Published October 10, 2025 | Version v1
Dataset Open

A Structure-Guided Kinase–Transcription Factor Interactome Atlas Reveals Docking Landscapes of the Kinome

  • 1. ROR icon Harvard Medical School
  • 2. ROR icon Dana-Farber Cancer Institute
  • 3. ROR icon Boston Children's Hospital
  • 4. ROR icon Howard Hughes Medical Institute

Description

This repository contains the complete supplementary data tables associated with the manuscript, "A Structure-Guided Kinase–Transcription Factor Interactome Atlas Reveals Docking Landscapes of the Kinome" by Kim et al. 2025. The full preprint is available on bioRxiv (DOI: https://doi.org/10.1101/2025.10.10.681672).

The study presents a structure-guided atlas of the human and Drosophila kinome, built by applying a new interface-aware scoring framework (iLIS) to AlphaFold-Multimer predictions to map kinase-transcription factor interactions. This resource provides residue-level structural insight into partner recognition across the kinome.

This repository contains the following data files, along with a README.txt file that describes the content of each file and its columns:

  • Supplementary Data 1: The complete human Serine/Threonine kinase–transcription factor interactome, including iLIS scores and predicted residue-level interface data.

  • Supplementary Data 2: The complete set of computationally derived Position Weight Matrices (PWMs) for the human S/T kinase screen.

  • Supplementary Data 3: The complete Drosophila kinome–transcription factor interactome, including iLIS scores and predicted residue-level interface data.

  • Supplementary Data 4: The complete set of computationally derived Position Weight Matrices (PWMs) for the Drosophila kinome screen.

  • Supplementary Data 5: A comprehensive catalog of all predicted interaction hotspots for the human and Drosophila kinomes.

  • Supplementary Figure 8: Sequence logos displaying the computed position weight matrices (PWMs) for all high-confidence kinase clusters identified in the Drosophila kinome screen, showing enrichment patterns for all 20 amino acids across the ±10 residue window.

     

Code Availability The Python code for iLIS analysis and benchmarking is publicly available on GitHub: https://github.com/flyark/AFM-LIS

Please refer to the README.txt file for detailed descriptions and the associated manuscript for full methodological details.

Files

Supplementary Figure 8.pdf

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Additional details

Software

Repository URL
https://github.com/flyark/AFM-LIS
Programming language
Python , Jupyter Notebook