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Abstract

Motivation: The binding of endogenous antigenic peptides to MHC class I
molecules is an important step during the immunologic response of a host
against a pathogen. Thus, various sequence- and structure-based prediction
methods have been proposed for this purpose. The sequence-based methods are
computationally efficient, but are hampered by the need of sufficient
experimental data and do not provide a structural interpretation of their
results. The structural methods are data-independent, but are quite
time-consuming and thus not suited for screening of whole genomes. Here, we
present a new method, which performs sequence-based prediction by incorporating
information obtained from molecular modeling. This allows us to perform large
databases screening and to provide structural information of the results.
Results: We developed a SVM-trained, quantitative matrix-based method for the
prediction of MHC class I binding peptides, in which the features of the
scoring matrix are energy terms retrieved from molecular dynamics simulations.
At the same time we used the equilibrated structures obtained from the same
simulations in a simple and efficient docking procedure. Our method consists of
two steps: First, we predict potential binders from sequence data alone and
second, we construct protein-peptide complexes for the predicted binders. So
far, we tested our approach on the HLA-A0201 allele. We constructed two
prediction models, using local, position-dependent (DynaPredPOS) and global,
position-independent (DynaPred) features. The former model outperformed the two
sequence-based methods used in our evaluation; the latter shows a much higher
generalizability towards other alleles than the position-dependent models. The
constructed peptide structures can be refined within seconds to structures with
an average backbone RMSD of 1.53 Å from the corresponding experimental
structures.