ArticleAntibodies (Basel, Switzerland)2018
OptMAVEn-2.0: De novo Design of Variable Antibody Regions against Targeted Antigen Epitopes.
Article in Antibodies (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 56 citations in OpenAlex.
- EscaPRRS-ORF5: a structure-aware evolutionary framework for prioritizing immune escape-prone variants in porcine reproductive and respiratory syndrome virus.Bioinformatics (Oxford, England) · 2026Article
- Ab-SELDON: Leveraging Diversity Data for an Efficient Automated Computational Pipeline for Antibody Design.Journal of chemical information and modeling · 2026Article
- Antibody Affinity Maturation by Computational Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- A versatile platform for combinatorial antibody library cloning and NGS-based quality control with high accuracy.Nucleic acids research · 2025Article
- Significantly enhancing human antibody affinity via deep learning and computational biology-guided single-point mutations.Briefings in bioinformatics · 2025Article
- Progress toward new function and design of extracellular G protein-coupled receptor nanobodies.Molecular pharmacology · 2025Review
- Viral escape-inspired framework for structure-guided dual bait protein biosensor design.PLoS computational biology · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Combining computational modeling and experimental library screening to affinity-mature VEEV-neutralizing antibody F5.Protein science : a publication of the Protein Society · 2025Article
- Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies' Recognition of TRBC1 and TRBC2.Antibodies (Basel, Switzerland) · 2023Article
- Improved HIV-1 neutralization breadth and potency of V2-apex antibodies by in silico design.Cell reports · 2023Article
- Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants.Colloids and surfaces. B, Biointerfaces · 2023Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- Exploring rigid-backbone protein docking in biologics discovery: a test using the DARPin scaffold.Frontiers in molecular biosciences · 2023Article
- Inactivation Mechanism and Efficacy of Grape Seed Extract for Human Norovirus Surrogate.Applied and environmental microbiology · 2022Article
- Affinity maturation of TCR-like antibodies using phage display guided by structural modeling.Protein engineering, design & selection : PEDS · 2022Article
- MutDock: A computational docking approach for fixed-backbone protein scaffold design.Frontiers in molecular biosciences · 2022Article
- IsAb: a computational protocol for antibody design.Briefings in bioinformatics · 2021Article
- Challenges in Detection of Serum Oncoprotein: Relevance to Breast Cancer Diagnostics.Breast cancer (Dove Medical Press) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monoclonal antibodies are becoming increasingly important therapeutic agents for the treatment of cancers, infectious diseases, and autoimmune disorders. However, laboratory-based methods of developing therapeutic monoclonal antibodies (e.g., immunized mice, hybridomas, and phage display) are time-consuming and are often unable to target a specific antigen epitope or reach (sub)nanomolar levels of affinity. To this end, we developed Optimal Method for Antibody Variable region Engineering (OptMAVEn) for de novo design of humanized monoclonal antibody variable regions targeting a specific antigen epitope. In this work, we introduce OptMAVEn-2.0, which improves upon OptMAVEn by (1) reducing computational resource requirements without compromising design quality; (2) clustering the designs to better identify high-affinity antibodies; and (3) eliminating intra-antibody steric clashes using an updated set of clashing parts from the Modular Antibody Parts (MAPs) database. Benchmarking on a set of 10 antigens revealed that OptMAVEn-2.0 uses an average of 74% less CPU time and 84% less disk storage relative to OptMAVEn. Testing on 54 additional antigens revealed that computational resource requirements of OptMAVEn-2.0 scale only sub-linearly with respect to antigen size. OptMAVEn-2.0 was used to design and rank variable antibody fragments targeting five epitopes of Zika envelope protein and three of hen egg white lysozyme. Among the top five ranked designs for each epitope, recovery of native residue identities is typically 45-65%. MD simulations of two designs targeting Zika suggest that at least one would bind with high affinity. OptMAVEn-2.0 can be downloaded from our GitHub repository and webpage as (links in Summary and Discussion section).
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.