arXiv:2605.26192v1 Announce Type: new Abstract: Protein structure generative models excel at predicting single protein static structures from sequence, but routinely fail to capture the correct conformational state of protein complexes, critical for protein design and induced proximity modalities such as antibodies and PROTACs. While structural proteomics techniques like Cross-Linking Mass Spectrometry (XL-MS) and Hydrogen-Deuterium Exchange (HDX-MS) offer valuable spatial and dynamic insights, integrating these sparse, heterogeneous measurements into these models remains an open challenge. He
The rapid advancement of AI in biological modeling converges with the increasing sophistication of structural proteomics techniques, creating an opportune moment for integrated solutions.
Improving protein complex prediction is crucial for drug discovery, synthetic biology, and material science, impacting pharmaceutical development and bio-engineering capabilities.
The ability to generate more accurate models of protein complexes by integrating experimental data will accelerate the design of new therapeutics and functional biomolecules.
- · Pharmaceutical companies
- · Synthetic biology startups
- · AI-driven drug discovery platforms
- · Biotechnology sector
- · Traditional drug discovery methods
- · Companies reliant solely on single-protein models
More effective and targeted protein complexes can be designed for therapeutic and industrial applications.
This improved design capability could lead to faster development of new medicines and biomaterials with novel functions.
The enhanced understanding and control over protein interactions could unlock entirely new approaches to disease treatment and sustainable manufacturing.
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Read at arXiv cs.LG