Matches in SemOpenAlex for { <https://semopenalex.org/work/W4240213134> ?p ?o ?g. }
Showing items 1 to 61 of
61
with 100 items per page.
- W4240213134 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.
- W4240213134 created "2022-05-12" @default.
- W4240213134 creator A5009383312 @default.
- W4240213134 creator A5067826490 @default.
- W4240213134 creator A5072654634 @default.
- W4240213134 date "2018-06-20" @default.
- W4240213134 modified "2023-09-23" @default.
- W4240213134 title "Non-H3 CDR template selection in antibody modeling through machine learning" @default.
- W4240213134 doi "https://doi.org/10.7287/peerj.preprints.26996v1" @default.
- W4240213134 hasPublicationYear "2018" @default.
- W4240213134 type Work @default.
- W4240213134 citedByCount "1" @default.
- W4240213134 countsByYear W42402131342020 @default.
- W4240213134 crossrefType "posted-content" @default.
- W4240213134 hasAuthorship W4240213134A5009383312 @default.
- W4240213134 hasAuthorship W4240213134A5067826490 @default.
- W4240213134 hasAuthorship W4240213134A5072654634 @default.
- W4240213134 hasBestOaLocation W42402131341 @default.
- W4240213134 hasConcept C104317684 @default.
- W4240213134 hasConcept C154945302 @default.
- W4240213134 hasConcept C167625842 @default.
- W4240213134 hasConcept C169627665 @default.
- W4240213134 hasConcept C181199279 @default.
- W4240213134 hasConcept C199360897 @default.
- W4240213134 hasConcept C41008148 @default.
- W4240213134 hasConcept C45484198 @default.
- W4240213134 hasConcept C54355233 @default.
- W4240213134 hasConcept C55493867 @default.
- W4240213134 hasConcept C70721500 @default.
- W4240213134 hasConcept C82714645 @default.
- W4240213134 hasConcept C86803240 @default.
- W4240213134 hasConceptScore W4240213134C104317684 @default.
- W4240213134 hasConceptScore W4240213134C154945302 @default.
- W4240213134 hasConceptScore W4240213134C167625842 @default.
- W4240213134 hasConceptScore W4240213134C169627665 @default.
- W4240213134 hasConceptScore W4240213134C181199279 @default.
- W4240213134 hasConceptScore W4240213134C199360897 @default.
- W4240213134 hasConceptScore W4240213134C41008148 @default.
- W4240213134 hasConceptScore W4240213134C45484198 @default.
- W4240213134 hasConceptScore W4240213134C54355233 @default.
- W4240213134 hasConceptScore W4240213134C55493867 @default.
- W4240213134 hasConceptScore W4240213134C70721500 @default.
- W4240213134 hasConceptScore W4240213134C82714645 @default.
- W4240213134 hasConceptScore W4240213134C86803240 @default.
- W4240213134 hasLocation W42402131341 @default.
- W4240213134 hasLocation W42402131342 @default.
- W4240213134 hasOpenAccess W4240213134 @default.
- W4240213134 hasPrimaryLocation W42402131341 @default.
- W4240213134 hasRelatedWork W10553687 @default.
- W4240213134 hasRelatedWork W13840495 @default.
- W4240213134 hasRelatedWork W1437423 @default.
- W4240213134 hasRelatedWork W15876127 @default.
- W4240213134 hasRelatedWork W2136133 @default.
- W4240213134 hasRelatedWork W5521957 @default.
- W4240213134 hasRelatedWork W5651025 @default.
- W4240213134 hasRelatedWork W5708970 @default.
- W4240213134 hasRelatedWork W9684605 @default.
- W4240213134 hasRelatedWork W17328580 @default.
- W4240213134 isParatext "false" @default.
- W4240213134 isRetracted "false" @default.
- W4240213134 workType "article" @default.