Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
arXiv:2605.23960v1 Announce Type: cross Abstract: Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals that underlie function. Here we introduce ProtDiS, a knowledge-guided framework that decomposes pretrained protein micro-environment embeddings into biologically grounded and task-relevant dimensions. Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression, yielding structural features that are more sp
The convergence of advanced deep learning techniques with increasing availability of protein structural data is enabling new methods to decipher complex biological information.
This research provides a more interpretable and targeted approach to understanding protein function, crucial for accelerating drug discovery, bioengineering, and material science.
Protein representation in AI models becomes less 'black box' and more aligned with biophysical principles, allowing for more precise manipulation and prediction of protein behavior.
- · Biotechnology and pharmaceutical companies
- · AI-driven drug discovery platforms
- · Synthetic biology researchers
- · Material science innovators
- · Traditional protein engineering methods
- · Companies reliant on brute-force experimental screening
- · AI models lacking biological interpretability
Improved design and discovery of novel proteins with desired functions, including enzymes and therapeutic antibodies.
Faster development cycles for new drugs, vaccines, and bio-industrial products, potentially lowering costs and increasing accessibility.
The ability to engineer biological systems with unprecedented precision, leading to transformative impacts across medicine, agriculture, and sustainable manufacturing.
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Read at arXiv cs.LG