Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

Hao Zhang, Ole Lund, Morten Nielsen, P Wang, N Papangelopoulos, Y Xu, A Sette, PE Bourne, J Ponomarenko, B Peters

    Research output: Contribution to journalJournal articleResearchpeer-review

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    Abstract

    Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.
    Original languageEnglish
    JournalP L o S One
    Volume5
    Issue number2
    Pages (from-to)e9272
    ISSN1932-6203
    DOIs
    Publication statusPublished - 2010

    Bibliographical note

    This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Cite this

    Zhang, Hao ; Lund, Ole ; Nielsen, Morten ; Wang, P ; Papangelopoulos, N ; Xu, Y ; Sette, A ; Bourne, PE ; Ponomarenko, J ; Peters, B. / Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules. In: P L o S One. 2010 ; Vol. 5, No. 2. pp. e9272.
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    abstract = "Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.",
    author = "Hao Zhang and Ole Lund and Morten Nielsen and P Wang and N Papangelopoulos and Y Xu and A Sette and PE Bourne and J Ponomarenko and B Peters",
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    Zhang, H, Lund, O, Nielsen, M, Wang, P, Papangelopoulos, N, Xu, Y, Sette, A, Bourne, PE, Ponomarenko, J & Peters, B 2010, 'Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules', P L o S One, vol. 5, no. 2, pp. e9272. https://doi.org/10.1371/journal.pone.0009272

    Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules. / Zhang, Hao; Lund, Ole; Nielsen, Morten; Wang, P; Papangelopoulos, N; Xu, Y; Sette, A; Bourne, PE; Ponomarenko, J; Peters, B.

    In: P L o S One, Vol. 5, No. 2, 2010, p. e9272.

    Research output: Contribution to journalJournal articleResearchpeer-review

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    T1 - Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

    AU - Zhang, Hao

    AU - Lund, Ole

    AU - Nielsen, Morten

    AU - Wang, P

    AU - Papangelopoulos, N

    AU - Xu, Y

    AU - Sette, A

    AU - Bourne, PE

    AU - Ponomarenko, J

    AU - Peters, B

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    N2 - Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.

    AB - Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.

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