De novo design of potent and selective mimics of IL-2 and IL-15

By Daniel‐Adriano Silva, Shawn Yu, Umut Y. Ulge, Jamie B. Spangler, Kevin M. Jude, Carlos Labão-Almeida, Lestat R. Ali, Alfredo Quijano-Rubio, Mikel Ruterbusch, Isabel Leung and 15 more. 2019

474 citations recorded by the source corpus.

We describe a de novo computational approach for designing proteins that recapitulate the binding sites of natural cytokines, but are otherwise unrelated in topology or amino acid sequence. We use this strategy to design mimics of the central immune cytokine interleukin-2 (IL-2) that bind to the IL-2 receptor βγc heterodimer (IL-2Rβγc) but have no binding site for IL-2Rα (also called CD25) or IL-15Rα (also known as CD215). The designs are hyper-stable, bind human and mouse IL-2Rβγc with higher affinity than the natural cytokines, and elicit downstream cell signalling independently of IL-2Rα and IL-15Rα. Crystal structures of the optimized design neoleukin-2/15 (Neo-2/15), both alone and in complex with IL-2Rβγc, are very similar to the designed model. Neo-2/15 has superior therapeutic activity to IL-2 in mouse models of melanoma and colon cancer, with reduced toxicity and undetectable immunogenicity. Our strategy for building hyper-stable de novo mimetics could be applied generally to signalling proteins, enabling the creation of superior therapeutic candidates. A hyper-stable de novo protein mimic of interleukin-2 computationally designed to not interact with a regulatory T-cell specific receptor subunit has improved therapeutic activity in mouse models of melanoma and colon cancer.

Subfields: Immunology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Low-N protein engineering with data-efficient deep learning (2020, background): “Indeed, several previous efforts successfully engineered valuable proteins using high-fidelity assays and low-N design 19,23,24,54–58 .”
Scaffolding protein functional sites using deep learning (2022, methodology): “Previous methods can scaffold functional sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to more complex sites composed of three or more chain segments, and the generated backbones are not guaranteed to be design”
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019, neutral): “22 23-25 Designing large artificial multiprotein assemblies has also been attempted, but remains considerably more challenging.”
How to Hallucinate Functional Proteins (2019, background): “Like directed evolution, this approach has enjoyed wide success and has resulted in useful proteins designed from physical principals (15; 6; 3; 16; 17).”
Recent advances in de novo protein design: Principles, methods, and applications (2021, background): “Likewise, proteins that bind to the interleukin-2 and interleukin-15 receptors were designed by building a helical bundle from interface helices of native interleukin-2 and interleukin-15 (6).”
Cytokine Networks in the Pathophysiology of Inflammatory Bowel Disease. (2019, background): “, 2015), and hyper-stable IL-2 mimetics for therapeutic use have been developed (Silva et al., 2019), but await testing in clinical trials.”
Deep learning methods for designing proteins scaffolding functional sites (2021, unknown): “Methods have been developed for functional site scaffolding for sites made up of one or two contiguous chain segments (6–10), but with the exception of helical bundles (8) these do not extend readily to”
De Novo Protein Design for Novel Folds using Guided Conditional Wasserstein Generative Adversarial Networks (gcWGAN) (2019, methodology): “In particular, the (energy minimum) principle-driven Rosetta tools (Leaver-Fay et al. , 2011) have made great success for de novo protein design (Marcos et al. , 2017; Shen et al. , 2018; Dou et al. , 2018; Silva et al. , 2019).”
Perturbing the energy landscape for improved packing during computational protein design (2020, unknown): “13-16 FastDesign is derived from FastRelax, Rosetta's default protocol for perturbing protein models to sample lower-energy conformations.”