Advancing NanoLuc Luciferase Stability beyond Directed Evolution and Rational Design through Expert-Guided Deep Learning.

ABSTRACT: Engineered luciferases have transformed biological imaging and sensing, yet optimizing NanoLuc luciferase (NLuc) remains challenging due to the inherent stability-activity trade-off and its limited sequence homology with characterized proteins. We report a hybrid approach that synergistically integrates deep learning with structure-guided rational design to develop enhanced NLuc variants that improve thermostability and thereby activity at elevated temperatures. By systematically analyzing libraries of engineered variants, we established that modifications to termini and loops distal from the catalytic center, combined with preservation of allosterically coupled networks, effectively increase thermal resilience while maintaining enzymatic function. Our optimized variantsnotably B.07 and B

SUBMITTER: Gardiner S 

PROVIDER: S-EPMC12887937 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature