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StabilityArc predicts protein stability landscapes with cross-protein transfer

By PulseAugur Editorial · [1 sources] ·

Researchers have developed StabilityArc, a novel method for predicting protein stability landscapes. This approach uses a shared decoder to interpret biochemical constraints across different proteins, enabling better prediction of mutation effects. In rigorous evaluations, StabilityArc demonstrated superior performance compared to existing zero-shot baselines and improved the accuracy of a supervised model, Kermut. AI

IMPACT This research could accelerate experimental prescreening for protein engineering by providing more accurate stability predictions.

RANK_REASON The cluster contains a research paper detailing a new method for protein stability prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

StabilityArc predicts protein stability landscapes with cross-protein transfer

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The cluster contains a research paper detailing a new method for protein stability prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Aaron L. Feller, Andrew D. Ellington, Claus O. Wilke ·

    StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes

    arXiv:2610.00742v1 Announce Type: cross Abstract: Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test whether a shared decoder, trained on measurements from diverse proteins, can inte…

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