Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization. Issue 8 (31st March 2014)
- Record Type:
- Journal Article
- Title:
- Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization. Issue 8 (31st March 2014)
- Main Title:
- Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization
- Authors:
- Weitzner, Brian D.
Kuroda, Daisuke
Marze, Nicholas
Xu, Jianqing
Gray, Jeffrey J. - Abstract:
- <abstract abstract-type="main"> <title>ABSTRACT</title> <p>Antibody Modeling Assessment II (AMA‐II) provided an opportunity to benchmark RosettaAntibody on a set of 11 unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non‐H3 CDR loops predicted to under an Ångström. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the 11 targets, two of which were predicted with sub‐Ångström accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better <italic>de novo</italic> loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. <italic>De novo</italic> CDR H3 modeling remains challenging at long loop lengths, but constraining the C‐terminal end of H3 to a kinked conformation allows near‐native conformations to be sampled more frequently. We also found that incorrect V<sub>L</sub>–V<sub>H</sub> orientations caused models with low H3 RMSDs to score poorly, suggesting that correct V<sub>L</sub>–V<sub>H</sub> orientations will improve discrimination between near‐native and incorrect conformations. These observations will guide the future development of RosettaAntibody. Proteins 2014; 82:1611–1623. © 2014 Wiley<abstract abstract-type="main"> <title>ABSTRACT</title> <p>Antibody Modeling Assessment II (AMA‐II) provided an opportunity to benchmark RosettaAntibody on a set of 11 unpublished antibody structures. RosettaAntibody produced accurate, physically realistic models, with all framework regions and 42 of the 55 non‐H3 CDR loops predicted to under an Ångström. The performance is notable when modeling H3 on a homology framework, where RosettaAntibody produced the best model among all participants for four of the 11 targets, two of which were predicted with sub‐Ångström accuracy. To improve RosettaAntibody, we pursued the causes of model errors. The most common limitation was template unavailability, underscoring the need for more antibody structures and/or better <italic>de novo</italic> loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. <italic>De novo</italic> CDR H3 modeling remains challenging at long loop lengths, but constraining the C‐terminal end of H3 to a kinked conformation allows near‐native conformations to be sampled more frequently. We also found that incorrect V<sub>L</sub>–V<sub>H</sub> orientations caused models with low H3 RMSDs to score poorly, suggesting that correct V<sub>L</sub>–V<sub>H</sub> orientations will improve discrimination between near‐native and incorrect conformations. These observations will guide the future development of RosettaAntibody. Proteins 2014; 82:1611–1623. © 2014 Wiley Periodicals, Inc.</p> </abstract> … (more)
- Is Part Of:
- Proteins. Volume 82:Issue 8(2014)
- Journal:
- Proteins
- Issue:
- Volume 82:Issue 8(2014)
- Issue Display:
- Volume 82, Issue 8 (2014)
- Year:
- 2014
- Volume:
- 82
- Issue:
- 8
- Issue Sort Value:
- 2014-0082-0008-0000
- Page Start:
- 1611
- Page End:
- 1623
- Publication Date:
- 2014-03-31
- Subjects:
- Proteins -- Periodicals
Proteins -- Periodicals
572.6 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
- DOI:
- 10.1002/prot.24534 ↗
- Languages:
- English
- ISSNs:
- 0887-3585
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6936.164000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3967.xml