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Peer-reviewedCC BY-NC-NDNature Communications

Global protein-ligand binding affinity profiling via photocatalytic labeling

  • Charles D. Warren1
  • Noah Yardeny1
  • Siyang Peng2
  • Colin S. Burdette1
  • Jacob B. Geri3,4,2,1
4 affiliations
  1. 1Tri-Institutional PhD Program in Chemical Biology (TPCB), New York, NY, USA
  2. 2Department of Pharmacology, Weill Cornell Medicine, Cornell University, New York, NY, USA
  3. 3Department of Pharmacology, Weill Cornell Medicine, Cornell University, New York, NY, USA. [email protected]
  4. 4Tri-Institutional PhD Program in Chemical Biology (TPCB), New York, NY, USA. [email protected]
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Abstract

Protein-ligand binding, selectivity, and affinity dictate the effects of drugs and endogenous molecules in cells. Currently, potential protein-ligand interactions are identified by qualitative interpretation of proteomic, transcriptomic, or genomic data, then binding affinities of hits are measured using purified proteins or engineered reporter systems to validate and quantify the strength of individual interactions. Few methods enable simultaneous target identification and biophysical affinity measurement, and these either apply to specific enzyme classes or proteins with ligand-dependent shifts in stability. Here we describe a general platform, termed Affinity Map, which leverages competitive binding analysis, high fidelity photocatalytic labeling, and high throughput proteomics for global quantitative binding affinity profiling. We show that this method is applicable to major classes of ligands, including small molecules, linear peptides, cyclic peptides, and proteins, and can measure affinities between unmodified ligands and proteins in cell lysates, organ extracts, and live cell surfaces.

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Cite

BibTeXRIS
BibTeX
@article{warren2026global,
  title = {{Global protein-ligand binding affinity profiling via photocatalytic labeling}},
  author = {Charles D. Warren and Noah Yardeny and Siyang Peng and Colin S. Burdette and Jacob B. Geri},
  journal = {Nature Communications},
  year = {2026},
  month = may,
  doi = {10.1038/s41467-026-72238-6},
  url = {https://doi.org/10.1038/s41467-026-72238-6},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Global protein-ligand binding affinity profiling via photocatalytic labeling
AU  - Charles D. Warren
AU  - Noah Yardeny
AU  - Siyang Peng
AU  - Colin S. Burdette
AU  - Jacob B. Geri
PY  - 2026
DA  - 2026/05/21
JO  - Nature Communications
DO  - 10.1038/s41467-026-72238-6
UR  - https://doi.org/10.1038/s41467-026-72238-6
AB  - Protein-ligand binding, selectivity, and affinity dictate the effects of drugs and endogenous molecules in cells. Currently, potential protein-ligand interactions are identified by qualitative interpretation of proteomic, transcriptomic, or genomic data, then binding affinities of hits are measured using purified proteins or engineered reporter systems to validate and quantify the strength of individual interactions. Few methods enable simultaneous target identification and biophysical affinity measurement, and these either apply to specific enzyme classes or proteins with ligand-dependent shifts in stability. Here we describe a general platform, termed Affinity Map, which leverages competitive binding analysis, high fidelity photocatalytic labeling, and high throughput proteomics for global quantitative binding affinity profiling. We show that this method is applicable to major classes of ligands, including small molecules, linear peptides, cyclic peptides, and proteins, and can measure affinities between unmodified ligands and proteins in cell lysates, organ extracts, and live cell surfaces.
SN  - 2041-1723
ER  -