Length-independent structural similarities enrich the antibody CDR canonical class model

Nowak, Jarosław; Baker, Terry; Georges, Guy; Kelm, Sebastian; Klostermann, Stefan; Shi, Jiye; Sridharan, Sridha; Deane, Charlotte M.

doi:10.1080/19420862.2016.1158370

Cited by 49 publications

(111 citation statements)

References 54 publications

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“…For example, ten-residue H1 loops, eleven-residue H1 loops, and eleven-residue L1 loops are separate BLAST-formatted databases. This ensures a compatible canonical conformation is chosen for each CDR, although recently others have had success using different length loop templates, particularly when somatic hypermutation introduces indels 26 .…”

Section: Introductionmentioning

confidence: 99%

“…PIGS does not include backbone refinement, and as a result it returns structures very rapidly with minimal computational cost. ABodyBuilder includes extensive refinement and has recent developments 49 ; it is different in that it allows CDR templates of mismatched lengths 26 and exploits a full six-dimensional V L -V H determination strategy 7 . RosettaAntibody is unique in that its extensive conformational refinement focused in antibody degrees of freedom is designed and tested toward creating a structure that is at an energy minimum and appropriate for downstream applications including docking or design.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and docking of antibody structures with Rosetta

et al. 2017

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We describe Rosetta-based computational protocols for predicting the three-dimensional structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally-determined structures as well as (1) energetic calculations to minimize loops, (2) docking methodology to refine the VL–VH relative orientation, and (3) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully-automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and docking of antibody structures with Rosetta

et al. 2017

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“…This is important, since it has been shown that loops of different lengths can adopt similar conformations (Nowak et al , 2016), and other methods do not make use of this information. Sphinx is capable of generating high-quality decoy sets that are enriched with near-native conformations and in addition produces more accurate predictions than can be achieved using a straightforward ab initio algorithm.…”

Section: Discussionmentioning

confidence: 99%

Sphinx: merging knowledge-based andab initioapproaches to improve protein loop prediction

et al. 2017

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MotivationLoops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction.ResultsWe show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed.Availability and ImplementationSphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx.Supplementary information Supplementary data are available at Bioinformatics online.

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“…A length-independent canonical form clustering protocol (36) was run on the North-defined (37) CDR loops of a SAbDab (35) snapshot from 26th September 2017. Model loops were inferred to have identical canonical forms to the template used by ABodyBuilder (19).…”

Section: Canonical Formsmentioning

confidence: 99%

The Therapeutic Antibody Profiler (TAP): Five Computational Developability Guidelines

Raybould

Marks

Krawczyk

et al. 2018

Preprint

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Therapeutic monoclonal antibodies (mAbs) must not only bind to their target but must also be free from 'developability issues', such as poor stability or high levels of aggregation. While small molecule drug discovery benefits from Lipinski's rule of five to guide the selection of molecules with appropriate biophysical properties, there is currently no in silico analog for antibody design. Here, we model the variable domain structures of a large set of post-Phase I clinical-stage antibody therapeutics (CSTs), and calculate an array of metrics to estimate their typical properties. In each case, we contextualize the CST distribution against a snapshot of the human antibody gene repertoire. We describe guideline values for five metrics thought to be implicated in poor developability: the total length of the Complementarity-Determining Regions (CDRs), the extent and magnitude of surface hydrophobicity, positive charge and negative charge in the CDRs, and asymmetry in the net heavy and light chain surface charges. The guideline cut-offs for each property were derived from the values seen in CSTs, and a flagging system is proposed to identify nonconforming candidates. On two mAb drug discovery sets, we were able to selectively highlight sequences with developability issues. We make available the Therapeutic Antibody Profiler (TAP), an open-source computational tool that builds downloadable homology models of variable domain sequences, tests them against our five developability guidelines, and reports potential sequence liabilities and canonical forms. TAP is freely available at http://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/TAP.php therapeutic monoclonal antibodies | developability guidelines | immunoglobulin gene sequencing | surface hydrophobicity | surface charge Correspondence: deane@stats.ox.ac.uk Raybould et al. | bioRχiv | June 29, 2018 | 1-7

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Length-independent structural similarities enrich the antibody CDR canonical class model

Cited by 49 publications

References 54 publications

Modeling and docking of antibody structures with Rosetta

Modeling and docking of antibody structures with Rosetta

Sphinx: merging knowledge-based andab initioapproaches to improve protein loop prediction

The Therapeutic Antibody Profiler (TAP): Five Computational Developability Guidelines

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