The aim of this site is to collect and to share experimental results on antibodies that would otherwise remain in laboratories, thus aiding researchers in selection and validation of antibodies.

Data pertaining to the publication "Can journal guidelines improve the reporting of antibody validation?". The project investigates the quality of antibody validation information provided in 120 biomedical publications and whether the introduction of journal validation guidelines improved the quality of this information.The data covers 60 publications before introduction of guidelines, and 60 after introduction, half of which from journals with guidelines. The quality of antibody validation information was coded by one author ("Antibody validation information data set.xlsx"), with a sample checked for interrater reliability by another ("Interrater reliability data set.xlsx"). Effects of journal guidelines introduction were tested statistically with a pseudo-experimental design. (Code for the statistical package R is provided.) The data package also includes detailed explanation of how coding was performed ("Coding protocol.docx") and an explanation of these files and data labels ("Data dictionary.docx").

Title: Antibody and Nanobody Design Dataset (ANDD): A Comprehensive Resource with Sequence, Structure, and Binding Affinity Data

DOI: 10.5281/zenodo.16894086

Resource Type: Dataset

Publisher: Zenodo

Publication Year: 2025

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Overview (Abstract):

The Antibody and Nanobody Design Dataset (ANDD) is a unified, large-scale dataset created to overcome the limitations of data fragmentation and incompleteness in antibody and nanobody research. It integrates sequence, structure, antigen information, and binding affinity data from 15 diverse sources, including OAS, PDB, SabDab, and others. ANDD comprises 48,800 antibody/nanobody sequences, structural data for 25,158 entries, antigen sequences for 12,617 entries, and a total of 9,569 binding affinity values for antibody/nanobody-antigen pairs. A key innovation is the augmentation of experimental affinity data with 5,218 high-quality predictions generated by the ANTIPASTI model. This makes ANDD the largest available dataset of its kind, providing a robust foundation for training and validating deep learning models in therapeutic antibody and nanobody design.

Keywords: Dataset, Antibody Design, Nanobody Design, VHH, Deep Learning, Protein Engineering, Binding Affinity, Therapeutic Antibodies, Computational Biology

Methods (Data Curation and Processing):

The ANDD was constructed through a rigorous multi-step process:

1. Data Collection: Data was aggregated from 15 primary sources, including both antibody/nanobody-specific databases (e.g., OAS, SAbDab, INDI, sdAb-DB) and general protein databases (e.g., PDB, UNIPROT, PDBbind).
2. Integration and Standardization: Data from disparate sources was consolidated into a consistent format, addressing challenges of format inconsistency. Entries were manually validated to exclude non-relevant data (e.g., T-cell receptors).
3. Affinity Data Augmentation: The ANTIPASTI deep learning model was used to predict and add binding affinity values for entries that had structural data but lacked experimental affinity measurements.
4. Manual Curation: Web-based data and information from publicly available patents targeting key antigens (HER2, IL-6, CD45, SARS-CoV-2 RBD) were manually extracted to enhance completeness.
5. Hierarchical Organization: Data is organized in a hierarchical structure, offering four progressively detailed levels: Sequence-only, Sequence+Structure, Sequence+Structure+Antigen, and Sequence+Structure+Antigen+Affinity.

Data Specifications and Format:

The dataset is distributed in two parts:

1. ANDD.csv: A comprehensive spreadsheet containing all annotated metadata for each entry.
2. All_structures/Folder: A directory containing the corresponding PDB structure files for entries with structural data.

The ANDD.csvfile includes the following key fields (a full description is available in the Data Record section of the paper):

• General Info: Source, Update_Date, PDB_ID, Experimental_Method, Ab_or_Nano, Source_Organism.
• Chain Details: Entity IDs, Asym IDs, Database Accession Codes, and Macromolecule Names for Heavy (H) and Light (L) chains.
• Antigen Details: Ag_Name, Ag_Seq, Ag_Source Organism, and relevant database identifiers.
• Sequence Data: Full amino acid sequences for H/L chains and individual CDR regions (H1-H3, L1-L3).
• Affinity Data: Experimentally measured or predicted Affinity_Kd(M), ∆Gbinding(kJ), and the Affinity_Method.
• Mutation Data: Annotation of any amino acid mutations (Ab/Nano_mutation).

Technical Validation:

The quality of ANDD has been ensured through extensive validation:

1. Manual Curation: A rigorous manual review process was conducted to check for accuracy and consistency between sequence, structure, and affinity data across randomly selected entries.
2. Affinity Validation with AlphaBind: The experimental Kd values were validated by comparing them against enrichment ratios predicted by the AlphaBind model, showing a significant correlation (Pearson’s r = 0.750).
3. Cross-Mapping Validation: The internal consistency between Kd and ∆Gbinding values within the dataset was confirmed, showing a perfect correlation (Pearson’s r = 1.000) as per thermodynamic principles.
4. Proof-of-Concept Application: The dataset's utility was demonstrated by fine-tuning the Diffab generative model on a subset of ANDD. The fine-tuned model showed significant improvements in generating nanobodies with better predicted binding affinity, structural diversity, and developability metrics.

Potential Uses:

ANDD is designed to accelerate research in computational biology and drug discovery, including:

• Training and benchmarking deep learning models for de novoantibody/nanobody sequence and structure generation.
• Developing and validating predictive models for antibody-antigen binding affinity.
• Studying structure-function relationships in antibody-antigen interactions.
• Facilitating the design of optimized therapeutic antibodies and nanobodies with improved specificity and efficacy.

Access and License:

The ANDD dataset is publicly available for download under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Users are free to share and adapt the material for any purpose, even commercially, provided appropriate credit is given to the original authors and this data descriptor is cited.

Antibody Validation Services Market Outlook

As per our latest research, the global antibody validation services market size reached USD 350 million in 2024, demonstrating robust momentum driven by the increasing demand for reliable antibodies in research and clinical applications. The market is anticipated to expand at a CAGR of 8.7% from 2025 to 2033, with the market size projected to reach approximately USD 748 million by 2033. This growth is primarily attributed to the surge in biomedical research, the proliferation of novel therapeutic targets, and stringent regulatory requirements for antibody validation, ensuring reproducibility and accuracy in scientific studies.

The antibody validation services market is experiencing significant growth due to the rising emphasis on reproducibility in biomedical research. In recent years, irreproducibility of research findings has emerged as a critical concern, leading to wasted resources and delayed scientific progress. As a result, research institutions and pharmaceutical companies are increasingly outsourcing antibody validation to specialized service providers. These providers offer comprehensive validation protocols, including Western blot, immunohistochemistry, and ELISA, to ensure antibody specificity and sensitivity. The growing use of antibodies in diagnostics and therapeutics, especially in oncology and infectious diseases, further fuels the need for robust validation services, enhancing the credibility of research outcomes and accelerating drug development pipelines.

Another key growth factor is the rapid expansion of the biopharmaceutical industry, which relies heavily on high-quality antibodies for drug discovery and development. Pharmaceutical and biotechnology companies are investing substantially in antibody-based therapeutics, necessitating rigorous validation to comply with regulatory standards. The advent of personalized medicine and targeted therapies has intensified the demand for validated antibodies that can accurately detect specific biomarkers. Additionally, the increasing complexity of biological targets, coupled with advancements in antibody engineering, has prompted organizations to seek specialized validation services to address technical challenges and ensure the efficacy and safety of their products.

Technological advancements in antibody validation methods are also propelling market growth. Innovations such as high-throughput screening, multiplex assays, and advanced imaging techniques have improved the efficiency and reliability of validation processes. These technologies enable service providers to deliver faster turnaround times and higher-quality data, catering to the evolving needs of research and clinical laboratories. Furthermore, the integration of artificial intelligence and machine learning in validation workflows is streamlining data analysis and interpretation, reducing human error and enhancing the reproducibility of results. The increasing collaboration between academic institutions, contract research organizations, and industry stakeholders is fostering a dynamic ecosystem that supports the continuous improvement of antibody validation standards.

From a regional perspective, North America dominates the antibody validation services market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading pharmaceutical companies, well-established research infrastructure, and stringent regulatory frameworks contribute to the region's leadership. Europe is also witnessing substantial growth, driven by increased funding for life sciences research and a strong focus on quality assurance. Meanwhile, Asia Pacific is emerging as a lucrative market, with rising investments in biotechnology, expanding healthcare infrastructure, and growing awareness about the importance of antibody validation. The Middle East & Africa and Latin America, although smaller in market size, are expected to register steady growth due to increasing research activities and government initiatives to strengthen healthcare systems.

This dataset contains underlying data from a study that evaluated twenty commercial antibodies agaisnt Huntingtin in western blot, immunoprecipitation and immunofluorescence. The study is accessible on our Zenodo community (DOI: 10.5281/zenodo.11582780) and serves as a research tool to facilitate reproducible and reliable Huntingtin resarch.

Localization by immunofluorescence and confocal microscopy of 72 antibodies targeting 72 genes and validation through siRNA knock down to verify protein localization and antibody binding. 59 of the antibodies are those described in Stadler et al 2012.

Version History August 2017 - additional phenotype to CMPO ontology mappings added

Open-access database of antibodies against human proteins developed through collaboration between Antibodypedia AB and the Nature Publishing Group. It aims to provide the scientific community and antibody distributors alike with information on the effectiveness of specific antibodies in specific applications--to help scientists select the right antibody for the right application. Antibodypedia's mission is to promote the functional understanding of the human proteome and expedite analysis of potential biomarkers discovered through clinical efforts. To this end, they have developed an open-access, curated, searchable database containing annotated and scored affinity reagents to aid users in selecting antibodies tailored to specific biological and biomedical assays. They envisage Antibodypedia as a virtual repository of validated antibodies against all human, and ultimately most model-organism, proteins. Such a tool will be exploitable to identify affinity reagents to document protein expression patterns in normal and pathological states and to purify proteins alone and in complex for structural and functional analyses. They hope to promote characterization of the roles and interplay of proteins and complexes in human health and disease. They encourage commercial providers to submit information regarding their inventory of antibodies with links to quality control data. Independent users can submit their own application-specific experimental data using standard validation criteria (supportive or non-supportive) developed with the assistance of an international advisory board recruited from academic research institutions. Users can also comment on specific antibodies without submitting validation data.

The ability to reproduce scientific findings is foundational in research; yet, it is compromised in part by poorly characterized reagents, including antibodies. In this report, we describe the application of complementary validation strategies tailored for use in immunohistochemical assays in the characterization of rabbit monoclonal antibodies against YAP and TAZ, homologous and sequentially similar transcriptional effectors of the Hippo signaling pathway. A lack of antibody reagents rigorously validated for immunohistochemistry has limited the Hippo signaling research community’s ability to interrogate YAP and TAZ independently in tissue. In a series of normal and diseased human tissues, we were able to demonstrate differential expression patterns of YAP and TAZ, suggesting the potential for functional differences of these proteins. These differences can now be studied in greater detail with these highly validated tools.

Description of study and method This dataset contains measurement values from a multiplexed screen of antibodies (Abs) to determine their selective recognition of G protein-coupled receptors (GPCRs).

Therapeutic antibodies are being developed to modulate GPCR function. However, validating the selectivity of anti-GPCR Abs is challenging due to sequence similarities of individual receptors within GPCR subfamilies. Here we present data from multiplexed immunoassay to test >400 anti-GPCR Abs from the Human Protein Atlas targeting a customized library of 215 expressed and solubilized GPCRs representing all GPCR subfamilies.

The GPCRs were conjugated with 1D4 and FLAG epitope tags (common protein tags for Ab binding), were overexpressed in Expi293F cells and solubilized for the assay. The Abs were tested using the suspension bead array (SBA) technology from Luminex. The technology employs color-coded beads with Abs that bind the target (for capture) and fluorescently labelled Abs that bind the 1D4 epitope tag (for detection). The resulting output data are the median fluorescence intensity (MFI) values for each sample and Ab. MFI represents a relative measurement that allows for comparison within Abs but not between. The dataset also contains Z-scores and robust Z-scores for visualization and to provide a similar scale for all Abs. For Ab validation, Abs targeting GPCRs were used for capture. To verify that GPCRs were expressed at all, Abs targeting the FLAG tag were used for capture.

Each anti-GPCR Ab was evaluated against its target GPCR and phenotypically closely related GPCRs by using a population density-based threshold to map the on-target and off-target binding of the antibody.

The files contain the following data and information

abval_dat.csv: Sample information and Luminex measurements (MFI, Z-scores and robust Z-scores) for samples used in the antibody validation screen (GPCR capture, 1D4 detection). expr_dat.csv: Sample information and Luminex measurements for lysates used to evaluate GPCR expression (FLAG capture, 1D4 detection). prot.csv: Information about antibodies used in the study. ReadMe: Details about the files and description of columns

This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.

This Dataset contains the following underlying data for a study which evaluated eight Rab10 antibodies by western blot, immunoprecipitation and immunofluorescence using a knockdown cell line as an isogenic control. The original study is also available on the Zenodo YCharOS community (https://doi.org/10.5281/zenodo.13684961).

The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).

This excel document presents a cursory landscape analysis of the commercially available monoclonal antibodies, mouse models, and cell lines available for the targets within the ASAP Technical Track callout. Monoclonal antibody landscape analysis was performed through searches of the online catalogs of Abcam, Thermofischer, Millipore Sigma, and Cell Signaling Technologies. Additional searches through CiteAb were performed when a large number of monoclonal antibodies were not available through the aforementioned vendors. Additionally, targets that have antibody validation datasets available through YCharOS (https://ycharos.com/data/) are denoted with "YCHAROS VALIDATED" in red. Mouse model landscape analysis was performed through searchers of the International Mouse Strain Resource (IMSR) at findmice.org. Cell line landscape analysis was performed through searchers of the online catalogs of Abcam, Horizon Discovery, The Jackson Laboratory, and C-BIG repository (https://cbigr-open.loris.ca/ipsc_catalogue/). If MJFF or ASAP have ongoing tool development efforts, the specific tools/models in development are listed in the table as well.

Results replicated in 3–4 animals for each antibody. Antibody host: •, mouse; †, rabbit; ‡, chicken. Antibody dilution: a, 1∶20; b, 1∶50; c, 1∶100; d, 1∶200; e, 1∶500; f, 1∶1000; g, 1∶5000; o, antibody did not work. Antibody company: Ab, Abcam; SC, Santa Cruz Biotechnology; IG, IMGenex; LB, Lifespan Biosciences; GT, Gene Tex; NB, Novus Biologicals; M, Millipore.

Supplementary dataset The folder contains individual antibody staining (~100) on frozen tonsil sections, fixed with acetone, 4% formaldehyde or antigen retrieved (AR) following formalin fixation. Each image is a stack of 3 TIFF unmodified fluorescence images on serial sections. Fixation and antibody name can be found in each image title. The collection is a compressed .zip file.The dataset can be downloaded at the Mendeley repository:Cattoretti, Giorgio; Bolognesi, Maddalena M; Bosisio, Francesca M; Faretta, Mario; Mascadri, Francesco (2020), “Antibodies validated for routinely processed tissue unpredictably stain frozen tissue sections.”, Mendeley Data, V1, doi: 10.17632/j88c2ftpsr.1http://dx.doi.org/10.17632/j88c2ftpsr.1References1. Sutherland BW, Toews J, Kast J. Utility of formaldehyde cross-linking and mass spectrometry in the study of protein-protein interactions. Journal of mass spectrometry : JMS, 43(6), 699-715 (2008).2. Bolognesi MM, Manzoni M, Scalia CR et al. Multiplex Staining by Sequential Immunostaining and Antibody Removal on Routine Tissue Sections. Journal of Histochemistry & Cytochemistry, 65(8), 431-444 (2017).3. Scalia CR, Boi G, Bolognesi MM et al. Antigen Masking During Fixation and Embedding, Dissected. Journal of Histochemistry & Cytochemistry, 65(1), 5-20 (2017).

ROIs of size 600 x 900 px (0.109 mm^2) size were chosen from 25 expert-specified tumor subregions within DLBCL biopsy specimens. Images were generated by Hamamatsu Nanozoomer 2.0 RS slide scanner. ROI no. 1 - 25 represent staining with CD14 antibody (imaged at 488 nm wavelength), ROI no. 26 -- 50 represent staining with CD163 antibody (imaged at 555 nm wavelength). Imaged regions in ROIs no. 1 and 26, 2 and 27, ... , coincide. Data were used for mutual comparison of automated macrophage recognition approaches and validation against manual counts.

This project contains the following underlying data included in a study which characterized antibodies for Charged multivesicular body protein 2b. The study is available on Zenodo (https://doi.org/10.5281/zenodo.6370501).

This project contains the following underlying data included in a study aiming at characterizing antibodies for Tyrosine-protein kinase SYK. The study is available on Zenodo (https://doi.org/10.5281/zenodo.6566940).

There is evidence to suggest that the yaws bacterium (Treponema pallidum ssp. pertenue) may exist in non-human primate populations residing in regions where yaws is endemic in humans. Especially in light of the fact that the World Health Organizaiton (WHO) recently launched its second yaws eradication campaign, there is a considerable need for reliable tools to identify treponemal infection in our closest relatives, African monkeys and great apes. It was hypothesized that commercially available serological tests detect simian anti-T. pallidum antibody in serum samples of baboons, with comparable sensitivity and specificity to their results on human sera. Test performances of five different treponemal tests (TTs) and two non-treponemal tests (NTTs) were evaluated using serum samples of 57 naturally T. pallidum-infected olive baboons (Papio anubis) from Lake Manyara National Park in Tanzania. The T. pallidum particle agglutination assay (TP-PA) was used as a gold standard for comparison. In addition, the overall infection status of the animals was used to further validate test performances. For most accurate results, only samples that originated from baboons of known infection status, as verified in a previous study by clinical inspection, PCR and immunohistochemistry, were included. All tests, TTs and NTTs, used in this study were able to reliably detect antibodies against T. pallidum in serum samples of infected baboons. The sensitivity of TTs ranged from 97.7-100%, while specificity was between 88.0-100.0%. The two NTTs detected anti-lipoidal antibodies in serum samples of infected baboons with a sensitivity of 83.3% whereas specificity was 100%. For screening purposes, the TT Espline TP provided the highest sensitivity and specificity and at the same time provided the most suitable format for use in the field. The enzyme immune assay Mastblot TP (IgG), however, could be considered as a confirmatory test.

Mass spectrometry is a rational orthogonal method for antibody-based assays, but implementation of MS in the validation pipeline of antibody manufacturers is hampered by the high cost and low throughput. Here we present a rapid method for antibody validation based on denaturing gel electrophoresis of biotinylated cell lysates (PAGE) followed by mass spectrometry (MS) and antibody array analysis (MAP). The first step, PAGE, produces 12 fractions containing proteins of increasing molecular weight. The fractions are analyzed in parallel by MS and MAP. Antibodies to be tested are immobilized on color coded polymer beads to create antibody arrays, which can comprise up to several thousand various antibodies. MS data provide definite protein identifications in each fraction, creating a reference for antibody reactivity patterns obtained via MAP. The method employs automated software to compare both datasets and provide validation data for each antibody tested. Due to the high-throughput nature of the assay we were able to screen several thousands of antibodies against six different cell lines. The differences in protein expression between the cell lines provide an additional control of antibody specificity. Using PAGE-MAP it is possible to screen and validate thousands of antibodies in a matter of weeks. Moreover, antibodies are tested under standardized conditions, which allows for direct comparison of their performance.

Spaceflight is known to affect immune cell populations. In particular, splenic B-cell numbers decrease during spaceflight and in ground-based physiological models. Although antibody isotype changes have been assessed during and after spaceflight, an extensive characterization of the impact of spaceflight on antibody composition has not been conducted in mice. Next Generation Sequencing and bioinformatic tools are now available to assess antibody repertoires. We can now identify immunoglobulin gene- segment usage, junctional regions, and modifications that contribute to specificity and diversity. Due to limitations on the International Space Station, alternate sample collection and storage methods must be employed. Our group compared Illumina MiSeq sequencing data from multiple sample preparation methods in normal C57Bl/6J mice to validate that sample preparation and storage would not bias the outcome of antibody repertoire characterization. In this report, we also compared sequencing techniques and a bioinformatic workflow on the data output when we assessed the IgH and Igκ variable gene usage. Our bioinformatic workflow has been optimized for Illumina HiSeq and MiSeq datasets, and is designed specifically to reduce bias, capture the most information from Ig sequences, and produce a data set that provides other data mining options.

Kidney transplant characteristics of the development and validation databases.