ArticleProteins2017
ATLAS: A database linking binding affinities with structures for wild-type and mutant TCR-pMHC complexes.
Article in Proteins, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- Learning Protein-Protein Binding Free Energies from Interface Graphs and Physicochemical Descriptors.ACS physical chemistry Au · 2026Article
- Hybrid Approach to Protein-Protein Complex Affinity Prediction Based on Language Models and Molecular Dynamics.International journal of molecular sciences · 2026Article
- Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.Nature communications · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- BindPred: a framework for predicting protein-protein binding affinity from language model embeddings.Bioinformatics (Oxford, England) · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- TCRLens: structure-aware equivariant graph learning for TCR-pMHC-I recognition and immunogenic epitope discovery.Bioinformatics advances · 2026Article
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
- TRAIT: A Comprehensive Database for T-cell Receptor-antigen Interactions.Genomics, proteomics & bioinformatics · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Integration of kinetic data into affinity-based models for improved T cell specificity prediction.Biophysical journal · 2024Article
- PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery.Scientific data · 2024Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.Nature communications · 2024Article
- Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Exploring the potential of structure-based deep learning approaches for T cell receptor design.PLoS computational biology · 2024Article
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.ArXiv · 2024Article
- RACER-m leverages structural features for sparse T cell specificity prediction.Science advances · 2024Article
- Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity?Nature methods · 2024Review
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7 authors.
Funding
Abstract
The ATLAS (Altered TCR Ligand Affinities and Structures) database (https://zlab.umassmed.edu/atlas/web/) is a manually curated repository containing the binding affinities for wild-type and mutant T cell receptors (TCRs) and their antigens, peptides presented by the major histocompatibility complex (pMHC). The database links experimentally measured binding affinities with the corresponding three dimensional (3D) structures for TCR-pMHC complexes. The user can browse and search affinities, structures, and experimental details for TCRs, peptides, and MHCs of interest. We expect this database to facilitate the development of next-generation protein design algorithms targeting TCR-pMHC interactions. ATLAS can be easily parsed using modeling software that builds protein structures for training and testing. As an example, we provide structural models for all mutant TCRs in ATLAS, built using the Rosetta program. Utilizing these structures, we report a correlation of 0.63 between experimentally measured changes in binding energies and our predicted changes. Proteins 2017; 85:908-916. © 2016 Wiley Periodicals, Inc.
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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.