open access publication

Preprint, 2023

Rapid and accurate deorphanization of ligand-receptor pairs using AlphaFold

In: bioRxiv, Volume 4, 03-29, Page 2023.03.16.531341, 10.1101/2023.03.16.531341

Contributors (11)

Danneskiold-Samsøe, Niels Banhos (0000-0002-2889-5630) (Corresponding author) [1] [2] Kavi, Deniz [2] Jude, Kevin M (0000-0002-3675-5136) [2] Nissen, Silas Boye (0000-0002-9473-4755) [2] Wat, Lianna W (0000-0001-6998-0594) [2] Coassolo, Laetitia (0000-0003-2138-4819) [2] Zhao, Meng (0000-0002-5415-8335) [2] Santana-Oikawa, Galia Asae [2] Broido, Beatrice Blythe [2] Garcia, Kenan Christopher [2] Svensson, Katrin Jennifer (0000-0001-5376-5128) (Corresponding author) [2]


  1. [1] University of Copenhagen
  2. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD]
  3. [2] Stanford University
  4. [NORA names: United States; America, North; OECD]


Secreted proteins are extracellular ligands that play key roles in paracrine and endocrine signaling, classically by binding cell surface receptors. Experimental assays to identify new extracellular ligand-receptor interactions are challenging, which has hampered the rate of novel ligand discovery. Here, using AlphaFold-multimer, we developed and applied an approach for extracellular ligand-binding prediction to a structural library of 1,108 single-pass transmembrane receptors. We demonstrate high discriminatory power and a success rate of close to 90 % for known ligand-receptor pairs where no a priori structural information is required. Importantly, the prediction was performed on de novo ligand-receptor pairs not used for AlphaFold training and validated against experimental structures. These results demonstrate proof-of-concept of a rapid and accurate computational resource to predict high-confidence cell-surface receptors for a diverse set of ligands by structural binding prediction, with potentially wide applicability for the understanding of cell-cell communication.


AlphaFold, applicability, approach, assays, cell surface receptors, cell-cell communication, communication, computational resources, concept, deorphanization, discovery, discriminatory power, diverse set, endocrine, experimental assays, experimental structures, extracellular ligand-receptor interactions, extracellular ligands, high discriminatory power, information, interaction, key role, library, ligand discovery, ligand-binding prediction, ligand-receptor interactions, ligand-receptor pairs, ligands, pairs, paracrine, power, prediction, proof, protein, rate, receptors, resources, results, role, secreted proteins, set, single-pass transmembrane receptors, structural information, structural library, structure, success rate, surface receptors, training, transmembrane receptors, understanding, wide applicability


  • National Institute of Diabetes and Digestive and Kidney Diseases
  • American Heart Association
  • Carlsberg Foundation
  • National Institute of General Medical Sciences