{'fraction_non_alphanumeric': 0.014985014985014986, 'fraction_numerical': 0.000999000999000999, 'mean_word_length': 5.30188679245283, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Novel Predictive Technique for the MHC Class II Peptide-Binding Interaction

Antigenic peptide is presented to a T-cell receptor through the formation of a stable complex with a Major Histocompatibility Complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide’s capacity to form a stable complex with a given MHC Class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. We have developed a novel predictive technique that uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC Class II peptide complex. This is the 1st structure-based technique, as previous methods have been based on binding data. ROC curves are used to quantify the accuracy of the molecular modeling technique. The novel predictive technique is found to be comparable with the best predictive software currently available.