Sentence examples for binding affinities were predicted from inspiring English sources
Peptide binding affinities were predicted using a linear combination of the hydrophobic, entropic, and electrostatic components of the interaction free energy with optimized weights.
Peptide binding affinities were predicted, and the affinities of 4 distinct high binders were determined using flow-based MHC stabilization assays (Table 3a).
The peptide binding affinities were predicted by first predicting whether or not each 9-mer fragment binds; if at least one was predicted to bind then the peptide was classified as a binder, otherwise it was classified as a non-binder.
By using the automated HLA peptide annotation method described above, we observed that similar binding affinities were predicted for HLA class I peptides identified at peptide-level FDR 1% and peptide-level FDR 5%, suggesting that a large fraction of true positives were excluded at peptide-level FDR 1%.
The proposed frameworks were applied to a complex of C3c with compstatin variant E1 and new variants with improved binding affinities are predicted and experimentally validated.
The peptide-HLA binding affinity was predicted with NetMHCpanA or-panB using the annotated HLA molecule, and, when possible, with NetMHC (a previously reported HLA prediction tool available as www.cbs.dtu.dk/services/NetMHC) using the supertype representative.
Nevertheless, a lower binding affinity is predicted for this sequence, possibly due to the assessment of energetic penalty for the solvent exposure of the trimethylene chain formed by Cβ, Cγ and Cδ atoms in the side chain of the P1 Arg.
Supplementary Data 2 lists the 69 different transcription factors whose DNA binding affinity was predicted to be affected by the 251 selected HGMD regulatory SNPs/mutations from the 13 different disease states under study.
