title: Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence.
abstract: The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak.
authors: Department of Biological Sciences, Birkbeck College, University of London , London , UK.
authors: Dimitris
authors: D
authors: Nikoloudis
authors: Department of Biological Sciences, Birkbeck College, University of London , London , UK.
authors: Jim E
authors: JE
authors: Pitts
authors: Division of Mathematical Biology, National Institute for Medical Research , London , UK.
authors: José W
authors: JW
authors: Saldanha
keywords: Antibody engineering; Blind test; CDR conformation; CDR-H3 sequence rules; Canonical templates; Conformational prediction; DCP; Humanisation; Prediction from sequence