediction using in silico methods serves as an efficient and cost-effective method for vaccine design. A mechanism by which vaccines elicit immune responses involves the activation of cytotoxic T lymphocytes (CTL), which requires that peptide epitopes are sufficiently loaded onto major histocompatibility complex (MHC) Class I molecules. A major consideration included physical interactions between the amino acids comprising the peptide epitope and the specific MHC Class I protein. In this paper, the predictive performances of two published quantitative matrices, namely, the re-scaled Miyazawa-Jernigan (MJ) or Betancourt-Thirumalai (BT), and quasi-chemical pair correlation (QC), were evaluated, showing differential allele-specific predictive performance for loading. In addition, since QC matrix has a higher average AUC than BT matrix, we recommend the use of QC matrix as a scoring function for peptide loading classification and regression algorithms.

Published
2020-08-29
How to Cite
[1]
A. Remigio, R. A. King, P. G. Lerona, D. Albao, and D. N. Bascos. Comparison of two quantitative matrices for in silico prediction of major histocompatibility complex class I peptide loading, Proceedings of the Samahang Pisika ng Pilipinas 38, SPP-2020-2A-03 (2020). URL: https://paperview.spp-online.org/proceedings/article/view/SPP-2020-2A-03.
Section
2A Complex Systems and Data Analytics (Short Presentations)