Information-Driven Docking for TCR-pMHC Complex Prediction
Information-Driven Docking for TCR-pMHC Complex Prediction
T cell receptor (TCR) recognition of peptides presented by major histocompatibility complex (MHC) molecules is a fundamental process in the adaptive immune system. An understanding of this recognition process at the molecular level is crucial for TCR based therapeutics and vaccine design. The broad nature of TCR diversity and cross-reactivity presents a challenge for traditional structural resolution. Computational modelling of TCR-pMHC complexes offers an efficient alternative. This study compares the ability of four general-purpose docking platforms (ClusPro, LightDock, ZDOCK and HADDOCK) to make use of varying levels of binding interface information for accurate TCR-pMHC modelling. Each platform was tested on an expanded benchmark set of 44 TCR-pMHC docking cases. In general, HADDOCK is shown to be the best performer. Docking strategy guidance is provided to obtain the best models for each platform for future research. The TCR-pMHC docking cases used in this study can be downloaded from https://github.com/innate2adaptive/ExpandedBenchmark.
- UNIVERSITY COLLEGE LONDON, Bartlett School of Planning United Kingdom
- Division of Infection and Immunity University College London United Kingdom
- Medica University of Vienna Austria
- MEDIZINISCHE UNIVERSITAT GRAZ Austria
- Medical University Vienna (MUV) Austria
complementarity determining region loops, computational modelling, ZDOCK, Immunology, Receptors, Antigen, T-Cell, Computational Biology, HADDOCK, RC581-607, Molecular Docking Simulation, Histocompatibility Antigens, docking, ClusPro, T cell receptor, Immunologic diseases. Allergy, LightDock, Peptides, Algorithms
complementarity determining region loops, computational modelling, ZDOCK, Immunology, Receptors, Antigen, T-Cell, Computational Biology, HADDOCK, RC581-607, Molecular Docking Simulation, Histocompatibility Antigens, docking, ClusPro, T cell receptor, Immunologic diseases. Allergy, LightDock, Peptides, Algorithms
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