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

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.

Web accessible database of data extracted from scientific literature, focusing on proteins that are drug-targets or candidate drug-targets and for which structural data are present in Protein Data Bank . Website supports query types including searches by chemical structure, substructure and similarity, protein sequence, ligand and protein names, affinity ranges and molecular weight . Data sets generated by BindingDB queries can be downloaded in form of annotated SDfiles for further analysis, or used as basis for virtual screening of compound database uploaded by user. Data are linked to structural data in PDB via PDB IDs and chemical and sequence searches, and to literature in PubMed via PubMed IDs .

Affinity Internet, Inc. Whois Database, discover comprehensive ownership details, registration dates, and more for Affinity Internet, Inc. with Whois Data Center.

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.

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.

IPLID integrates protein-ligand interaction data from multiple well-known resources, including BindingDB, ChEMBL, DrugBank, GPCRDB, PubChem, LINCS-HMS KinomeScan, and four published kinome assay results. Our database can facilitate projects in machine learning or deep learning-based drug development and other applications by providing integrated data sets appropriate for many research interests. Our database can be utilized for small-scale (e.g. kinases or GPCRs only) and large-scale (e.g. proteome-wide), qualitative or quantitative projects. With its ease of use and straightforward data format, IPLID offers a great educational resource for computer science and data science trainees who lack familiarity with chemistry and biology.

Data statistics

Target (data type) Activities | Unique chemicals | Unique proteins | File name

All (binary) 96318 | 18107 | 3107 | integrated_binary_activity.tsv

All (numerical) 2798365 | 683009 | 5876 | integrated_continuous_activity.tsv

CYP450 (binary) 67552 | 17273 | 47 | integrated_cyp450_binary.tsv

CRT (binary) 4152 | 1219 | 412 | integrated_cancer_related_targets_binary.tsv

CDT (binary) 519 | 349 | 88 | integrated_cardio_targets_binary.tsv

DRT (binary) 4433 | 1325 | 852 | integrated_disease_related_targets_binary.tsv

FDA (binary) 6217 | 1521 | 592 | integrated_fda_approved_targets_binary.tsv

GPCR (binary) 1958 | 545 | 129 | integrated_gpcr_binary.tsv

NR (binary) 1335 | 657 | 264 | integrated_nr_binary.tsv

PDT (binary) 1469 | 674 | 404 | integrated_potential_drug_targets_binary.tsv

TF (binary) 1966 | 998 | 304 | integrated_tf_binary.tsv

*Abbreviations: CYP450 (Cytochrome P450), CRT (Cancer-Related Target), CDT (Cardiovascular Disease candidate Target), DRT (Disease-Related Target), FDA (FDA-approved target), GPCR (G-Protein Coupled Receptor), NR (Nuclear Receptor), PDT (Potential Drug Target), TF (Transcription Factor)

*These protein classifications are from UniProt database and the Human Protein Atlas (https://www.proteinatlas.org/)

IPLID data statistics

A database of binding affinities for the protein-ligand complexes in the Protein Data Bank (PDB). The PDBbind database is a collection of the experimentally measured binding affinities exclusively for the protein-ligand complexes available in the Protein Data Bank (PDB). It thus provides a link between energetic and structural information of those complexes and may be of great value to various molecular recognition studies. This site was last updated in 2007. The updated version of the resource is maintained by the Shanghai Institute of Organic Chemistry (http://www.pdbbind.org.cn).

In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions indicates how successful an antibody is in neutralizing an antigen. Therefore, the strength, measured by “binding affinity”, is a critical aspect of antibody engineering. In theory, the higher the binding affinity, the higher the chances are that the antibody is successful against the target antigen. Currently, techniques such as molecular docking and molecular dynamics are utilized in quantifying the binding affinity. However, owing to the computational complexity of the aforementioned techniques, running simulations for large antibodies/antigens remains a daunting task. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody-antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody-antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized datasets for antibody-antigen binding affinity prediction, consisting of both protein sequences and structures. Subsequently, we propose a deep geometric neural network comprising a structure-based model and a sequence-based model that considers both atomistic and evolutionary details when predicting the binding affinity. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation between the predictions and target values. We release the datasets and code publicly https://drug-discovery-entc.github.io/p2pxml/ to support the development of antibody-antigen binding affinity prediction frameworks for the benefit of science and society.

A dataset to fine-tune language models on protein-ligand binding affinity prediction.

AbRank is a large-scale benchmark and evaluation framework that reframes affinity prediction as a pairwise ranking problem. It aggregates over 380,000 binding assays from nine heterogeneous sources, spanning diverse antibodies, antigens, and experimental conditions, and introduces standardized data splits that systematically increase distribution shift, from local perturbations such as point mutations to broad generalization across novel antigens and antibodies. To ensure robust supervision, AbRank defines a 10-confident ranking framework by filtering out comparisons with marginal affinity differences, focusing training on pairs with at least an 10-fold difference in measured binding strength.

A dataset of ~500 antibodies with binding affinity: antibody sequence, antigen sequence, Kd. Obtained from SAbDab via Therapeutic Data Commons

Python code (get_antibody_affinity_data.py) and dataset (antibody_affinity_protein_sabdab.csv)

A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at https://psnpbind.org. The source code of the tools used in constructing PSnpBind is available on GitHub.

This is the accompanying dataset that was generated by the GitHub project: https://github.com/tonyreina/tdc-tcr-epitope-antibody-binding. In that repository I show how to create a machine learning models for predicting if a T-cell receptor (TCR) and protein epitope will bind to each other.

A model that can predict how well a TCR bindings to an epitope can lead to more effective treatments that use immunotherapy. For example, in anti-cancer therapies it is important for the T-cell receptor to bind to the protein marker in the cancer cell so that the T-cell (actually the T-cell's friends in the immune system) can kill the cancer cell.

HuggingFace provides a "one-stop shop" to train and deploy AI models. In this case, we use Facebook's open-source Evolutionary Scale Model (ESM-2). These embeddings turn the protein sequences into a vector of numbers that the computer can use in a mathematical model.

To load them into Python use the Pandas library:

import pandas as pd

train_data = pd.read_pickle("train_data.pkl") validation_data = pd.read_pickle("validation_data.pkl") test_data = pd.read_pickle("test_data.pkl")

The epitope_aa and the tcr_full columns are the protein (peptide) sequences for the epitope and the T-cell receptor, respectively. The letters correspond to the standard amino acid codes.

The epitope_smi column is the SMILES notation for the chemical structure of the epitope. We won't use this information. Instead, the ESM-1b embedder should be sufficient for the input to our binary classification model.

The tcr column is the CDR3 hyperloop. It's the part of the TCR that actually binds (assuming it binds) to the epitope.

The label column is whether the two proteins bind. 0 = No. 1 = Yes.

The tcr_vector and epitope_vector columns are the bio-embeddings of the TCR and epitope sequences generated by the Facebook ESM-1b model. These two vectors can be used to create a machine learning model that predicts whether the combination will produce a successful protein binding.

From the TDC website:

T-cells are an integral part of the adaptive immune system, whose survival, proliferation, activation and function are all governed by the interaction of their T-cell receptor (TCR) with immunogenic peptides (epitopes). A large repertoire of T-cell receptors with different specificity is needed to provide protection against a wide range of pathogens. This new task aims to predict the binding affinity given a pair of TCR sequence and epitope sequence.

Weber et al.

Dataset Description: The dataset is from Weber et al. who assemble a large and diverse data from the VDJ database and ImmuneCODE project. It uses human TCR-beta chain sequences. Since this dataset is highly imbalanced, the authors exclude epitopes with less than 15 associated TCR sequences and downsample to a limit of 400 TCRs per epitope. The dataset contains amino acid sequences either for the entire TCR or only for the hypervariable CDR3 loop. Epitopes are available as amino acid sequences. Since Weber et al. proposed to represent the peptides as SMILES strings (which reformulates the problem to protein-ligand binding prediction) the SMILES strings of the epitopes are also included. 50% negative samples were generated by shuffling the pairs, i.e. associating TCR sequences with epitopes they have not been shown to bind.

Task Description: Binary classification. Given the epitope (a peptide, either represented as amino acid sequence or as SMILES) and a T-cell receptor (amino acid sequence, either of the full protein complex or only of the hypervariable CDR3 loop), predict whether the epitope binds to the TCR.

Dataset Statistics: 47,182 TCR-Epitope pairs between 192 epitopes and 23,139 TCRs.

References:

Weber, Anna, Jannis Born, and María Rodriguez Martínez. “TITAN: T-cell receptor specificity prediction with bimodal attention networks.” Bioinformatics 37.Supplement_1 (2021): i237-i244.

Bagaev, Dmitry V., et al. “VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium.” Nucleic Acids Research 48.D1 (2020): D1057-D1062.

Dines, Jennifer N., et al. “The immunerace study: A prospective multicohort study of immune response action to covid-19 events with the immunecode™ open access database.” medRxiv (2020).

Dataset License: CC BY 4.0.

Contributed by: Anna Weber and Jannis Born.

The Facebook ESM-2 model has the MIT license and was published in:

• Zeming Lin et al, Evolutionary-scale prediction of atomic-level protein structure with a language model, Science (2023). DOI: 10.1126/science.ade2574 https://www.science.org/doi/10.1126/science.ade2574

HuggingFace has several versions of the trained model.

Checkpoint name Number of layers Number of parameters

esm2_t48_15B_UR50D 48 15B

esm2_t36_3B_UR50D 36 3B

esm2_t33_650M_UR50D 33 650M

esm2_t30_150M_UR50D 30 150M

esm2_t12_35M_UR50D 12 35M

esm2_t6_8M_UR50D 6 8M

A 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.

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.

BindingDB is the first public database of protein-small molecule affinity data.

Database of protein-ligand crystal structures that is a subset of the Protein Data Bank (PDB), containing every high-quality example of ligand-protein binding. The resolved protein crystal structures with clearly identified biologically relevant ligands are annotated with experimentally determined binding data extracted from literature. A viewer is provided to examine the protein-ligand structures. Ligands have additional chemical data, allowing for cheminformatics mining. The binding-affinity data ranges 13 orders of magnitude. The issue of redundancy in the data has also been addressed. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were chosen by tightest binding, best resolution, etc. For the 1780 best complexes that comprise the nonredundant version of Binding MOAD, 475 (27%) have binding data. This collection of protein-ligand complexes will be useful in elucidating the biophysical patterns of molecular recognition and enzymatic regulation. The complexes with binding-affinity data will help in the development of improved scoring functions and structure-based drug discovery techniques.

This database contains information on protein expression in the Drosophila melanogaster brain. It consists of a collection of 3D confocal datasets taken from EYFP expressing protein trap Drosophila lines from the Cambridge Protein Trap project. Currently there are 884 brain scans from 535 protein trap lines in the database. Drosophila protein trap strains were generated by the St Johnston Lab and the Russell Lab at the University of Cambridge, UK. The piggyBac insertion method was used to insert constructs containing splice acceptor and donor sites, StrepII and FLAG affinity purification tags, and an EYFP exon (Venus). Brain images were acquired by Seymour Knowles-Barley, in the Armstrong Lab at the University of Edinburgh. Whole brain mounts were imaged by confocal microscopy, with a background immunohistochemical label added to aid the identification of brain structures. Additional immunohistochemical labeling of the EYFP protein using an anti-GFP antibody was also used in most cases. The trapped protein signal (EYFP / anti-GFP), background signal (NC82 label), and the merged signal can be viewed on the website by using the corresponding channel buttons. In all images the trapped protein / EYFP signal appears green and the background / NC82 channel appears magenta. Original .lsm image files are also available for download.