The binding of an antigen peptide to MHC class II molecules is essential for initiating an immune response. Thus, fast and accurate identification of potential binding peptides is critical for basic research and clinical translation. Nowadays, various computational approaches exist for this task, which can roughly be divided into two classes: sequence-based methods employing machine learning (ML), and structure-based methods using physical concepts realized by docking, molecular dynamics, or threading. We present a novel structure-based approach which utilizes statistical scoring functions (SSFs). SSFs have a wide range of applications in protein science, e.g. for the assessment of protein structures or protein stability prediction. Here, SSFs are used to evaluate interactions between peptides and MHC II molecules. Thereby, predictions are performed on sets of MHC II allele-specific 3D models, where potential binding peptide sequences are applied on each of these models and subsequently scored. Finally, a consensus score is computed. Our method is not limited to specific alleles or the availability of an experimentally determined structure. The prediction is fast, and its accuracy is close to the performance shown by ML approaches, while the risk of overfitting on certain training data is reduced.
|Publication status||Published - 22 Jul 2019|
|Event||ISMB/ECCB 2019: 27th Conference on Intelligent Systems for Molecular Biology and 18th European Conference on Computational Biology - Congress Center Basel, Basel, Switzerland|
Duration: 21 Jul 2019 → 25 Jul 2019
|Period||21/07/19 → 25/07/19|
Fields of Science and Technology Classification 2012
- 106 Biology
- 102 Computer Sciences