A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes

100 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020, methodology): “RosettaTCR can be combined with docking to generate models of TCR-peptide-MHC complexes 218 or TCRs in complex with non-peptide antigens bound to MHC-like proteins 219 .”
TCRmodel: high resolution modeling of T cell receptors from sequence (2018, methodology): “As we previously developed a TCR-pMHC docking algorithm in Rosetta (18), such a modeling pipeline should be possible to implement.”
Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes (2014, unknown): “We re-trained the ZRANK scoring function using the previously reported set of TCR-pMHC test cases and docking results [11] to determine whether such an optimized function would improve success on the TCR-CD1 and TCR-MR1 cases in Table 1.”
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, background): “41,42 Remarkably, AlphaFold was able to generate models with acceptable or higher accuracy for approximately half (51”
HPEPDOCK: a web server for blind peptide–protein docking based on a hierarchical algorithm (2018, background): “To address these challenges, a variety of state-of-the-art algorithms have been developed for protein–peptide docking in the past few years (5–16).”