Accurate Sequence-to-Affinity Models for SH2 Domains Predict Signaling Network Connectivity

Researchers at Columbia University have developed a computational strategy that improves the predictive power of SH2 domain specificity profiling, enabling the accurate identification of signaling network connectivity and the impact of missense variants in phosphoproteins on SH2 binding.

The study, published in bioRxiv, utilized multi-round affinity selection and deep sequencing with large randomized phosphopeptide libraries to produce suitable data for training an additive binding free energy model that covers the full theoretical ligand sequence space. This approach has significant implications for understanding phosphotyrosine-dependent signaling networks and has the potential to inform the development of novel therapeutics.

Key Takeaways:

• Researchers at Columbia University have developed a computational strategy that improves the predictive power of SH2 domain specificity profiling.
• The strategy utilizes multi-round affinity selection and deep sequencing with large randomized phosphopeptide libraries to produce suitable data for training an additive binding free energy model.
• The model can be used to predict signaling network connectivity and the impact of missense variants in phosphoproteins on SH2 binding.
• The study has significant implications for understanding phosphotyrosine-dependent signaling networks and has the potential to inform the development of novel therapeutics.
• The research was carried out by H. Tomas Rube and colleagues, including Dejan Gagoski, Chaitanya Rastogi, Lucas A. N. Melo, Xiaoting Li, Rashmi Voleti, Neel H. Shah, and Harmen J. Bussemaker.
• The study used bioRxiv as the publication platform and highlights the importance of computational modeling in understanding complex biological systems.

Statistics:

• The study utilized multi-round affinity selection and deep sequencing with large randomized phosphopeptide libraries to produce suitable data for training the additive binding free energy model.
• The model covers the full theoretical ligand sequence space, enabling the accurate prediction of signaling network connectivity.
• The study has potential implications for the development of novel therapeutics targeting phosphotyrosine-dependent signaling networks.

Sources:

• "Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression" published in bioRxiv, 2025.
• NewsRx. Researchers at Columbia University Target Life Science (Accurate sequence-to-affinity models for SH2 domains from multi-round peptide binding assays coupled with free-energy regression). Life Science Weekly. November 4, 2025; p 5363.