Binding prediction tools are commonly used to identify peptides presented on MHC class II molecules. Recently, a wealth of data in the form of naturally eluted ligands has become available and discrepancies between ligand elution data and binding predictions have been reported. Quantitative metrics for such comparisons are currently lacking. In this study, we assessed how efficiently MHC class II binding predictions can identify naturally eluted peptides, and investigated instances with discrepancies between the two methods in detail. We found that, in general, MHC class II eluted ligands are predicted to bind to their reported restriction element with high affinity. But, for several studies reporting an increased number of ligands that were not predicted to bind, we found that the reported MHC restriction was ambiguous. Additional analyses determined that most of the ligands predicted to not bind, are predicted to bind other co-expressed MHC class II molecules. For selected alleles, we addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQA1*05:01/DQB1*02:01 (DQ2.5) however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively. These findings have important implications for the interpretation of ligand elution data and for the improvement of MHC class II binding predictions.
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http://dx.doi.org/10.1111/imm.13279 | DOI Listing |
J Phys Chem B
January 2025
Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States.
Macrocyclization or stapling is an important strategy for increasing the conformational stability and target-binding affinity of peptides and proteins, especially in therapeutic contexts. Atomistic simulations of such stapled peptides and proteins could help rationalize existing experimental data and provide predictive tools for the design of new stapled peptides and proteins. Standard approaches exist for incorporating nonstandard amino acids and functional groups into the force fields required for MD simulations and have been used in the context of stapling for more than a decade.
View Article and Find Full Text PDFJ Med Chem
January 2025
Hangzhou Carbonsilicon AI Technology Company Limited, Hangzhou 310018, Zhejiang, China.
Applying artificial intelligence techniques to flexibly model the binding between the ligand and protein has attracted extensive interest in recent years, but their applicability remains improved. In this study, we have developed CarsiDock-Flex, a novel two-step flexible docking paradigm that generates binding poses directly from predicted structures. CarsiDock-Flex consists of an equivariant deep learning-based model termed CarsiInduce to refine ESMFold-predicted protein pockets with the induction of specific ligands and our existing CarsiDock algorithm to redock the ligand into the induced binding pockets.
View Article and Find Full Text PDFBioinformatics
January 2025
Institute for Computational Systems Biology, Universität Hamburg, Hamburg, 22761, Germany.
Motivation: Transcription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs.
View Article and Find Full Text PDFElife
January 2025
Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, NIH, Bethesda, United States.
Transcription factor partners can cooperatively bind to DNA composite elements to augment gene transcription. Here, we report a novel protein-DNA binding screening pipeline, termed Spacing Preference Identification of Composite Elements (SPICE), that can systematically predict protein binding partners and DNA motif spacing preferences. Using SPICE, we successfully identified known composite elements, such as AP1-IRF composite elements (AICEs) and STAT5 tetramers, and also uncovered several novel binding partners, including JUN-IKZF1 composite elements.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Cresset, New Cambridge House, Bassingbourn Road, Litlington SG8 0O5, Cambridgeshire, United Kingdom.
Recent increases in the availability of computational power have improved the accessibility of ligand-protein relative binding free energy (RBFE) calculations; however, these calculations remain resource-intensive, which can limit their practical application. RBFE calculations typically use a set of thermodynamic intermediates mediated by the transformation coordinate λ. Optimizing λ offers a way to tune the computational efforts required for a given RBFE calculation.
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