ArticlePloS one2021
Robustification of RosettaAntibody and Rosetta SnugDock.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Article
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- Ensemble molecular mimicry correlates with antibody cross-reactivity in proteome-wide studies.Frontiers in immunology · 2026Article
- Antibody Affinity Maturation by Computational Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Deep-learning structure elucidation from single-mutant deep mutational scanning.Nature communications · 2025Article
- NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores.Journal of cheminformatics · 2025Article
- RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data.Journal of structural biology · 2025Article
- Nanobody engineering: computational modelling and design for biomedical and therapeutic applications.FEBS open bio · 2025Review
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- Exploring Bioinformatics Solutions for Improved Leishmaniasis Diagnostic Tools: A Review.Molecules (Basel, Switzerland) · 2024Review
- A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.Protein science : a publication of the Protein Society · 2024Article
- A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases.bioRxiv : the preprint server for biology · 2024Article
- Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop.Research (Washington, D.C.) · 2024Article
- Simultaneous selection of nanobodies for accessible epitopes on immune cells in the tumor microenvironment.Nature communications · 2023Article
- Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.Nature communications · 2023Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- Studying the characteristics of nanobody CDR regions based on sequence analysis in combination with 3D structures.Journal, genetic engineering & biotechnology · 2022Article
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- Protein Structure Prediction with Mass Spectrometry Data.Annual review of physical chemistry · 2022Review
- VInternational journal of molecular sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In recent years, the observed antibody sequence space has grown exponentially due to advances in high-throughput sequencing of immune receptors. The rise in sequences has not been mirrored by a rise in structures, as experimental structure determination techniques have remained low-throughput. Computational modeling, however, has the potential to close the sequence-structure gap. To achieve this goal, computational methods must be robust, fast, easy to use, and accurate. Here we report on the latest advances made in RosettaAntibody and Rosetta SnugDock-methods for antibody structure prediction and antibody-antigen docking. We simplified the user interface, expanded and automated the template database, generalized the kinematics of antibody-antigen docking (which enabled modeling of single-domain antibodies) and incorporated new loop modeling techniques. To evaluate the effects of our updates on modeling accuracy, we developed rigorous tests under a new scientific benchmarking framework within Rosetta. Benchmarking revealed that more structurally similar templates could be identified in the updated database and that SnugDock broadened its applicability without losing accuracy. However, there are further advances to be made, including increasing the accuracy and speed of CDR-H3 loop modeling, before computational approaches can accurately model any antibody.
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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.