ReviewProtein engineering, design & selection : PEDS2012
Computer-aided antibody design.
Review in Protein engineering, design & selection : PEDS, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 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
111 citing papers in PubMed.
- Advances in linear epitope-based subunit vaccines powered by artificial intelligence: current status and challenges.Frontiers in immunology · 2026Review
- Optimizing the breadth of SARS-CoV-2-neutralizing antibodies in vivo and in silico.Human vaccines & immunotherapeutics · 2025Review
- Improving the solubility of single domain antibodies using VH-like hallmark residues.Protein science : a publication of the Protein Society · 2025Article
- EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization.Journal of pharmaceutical analysis · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances.Molecular biotechnology · 2025Review
- ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.Frontiers in bioinformatics · 2025Article
- AntiBinder: utilizing bidirectional attention and hybrid encoding for precise antibody-antigen interaction prediction.Briefings in bioinformatics · 2024Article
- De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model.Nature communications · 2024Article
- AntiFormer: graph enhanced large language model for binding affinity prediction.Briefings in bioinformatics · 2024Article
- De Novo Synthesis and Structural Elucidation of CDR-H3 Loop Mimics.ACS chemical biology · 2024Article
- AbFlex: designing antibody complementarity determining regions with flexible CDR definition.Bioinformatics (Oxford, England) · 2024Article
- Next generation of multispecific antibody engineering.Antibody therapeutics · 2024Review
- Article
- Targeting Ras-binding domain of ELMO1 by computational nanobody design.Communications biology · 2023Article
- Molecular Dynamics Methods for Antibody Design.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction.Frontiers in molecular biosciences · 2023Article
- Molecular Dynamics Simulation for Investigating Antigen-Antibody Interaction.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Analytical Method for Experimental Validation of Computer-Designed Antibody.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Using Graph-Based Signatures to Guide Rational Antibody Engineering.Methods in molecular biology (Clifton, N.J.) · 2023Article
51 more citing papers are in PubMed but not listed here.
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
Recent clinical trials using antibodies with low toxicity and high efficiency have raised expectations for the development of next-generation protein therapeutics. However, the process of obtaining therapeutic antibodies remains time consuming and empirical. This review summarizes recent progresses in the field of computer-aided antibody development mainly focusing on antibody modeling, which is divided essentially into two parts: (i) modeling the antigen-binding site, also called the complementarity determining regions (CDRs), and (ii) predicting the relative orientations of the variable heavy (V(H)) and light (V(L)) chains. Among the six CDR loops, the greatest challenge is predicting the conformation of CDR-H3, which is the most important in antigen recognition. Further computational methods could be used in drug development based on crystal structures or homology models, including antibody-antigen dockings and energy calculations with approximate potential functions. These methods should guide experimental studies to improve the affinities and physicochemical properties of antibodies. Finally, several successful examples of in silico structure-based antibody designs are reviewed. We also briefly review structure-based antigen or immunogen design, with application to rational vaccine development.
Indexed as
Identifiers
What OpenQuestion holds
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.