Biology subjects

Shonai, D.

Publications and source records attributed to Shonai, D..

5 recordsLinked to original sources

bioRxiv2026

Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

De novo protein design has advanced rapidly, yet designing binders to polar, solvent-exposed epitopes and small, flexible ligands remains challenging. Such hydrated surfaces and flexible molecules, including carbohydrates, provide few of the hydrophobic contacts favoured by current methods and have largely resisted de novo binders. To address this challenge, here we introduce latent generative search for binder design, a novel framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model. The model codesigns sequence and structure - generating them together in a continuous latent space - and thereby removes the inverse-folding step on which current methods rely. In a screen of more than one million designs by multiplexed phage display, latent generative search produced more validated binders than every other method tested, its codesigned sequences surpassing post hoc redesign. It delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets. Our approach also accessed previously untapped biology, generating the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens - a polar, flexible target class beyond the reach of current design methods.

bioengineering↗

bioRxiv2026

In vivo Proximity & Spatial Proteomics with CRISPR Screening Identify STXBP1 as a Protective Modifier of a-synuclein Toxicity in Dopamine Neurons.

Parkinsons disease (PD) is a disease of adults involving the loss of dopaminergic neurons after a long, asymptomatic, prodromal period. -synuclein, LRRK2, and VPS35 are linked to familial PD, however, how these mutations predispose dopamine neurons to death during the early prodromal phases remains unclear. Here, we used in vivo native proximity proteomics (iBioID) and dopaminergic neuron-specific subcellular proteomics across multiple PD models to uncover early alterations preceding neuronal loss. Our analyses identified convergent disruptions in synaptic protein abundance, indicating that presynaptic trafficking defects are early events in PD pathogenesis. Using a targeted CRISPR-based genetic screen in dopamine neurons, we demonstrated that mimicking this misregulation of STXBP1 amplifies vulnerability to -synuclein, implicating it as a previously underappreciated toxicity buffering factor. These findings highlight convergent mechanisms that sensitize dopamine neuronal degeneration and that presynaptic vesicle SNARE-complex proteins could serve as key targets for disease-modifying therapies in PD and related neurodegenerative disorders. HighlightsO_LIIn vivo native-BioID mapping of multiple Parkinsons disease (PD) protein interactomes revealed a convergent presynaptic network. C_LIO_LIiBioID analysis on mutant PD proteins (-synuclein A30P, LRRK2 G2019S, VPS35 D620N) uncovered mutation-specific shifts in local proximity networks, notably in endocytic and vesicle recycling pathways. C_LIO_LISpatial proteomics (iBioCoFrac) of dopamine neurons in vivo identified functional modules with reduced levels of key synaptic proteins in PD models. C_LIO_LIComparative proteomics using iBioCoFrac revealed synaptic vesicle regulation as a primary site of molecular convergence and early molecular signatures in dopamine neurons across multiple PD mouse models. C_LIO_LIAn in vivo CRISPR screen pinpointed the presynaptic protein Stxbp1/Munc18-1 as an -synuclein toxicity modifier in dopaminergic neurons. C_LI

neuroscience↗

bioRxiv2025

Deep Learning-coupled Proximity Proteomics to Deconvolve Kinase Signaling In Vivo

Deconvolving the substrates of hundreds of kinases linked to phosphorylation networks driving cellular behavior is a fundamental, unresolved biological challenge, largely due to the poorly understood interplay of kinase selectivity and substrate proximity. We introduce KolossuS, a deep learning framework leveraging protein language models to decode kinase-substrate specificity. KolossuS achieves superior prediction accuracy and sensitivity across mammalian kinomes, enabling proteome-wide predictions and evolutionary insights. By integrating KolossuS with CRISPR-based proximity proteomics in vivo, we capture kinase-substrate recognition and spatial context, obviating prior limitations. We show this combined framework identifies kinase substrates associated with physiological states such as sleep, revealing both known and novel Sik3 substrates during sleep deprivation. This novel integrated computational-experimental approach promises to transform systematic investigations of kinase signaling in health and disease.

cell biology↗

bioRxiv2024

Miniaturizing, Modifying, and Augmenting Nature's Proteins with Raygun

Proteins have evolved over billions of years through extensive and coordinated substitutions, insertions and deletions (indels). Computational protein design cannot yet fully mimic natures ability to engineer new proteins from existing templates. Protein language models generate informative per-residue representations, but leveraging them to execute large-scale, function-preserving mutations and indels has remained beyond reach. We introduce Raygun, a generative AI framework that unlocks efficient miniaturization, modification, and augmentation of proteins, using a novel probabilistic encoding of protein sequences constructed from language model embeddings. Emulating evolution, Raygun shrinks proteins by 10-25% (sometimes over 50%) while preserving predicted structural integrity and fidelity, introduces extensive sequence diversity while preserving functional sites, and can expand proteins beyond their natural size. These capabilities unlock new opportunities in gene therapy and biotechnology. In cell-based validation, Raygun successfully miniaturized fluorescent proteins, two of which are smaller than 96% of fluorescent proteins reported in FPbase, as well as TurboID, a synthetic biotin ligase widely adopted for proteomics. It also successfully expanded Epidermal Growth Factor (EGF), a natural binding partner to the EGFR protein, generating EGF variants with higher binding affinity than the wildtype. Rayguns conceptual innovations in template-based protein design reveal that protein function can be encoded in a length-independent space. This fundamental insight bridges protein representation learning with evolutionary biology and could unlock the development of more efficient molecular tools and biological therapeutics.

bioinformatics↗

bioRxiv2022

Chemico-genetic Analysis of Native Autism Proteomes Reveals Shared Biology Predictive of Functional Modifiers

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving high specificity spatial interactomes compared to prior methods. The resulting native proximity interactomes are enriched for human genes dysregulated in the brain of autistic individuals and reveal unexpected and highly significant interactions with other lower-confidence autism risk gene products, positing new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

neuroscience↗