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• W4246901651 abstract "Antibodies are proteins generated by the adaptive immune system to recognize and counteract a plethora of pathogens through specific binding. This adaptive binding is mediated by structural diversity in the six complementary determining region (CDR) loops (H1, H2, H3, L1, L2 and L3), which also makes accurate structural modeling of CDRs challenging. Both homology and de novo modeling approaches have been used; to date, the former has achieved greater accuracy for the non-H3 loops. The better performance of homology modeling in non-H3 CDRs is due to the fact that most of the non-H3 CDR loops of the same length and type can be grouped into a few structural clusters. Most antibody-modeling suites utilize homology modeling for the non-H3 CDRs, differing only in the alignment algorithm and how/if they utilize structural clusters. While RosettaAntibody and SAbPred do not explicitly assign query CDR sequences to clusters, two other approaches, PIGS and Kotai Antibody Builder, utilize sequence-based rules to assign CDR sequences to clusters. While the manually curated sequence rules can identify better structural templates, because their curation requires extensive literature search and human effort, they lag behind the deposition of new antibody structures and are infrequently updated. In this study, we propose a machine learning approach (Gradient Boosting Machine [GBM]) to learn the structural clusters of non-H3 CDRs from sequence alone. We argue the GBM method gives simplicity in feature selection and immediate integration of new data compared to manual sequence rules curation. We compare the classification results using the GBM method to that of RosettaAntibody in a 3-repeat 10-fold cross-validation scheme on the cluster-annotated antibody database PyIgClassify and we observe an improvement in the classification accuracy from 78.8±0.2% to 85.1±0.2%. We find the GBM models can reduce the errors in specific cluster membership misclassifications if the involved clusters have relatively abundant data. Based on the factors identified, we suggest methods that can enrich structural classes with sparse data can possibly further improve prediction accuracy in future studies." @default.
• W4246901651 created "2022-05-12" @default.
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• W4246901651 date "2018-06-20" @default.
• W4246901651 modified "2023-09-28" @default.
• W4246901651 title "Non-H3 CDR template selection in antibody modeling through machine learning" @default.
• W4246901651 doi "https://doi.org/10.7287/peerj.preprints.26996" @default.
• W4246901651 hasPublicationYear "2018" @default.
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