Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the change of PPB binding affinity upon mutation, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset concerning PPB affinity. Therefore, the current study aimed to introduce and disclose a PPB affinity dataset (PPB-Affinity), which will definitely benefit the development of applicable DL to predict the PPB affinity. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest and publicly available PPB-Affinity dataset, which may finally help the industry in improving the screening efficiency of discovering new large-molecular drugs.

Codes for PPB-Affinity database preparation is disclosed at https://github.com/Huatsing-Lau/PPB-Affinity-DataPrepWorkflow" href="https://github.com/Huatsing-Lau/PPB-Affinity-DataPrepWorkflow">https://github.com/Huatsing-Lau/PPB-Affinity-DataPrepWorkflow.
Codes for the benchmark algorithm is disclosed at https://github.com/ChenPy00/PPB-Affinity.

Files are orginized as follows:

- PPB-Affinity.xlsx

- samples_deleted.zip

- PDB/

- Affinity Benchmark v5.5/

- file1.pdb

- file2.pdb

- ...

- filek.pdb

- ATLAS/

- PDBbind v2020/

- SAbDab/

- SKEMPIv2.0/

Database of affinity data for protein-ligand complexes of the Protein Data Bank (PDB) providing direct and free access to the experimental affinity of a given complex structure. Affinity data are exclusively obtained from the scientific literature. As of Thursday, May 01st, 2014, AffinDB contains 748 affinity values covering 474 different PDB complexes. More than one affinity value may be associated with a single PDB complex, which is most frequently due to multiple references reporting affinity data for the same complex. AffinDB provides access to data in three different forms: # Summary information for PDB entry # Affinity information window # Tabular reports

We developed a database, MPAD (Membrane Protein complex binding Affinity Database), which includes 5436 experimental data from 950 proteins on the binding affinities of membrane protein complexes and their mutants along with sequence, structure, and functional information, membrane specific features, experimental conditions, as well as literature information. The database can be freely accessed at https://web.iitm.ac.in/bioinfo2/mpad.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 29,2025. Database of functional residues in alpha-helical and beta-barrel membrane proteins. Each protein is identified with its name and source alongwith the Uniprot code. The protein data bank (PDB) codes are also given for available proteins. Different methods and experimental parameters, for example, affinity, dissociation constant, IC50, activity etc. are given in the database. Further, the database provides the numerical experimental value for each residue (or mutant) in a protein. The experimental data are collected from the literature both by searching the journals as well as with the keyword search at PUBMED. In addition, complete reference is given with journal citation and PMID number. TNFunction is cross-linked with the sequence database, Uniprot, structural database, PDB, and literature database, PubMed. The WWW interface enables users to search data based on various terms with different display options for outputs.

Comprehensive dataset containing 65 verified Affinity Group locations in United States with complete contact information, ratings, reviews, and location data.

Database containing all antibody structures available in the PDB, annotated and presented in consistent fashion.Each structure is annotated with number of properties including experimental details, antibody nomenclature (e.g. heavy-light pairings), curated affinity data and sequence annotations. You can use the database to inspect individual structures, create and download datasets for analysis, search the database for structures with similar sequences to your query, monitor the known structural repetoire of antibodies.

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.

Affinity Analysis Platform Market Outlook

According to our latest research, the global affinity analysis platform market size reached USD 1.87 billion in 2024, demonstrating robust momentum across sectors. With a projected CAGR of 13.2% during the forecast period, the market is anticipated to attain a value of USD 5.58 billion by 2033. This impressive growth is primarily attributed to increasing demand for advanced data analytics solutions, rising adoption of AI-driven customer insights, and the ongoing digital transformation across industries. As organizations strive to gain a competitive edge through data-driven decision-making, affinity analysis platforms are rapidly becoming indispensable tools for uncovering actionable patterns and optimizing business strategies.

A major growth factor propelling the affinity analysis platform market is the exponential increase in data generation from digital channels, IoT devices, and customer interactions. Organizations across retail, BFSI, healthcare, and e-commerce are leveraging affinity analysis to mine relationships and associations within large datasets, enabling them to understand customer behavior, preferences, and trends with unprecedented accuracy. This demand is further amplified by the proliferation of omnichannel strategies, where businesses seek to create seamless and personalized experiences for their customers. As a result, the need for sophisticated analytics tools capable of real-time processing and actionable insights has never been higher, driving continuous innovation and investment in affinity analysis technologies.

Another significant driver is the integration of artificial intelligence and machine learning algorithms within affinity analysis platforms. These technologies empower organizations to automate complex analytical processes, enhance the accuracy of predictions, and uncover hidden correlations that traditional methods might overlook. The ability to deliver highly targeted marketing campaigns, optimize product recommendations, and detect fraudulent activities in real time has become a key differentiator for businesses. Furthermore, advancements in cloud computing have democratized access to these platforms, allowing even small and medium enterprises to benefit from enterprise-grade analytics without heavy upfront investments in infrastructure.

The increasing regulatory focus on data privacy and security is also shaping the affinity analysis platform market. As data-driven strategies become central to business operations, organizations are under pressure to comply with stringent regulations such as GDPR, CCPA, and HIPAA. This has led to a surge in demand for platforms that offer robust security features, data governance capabilities, and compliance tools. Vendors are responding by enhancing their offerings with advanced encryption, access controls, and audit trails, thereby building trust and ensuring the responsible use of customer data. This regulatory landscape, while challenging, is also fostering innovation and driving adoption among risk-averse industries like healthcare and finance.

From a regional perspective, North America continues to dominate the affinity analysis platform market, accounting for the largest share owing to the early adoption of advanced analytics, presence of key technology providers, and high digital maturity of enterprises. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, booming e-commerce, and increasing investments in AI and big data. Europe remains a significant market, driven by stringent data protection regulations and a strong focus on customer-centric business models. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by expanding digital infrastructure and rising awareness of the benefits of affinity analysis.

Component Analysis

The affinity analysis platform market by component is segmented into software and services, each playing a crucial role in delivering value to end-users. The software segment, which includes analytics engines, visualization tools, and data integration modules, holds the lion’s share of the market. This dominance is attributed to the continuous advancements in analytics algorithms, user-friendly interfaces, and integration capabilities with existing enterprise systems. Organizations are increasingly seeking scalable and customizable software solutions that can handle large vol

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 15, 2013. A repository of sequence specificity models and condition-specific regulatory activities for a large number of DNA- and RNA-binding proteins in Saccharomyces cerevisiae. Accurate and comprehensive information about the nucleotide sequence specificity of trans-acting factors (TFs) is essential for computational and experimental analyses of gene regulatory networks. The sequence specificities in TransfactomeDB, represented as position-specific affinity matrices (PSAMs), are directly estimated from genomewide measurements of TF-binding using our previously published MatrixREDUCE algorithm, which is based on a biophysical model. For each mRNA expression profile in the NCBI Gene Expression Omnibus, we used sequence-based regression analysis to estimate the post-translational regulatory activity of each TF for which a PSAM is available. The trans-factor activity profiles across multiple experiments available in TransfactomeDB allow the user to explore potential regulatory roles of hundreds of TFs in any of thousands of microarray experiments.

A three-dimensional database search has been applied to design a series of endo- and exo-3-(pyridin-3-yl)bicyclo[2.2.1]heptan-2-amines as nicotinic receptor ligands. The synthesized compounds were tested in radioligand binding assay on rat cortex against [3H]-cytisine and [3H]-methyllycaconitine to measure their affinity for α4β2* and α7* nicotinic receptors. The new derivatives showed some preference for the α4β2* over the α7* subtype, with their affinity being dependent on the endo/exo isomerism and on the methylation degree of the basic nitrogen. The endo primary amines displayed the lowest Ki values on both receptor subtypes. Selected compounds (1a, 2a, 3a, and 6a) were tested on heterologously expressed α4β2, α7, and α3β2 receptors and on SHSY-5Y cells. Compounds 1a and 2a showed α4β2 antagonistic properties while behaved as full agonists on recombinant α7 and on SHSY5Y cells. On the α3β2 subtype, only the chloro derivative 2a showed full agonist activity and submicromolar potency (EC50 = 0.43 μM). The primary amines described here represent new chemotypes for the α7 and α3* receptor subtypes.

Despite the mounting evidence that taxonomic diversity dynamics are patterned environmentally and that taxonomic diversity and morphological disparity are decoupled both temporally and spatially in many clades, very little work has been done to assess whether disparity is also influenced by environment. Here I investigate whether trilobite disparity shows environmental patterning through time. I used the method developed by Simpson and Harnik (2009) for estimating latitudinal, substrate, and bathymetric affinities from fossil occurrence data, downloaded from the Paleobiology Database. This method has the advantages that the biological null hypothesis is explicitly separated from the expectation due to sampling, and that the posterior probability can be used to infer degree of preference for one habitat compared to another. To measure morphology, I used a data set of outlines of the trilobite cranidium from Foote (1993). Many of the species in this data set are not represented in the Paleobiology Database in sufficient numbers to assess species-level affinity for these taxa, but species-level affinity could be estimated with high fidelity by using genus-level affinities. Results show that cranidial morphological diversity was structured by environmental preferences of the taxa but the structure was complex and changed through time. In particular, there was little differentiation in morphospace around latitudinal, substrate, or bathymetric affinity during the Cambrian. In contrast, both diversification and expansion into previously unoccupied areas of morphospace during the Ordovician were dominated by tropical, deeper-water taxa.

View Affinity Us Inc import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

Access updated Affinity import data India with HS Code, price, importers list, Indian ports, exporting countries, and verified Affinity buyers in India.

View Affinity E Life import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

Experiments for protein-ligand-complex-based protein-ligand binding affinity prediction for the PDBBind datasets.

Thailand Credit Card: Credit Outstanding: Non Bank: Affinity Card data was reported at 143,921.100 THB mn in May 2018. This records an increase from the previous number of 143,812.420 THB mn for Apr 2018. Thailand Credit Card: Credit Outstanding: Non Bank: Affinity Card data is updated monthly, averaging 82,042.700 THB mn from Jan 2005 (Median) to May 2018, with 161 observations. The data reached an all-time high of 154,735.750 THB mn in Dec 2017 and a record low of 39,240.690 THB mn in Jan 2005. Thailand Credit Card: Credit Outstanding: Non Bank: Affinity Card data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.

This is the Supporting Information for the experimental data paper associated with the CASP16 pharmaceutical protein-ligand pose- and affinity-prediction challenge. The contents are summarized as follows. The paper's DOI will be added to this Zenodo record once it is available.

Roche: Semicolon-delimited files with ligand SMILES strings, PDB identifiers, IC50 data (μM) for chymase and ATX, and ligand pKa data, as well as IC50 for cathepsin G, which is similar to chymase but was not used as a CASP16 target.

Idorsia: Semicolon-delimited files with ligand SMILES strings and PDB identifiers for 3CL/Mpro targets. Table of X-ray data processing statistics (Table S1) and structure refinement statistics (Table S2).

The SI also includes an inventory of the SI files with data definitions.

27 Global export shipment records of Affinity Column with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

View Affinity import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

Coomassie-stained gels used in semi-quantitative analysis of purified calcineurin from calmodulin Sepharose resins.