Starkly Speaking: SLAE: Strictly Local All-atom Environment for Protein Representation
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Hannes Stärk
Jan 25, 2026, 12:32:51 PMJan 25
to stark...@googlegroups.com, Peter Mikhael
Hi together,
Tomorrow we will discuss the following paper with @Peter Mikhael as the host!
Speaker:
Yilin Chen from Stanford University, where he works with Possu Huang.
Paper:
SLAE: Strictly Local All-atom Environment for Protein Representation https://www.biorxiv.org/content/10.1101/2025.10.03.680398v1 (Yilin Chen, Tianyu Lu, Cizhang Zhao, Hannah K. Wayment-Steele, Po-Ssu Huang)
Building physically grounded protein representations is central to computational biology, yet most existing approaches rely on sequence-pretrained language models or backbone-only graphs that overlook side-chain geometry and chemical detail. We present SLAE, a unified all-atom framework for learning protein representations from each residue’s local atomic neighborhood using only atom types and interatomic geometries. To encourage expressive feature extraction, we introduce a novel multi-task autoencoder objective that combines coordinate reconstruction, sequence recovery, and energy regression. SLAE reconstructs all-atom structures with high fidelity from latent residue environments and achieves state-of-the-art performance across diverse downstream tasks via transfer learning. SLAE’s latent space is chemically informative and environmentally sensitive, enabling quantitative assessment of structural qualities and smooth interpolation between conformations at all-atom resolution.
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Yilin Chen from Stanford University, where he works with Possu Huang.
Paper:
SLAE: Strictly Local All-atom Environment for Protein Representation https://www.biorxiv.org/content/10.1101/2025.10.03.680398v1 (Yilin Chen, Tianyu Lu, Cizhang Zhao, Hannah K. Wayment-Steele, Po-Ssu Huang)
Building physically grounded protein representations is central to computational biology, yet most existing approaches rely on sequence-pretrained language models or backbone-only graphs that overlook side-chain geometry and chemical detail. We present SLAE, a unified all-atom framework for learning protein representations from each residue’s local atomic neighborhood using only atom types and interatomic geometries. To encourage expressive feature extraction, we introduce a novel multi-task autoencoder objective that combines coordinate reconstruction, sequence recovery, and energy regression. SLAE reconstructs all-atom structures with high fidelity from latent residue environments and achieves state-of-the-art performance across diverse downstream tasks via transfer learning. SLAE’s latent space is chemically informative and environmentally sensitive, enabling quantitative assessment of structural qualities and smooth interpolation between conformations at all-atom resolution.
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Slack Workspace for discussion and paper voting:
https://join.slack.com/t/logag/shared_invite/zt-2zuxi7gd1-rLUgxg6gnCkhO7WlRsyElg
All information: Schedule of upcoming papers, recordings, mailing list:
https://portal.valencelabs.com/starklyspeaking
Hannes Stärk
Website: https://hannes-stark.com
PhD student at MIT
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