IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0025055.html
   My bibliography  Save this article

Predicting Peptide Binding Affinities to MHC Molecules Using a Modified Semi-Empirical Scoring Function

Author

Listed:
  • Webber W P Liao
  • Jonathan W Arthur

Abstract

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.

Suggested Citation

  • Webber W P Liao & Jonathan W Arthur, 2011. "Predicting Peptide Binding Affinities to MHC Molecules Using a Modified Semi-Empirical Scoring Function," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
  • Handle: RePEc:plo:pone00:0025055
    DOI: 10.1371/journal.pone.0025055
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0025055
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0025055&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0025055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Andrew J Bordner, 2010. "Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-12, December.
    2. Mevik, Björn-Helge & Wehrens, Ron, 2007. "The pls Package: Principal Component and Partial Least Squares Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i02).
    3. George F. Gao & José Tormo & Ulrich C. Gerth & Jessica R. Wyer & Andrew J. McMichael & David I. Stuart & John I. Bell & E. Yvonne Jones & Bent K. Jakobsen, 1997. "Crystal structure of the complex between human CD8αα and HLA-A2," Nature, Nature, vol. 387(6633), pages 630-634, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    2. repec:jss:jstsof:23:i12 is not listed on IDEAS
    3. Elton Mammadov & Michael Denk & Frank Riedel & Cezary Kaźmierowski & Karolina Lewinska & Remigiusz Łukowiak & Witold Grzebisz & Amrakh I. Mamedov & Cornelia Glaesser, 2022. "Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains," Land, MDPI, vol. 11(3), pages 1-24, March.
    4. Giacomo Crucil & Fabio Castaldi & Emilien Aldana-Jague & Bas van Wesemael & Andy Macdonald & Kristof Van Oost, 2019. "Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction," Sustainability, MDPI, vol. 11(7), pages 1-18, March.
    5. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    6. Alessandro Barbarino & Efstathia Bura, 2015. "Forecasting with Sufficient Dimension Reductions," Finance and Economics Discussion Series 2015-74, Board of Governors of the Federal Reserve System (U.S.).
    7. Samuel Trachsel & Thanda Dhliwayo & Lorena Gonzalez Perez & Jose Alberto Mendoza Lugo & Mathias Trachsel, 2019. "Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropica," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.
    8. Tomasz Rymarczyk & Krzysztof Król & Edward Kozłowski & Tomasz Wołowiec & Marta Cholewa-Wiktor & Piotr Bednarczuk, 2021. "Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks," Energies, MDPI, vol. 14(23), pages 1-35, December.
    9. Natallia Pashkevich & Darek Haftor & Mikael Karlsson & Soumitra Chowdhury, 2019. "Sustainability through the Digitalization of Industrial Machines: Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    10. Hernandez-Villafuerte, Karla Vanessa, 2011. "Relationship Between Spatial Price Transmission And Geographical Distance In Brazil," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114545, European Association of Agricultural Economists.
    11. Nunes Matthew A & Balding David J, 2010. "On Optimal Selection of Summary Statistics for Approximate Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-16, September.
    12. Wehrens, Ron & Franceschi, Pietro, 2012. "Meta-Statistics for Variable Selection: The R Package BioMark," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i10).
    13. Oladosu, Gbadebo A. & Leiby, Paul N. & Bowman, David C. & Uría-Martínez, Rocio & Johnson, Megan M., 2018. "Impacts of oil price shocks on the United States economy: A meta-analysis of the oil price elasticity of GDP for net oil-importing economies," Energy Policy, Elsevier, vol. 115(C), pages 523-544.
    14. Zhao, Ting & Yang, Zhenshan, 2017. "Towards green growth and management: Relative efficiency and gaps of Chinese cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 481-494.
    15. Miles Grafton & Therese Kaul & Alan Palmer & Peter Bishop & Michael White, 2019. "Technical Note: Regression Analysis of Proximal Hyperspectral Data to Predict Soil pH and Olsen P," Agriculture, MDPI, vol. 9(3), pages 1-18, March.
    16. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    17. Radim VAŠÁT & Radka KODEŠOVÁ & Aleš KLEMENT & Ondřej JAKŠÍK, 2015. "Predicting oxidizable carbon content via visible- and near-infrared diffuse reflectance spectroscopy in soils heavily affected by water erosion," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 10(2), pages 74-77.
    18. Carolina Olid & Valentí Rodellas & Gerard Rocher-Ros & Jordi Garcia-Orellana & Marc Diego-Feliu & Aaron Alorda-Kleinglass & David Bastviken & Jan Karlsson, 2022. "Groundwater discharge as a driver of methane emissions from Arctic lakes," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    19. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
    20. Alamgir Kabir & Md Jahanur Rahman & Abu Ahmed Shamim & Rolf D W Klemm & Alain B Labrique & Mahbubur Rashid & Parul Christian & Keith P West Jr., 2017. "Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-16, December.
    21. Shianghau Wu & Jiannjong Guo, 2018. "PLS and OPLS Discriminatory Analyses on Political Sustainability in Taiwan," Sustainability, MDPI, vol. 10(1), pages 1-13, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0025055. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.