ArticleProteins2014
Blind prediction performance of RosettaAntibody 3.0: grafting, relaxation, kinematic loop modeling, and full CDR optimization.
Article in Proteins, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
60 citing papers in PubMed.
- Nanobodies: From Discovery to AI-Driven Design.Biology · 2025Review
- AI-based IsAb2.0 for antibody design.Briefings in bioinformatics · 2024Article
- De Novo Synthesis and Structural Elucidation of CDR-H3 Loop Mimics.ACS chemical biology · 2024Article
- Next generation of multispecific antibody engineering.Antibody therapeutics · 2024Review
- Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines.Antibodies (Basel, Switzerland) · 2023Review
- Article
- Structural Classification of CDR-H3 in Single-Domain VMethods in molecular biology (Clifton, N.J.) · 2023Article
- Recent Progress in the Discovery and Development of Monoclonal Antibodies against Viral Infections.Biomedicines · 2022Review
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- Article
- NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.Frontiers in immunology · 2022Article
- Article
- Delicate balance among thermal stability, binding affinity, and conformational space explored by single-domain VScientific reports · 2021Article
- PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.Biophysicist (Rockville, Md.) · 2021Article
- Antibody heavy chain CDR3 length-dependent usage of human IGHJ4 and IGHJ6 germline genes.Antibody therapeutics · 2021Article
- Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.Biochemistry · 2021Review
- Robustification of RosettaAntibody and Rosetta SnugDock.PloS one · 2021Article
- Polyreactive Broadly Neutralizing B cells Are Selected to Provide Defense against Pandemic Threat Influenza Viruses.Immunity · 2020Article
- How repertoire data are changing antibody science.The Journal of biological chemistry · 2020Review
- Geometric potentials from deep learning improve prediction of CDR H3 loop structures.Bioinformatics (Oxford, England) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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 de novo loop methods. In some cases, better templates could have been found by considering residues outside of the CDRs. De novo 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 VL -VH orientations caused models with low H3 RMSDs to score poorly, suggesting that correct VL -VH orientations will improve discrimination between near-native and incorrect conformations. These observations will guide the future development of RosettaAntibody.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.