SLAE - Strictly Local All-atom Environment for Protein Representation

Learn about SLAE (Strictly Local All-atom Environment), a unified all-atom framework for protein representation learning that captures local atomic neighborhoods using only atom types and interatomic geometries. Discover how this approach overcomes limitations of sequence-pretrained language models and backbone-only graphs by incorporating side-chain geometry and chemical detail. Explore the novel multi-task autoencoder objective that combines coordinate reconstruction, sequence recovery, and energy regression to encourage expressive feature extraction. Understand how SLAE achieves high-fidelity all-atom structure reconstruction from latent residue environments and delivers state-of-the-art performance across diverse downstream tasks through transfer learning. Examine the chemically informative and environmentally sensitive latent space that enables quantitative assessment of structural qualities and smooth interpolation between conformations at all-atom resolution, making it a powerful tool for computational biology and drug discovery applications.