Discovery of highly active kynureninases for cancer immunotherapy through protein language model

Abstract

Tailor-made enzymes empower a wide range of versatile applications, although searching for the desirable enzymes often requires high throughput screening and thus poses significant challenges. In this study, we employed homology searches and protein language models to discover and prioritize enzymes by their kinetic parameters. We aimed to discover kynureninases as a potentially versatile therapeutic enzyme, which hydrolyses L-kynurenine, a potent immunosuppressive metabolite, to overcome the immunosuppressive tumor microenvironment in anticancer therapy. Subsequently, we experimentally validated the efficacy of four top-ranked kynureninases under in vitro and in vivo conditions. Our findings revealed a catalytically most active one with a nearly twofold increase in turnover number over the prior best and a 3.4-fold reduction in tumor weight in mouse model comparisons. Consequently, our approach holds promise for the targeted quantitative enzyme discovery and selection suitable for specific applications with higher accuracy, significantly broadening the scope of enzyme utilization. A web-executable version of our workflow is available at seekrank.steineggerlab.com and our code is available as free open-source software at github.com/steineggerlab/SeekRank.