Biology subjects

Exposit, M.

Publications and source records attributed to Exposit, M..

8 recordsLinked to original sources

bioRxiv2026

Designed Minibinders Rewire Receptor Signaling to Enable Functional Human Myogenic Reprogramming

Sarcopenia, loss of muscle mass is a considerable health burden that demands immediate societal attention. Direct myogenic somatic cell reprogramming, a potential muscle regeneration method is constrained by an inability to control the signaling logic that governs cell fate. Here, we show that this barrier can be overcome using AI-designed receptor modulators. Screening de novo minibinders, we identify a synthetic protein cocktail, C6-DPC, that drives efficient human fibroblast-to-muscle transdifferentiation with robust structural and metabolic maturation. C6-DPC reprograms extracellular signaling by activating pro-myogenic FGFR1/2c pathways while suppressing anti-myogenic inputs through ALK1 and TGFBR2; targeted depletion of ALK1 is sufficient to lower the reprogramming barrier. Inflammatory signaling via gp130 emerges as a dominant checkpoint, and its inhibition further enhances conversion. Engineered tissues generate high twitch and tetanic forces in both wild-type and dystrophin-deficient human cells. These findings demonstrate that programmable synthetic ligands can rewrite receptor-level signaling to direct cell fate and enable functional tissue regeneration.

biochemistry↗

bioRxiv2025

Geometric Tuning of Cytokine Receptor Association Modulates Synthetic Agonist Signaling

Cytokines signal by bringing receptor subunits together, but the role of receptor geometry in shaping signaling remains unclear because natural ligands enforce fixed assemblies. Here, we present a de novo protein design platform that rigidly scaffolds receptor-binding domains into defined spatial arrangements. Applying this across IL-7, type I and III interferons, IL-10, gp130, {beta} common, and synthetic receptor pairs, we show that by varying geometry, we can bias pSTAT pathway usage and tune functional outcomes. Geometric control allowed us to decouple pSTAT1 from pSTAT5 in IL-7, separate antigen presentation (MHC-I) from checkpoint induction (PD-L1) in type I interferons, and suppress pro-inflammatory IFN{gamma} secretion while retaining anti-inflammatory activity in IL-10. We further created minimal IL-6 and IL-3 agonists and strengthened synthetic receptor pairings inaccessible with present cytokines. These results establish receptor geometry as a central determinant of cytokine activity and provide a platform for programmable immune modulation.

synthetic biology↗

bioRxiv2025

Direct cell reprogramming by a designed agonist inducing HER2-FGFR proximity

Growth factor induced receptor dimerization and activation of downstream pathways can modulate cell fate decisions. Here, we investigate the potential of de novo designed synthetic ligands, termed Novokines, to reprogram cell identity by inducing proximity of novel pairs of receptor subunits. We find that a design, H2F, that brings together HER2 (which has no known natural ligand) and the FGF receptor has potent signaling activity. H2F induces robust signaling and reprograms fibroblasts into myogenic cells. Unlike native FGF ligands, H2F selectively activates the MAPK pathway without engaging PLC{gamma}-mediated Ca{superscript 2} signaling. FRET assays confirm H2F-mediated HER2-FGFR proximity, and phosphoproteomic analysis reveals activation of MAPK effectors. H2F-induced ERK phosphorylation is abolished in cells expressing a kinase-dead FGFR1 (K514M) mutant, confirming the requirement for FGFR catalytic activity. H2F treatment significantly increases myofiber formation from adult patient-derived primary myoblasts, demonstrating its capacity to promote myogenic regeneration. Our findings demonstrate that synthetic receptor pairings can rewire signaling outputs to drive regeneration, providing a programmable platform for cell fate engineering.

biochemistry↗

bioRxiv2025

High-Throughput De Novo Protein Design Yields Novel Immunomodulatory Agonists

Cytokines regulate cell behavior by bringing together specific receptor subunits to trigger downstream signaling. Designed molecules that bring together non-natural receptor pairs could have novel signaling responses and cell specificities. We present a high-throughput de-novo design approach to create novel cytokines by generating and fusing pairs of computationally designed binders. By combining 33 designed receptor-binding domains, we generated over a thousand potential de novo designed "Novokines", of which 75 activated pSTAT signaling in peripheral blood mononuclear cells. We characterized 18 of these, including new pairings of established common receptors, cross-family pairings such as TrkA-{gamma}common, and a series of pairings with interferon receptor-1 (IFNAR1), revealing that IFNAR1 can function as a versatile common receptor similar to {gamma}common or {beta}common. We identify novokines that drive monocyte proliferation, T cell survival and CD4+ T cell-specific proliferation. Our framework provides a blueprint for expanding the understanding of cytokine signaling and generating novel therapeutic proteins.

synthetic biology↗

bioRxiv2025

Design of overlapping genes using deep generative models of protein sequences

In nature, viruses frequently evolve overlapping genes (OLG) in alternate reading frames of the same nucleotide sequence despite the drastically reduced protein sequence space resulting from the sharing of codon nucleotides. Their existence leads one to wonder whether amino acid sequences are sufficiently degenerate with respect to protein folding to broadly allow arbitrary pairs of functional proteins to be overlapped. Here, we investigate this question by engineering synthetic OLGs using state-of-the-art generative models. To evaluate the approach, we first design overlapped sequences targeting two different protein families. We then encode distinct highly ordered de novo protein structures and observe surprisingly high in silico and experimental success rates. This demonstrates that the overlap constraints under the structure of the standard genetic code do not significantly restrict simultaneous accommodation of well defined 3D folds in alternative reading frames. Our work suggests that OLG sequences may be frequently accessible in nature and could be readily exploited to compress and constrain synthetic genetic circuits.

synthetic biology↗

bioRxiv2025

De Novo Design of Peptide Masks Enables Rapid Generation of Conditionally-Active Miniprotein Binders

The widespread expression of many therapeutic targets in both diseased and healthy tissues presents a significant challenge for protein therapeutics, often resulting in dose-limiting side effects. To focus the action of biotherapeutics at the disease site, they can be reversibly inactivated by tethering an affinity mask through a linker cleavable by a disease-specific cue. However, current methods to generate affinity masks require extensive screening of masking sequence libraries. Here, we report a workflow to design the first de novo peptide masks for the reversible inactivation of miniprotein binders and we show its application on an EGFR antagonist miniprotein. By extending the C-terminus of the miniprotein to cover the binding interface, a masking sequence is generated that decreases binding to EGFR over 1000 times. Binding of the parental miniprotein can be fully rescued by cleavage of the linker between the binder and the mask with tumor-specific proteases. Additionally, by site-specifically conjugating the peptide mask through a chemical linker, we show we can render the protein responsive to alternative stimuli such as light. Our approach opens the doors to dramatically accelerating the development of affinity-based masks for other proteins with therapeutic potential to render them conditionally active to any stimulus.

biochemistry↗

bioRxiv2022

De novo design of high-affinity protein binders to bioactive helical peptides

Many peptide hormones form an alpha-helix upon binding their receptors1-4, and sensitive detection methods for them could contribute to better clinical management. De novo protein design can now generate binders with high affinity and specificity to structured proteins5,6. However, the design of interactions between proteins and short helical peptides is an unmet challenge. Here, we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that with the RFdiffusion generative model, picomolar affinity binders can be generated to helical peptide targets either by noising and then denoising lower affinity designs generated with other methods, or completely de novo starting from random noise distributions; to our knowledge these are the highest affinity designed binding proteins against any protein or small molecule target generated directly by computation without any experimental optimization. The RFdiffusion designs enable the enrichment of parathyroid hormone or other bioactive peptides in human plasma and subsequent detection by mass spectrometry, and bioluminescence-based protein biosensors. Capture reagents for bioactive helical peptides generated using the methods described here could aid in the improved diagnosis and therapeutic management of human diseases.7,8

bioengineering↗

bioRxiv2021

Deep learning methods for designing proteins scaffolding functional sites

Current approaches to de novo design of proteins harboring a desired binding or catalytic motif require pre-specification of an overall fold or secondary structure composition, and hence considerable trial and error can be required to identify protein structures capable of scaffolding an arbitrary functional site. Here we describe two complementary approaches to the general functional site design problem that employ the RosettaFold and AlphaFold neural networks which map input sequences to predicted structures. In the first "constrained hallucination" approach, we carry out gradient descent in sequence space to optimize a loss function which simultaneously rewards recapitulation of the desired functional site and the ideality of the surrounding scaffold, supplemented with problem-specific interaction terms, to design candidate immunogens presenting epitopes recognized by neutralizing antibodies, receptor traps for escape-resistant viral inhibition, metalloproteins and enzymes, and target binding proteins with designed interfaces expanding around known binding motifs. In the second "missing information recovery" approach, we start from the desired functional site and jointly fill in the missing sequence and structure information needed to complete the protein in a single forward pass through an updated RoseTTAFold trained to recover sequence from structure in addition to structure from sequence. We show that the two approaches have considerable synergy, and AlphaFold2 structure prediction calculations suggest that the approaches can accurately generate proteins containing a very wide array of functional sites.

biochemistry↗