Biology subjects

Tsuboyama, K.

Publications and source records attributed to Tsuboyama, K..

11 recordsLinked to original sources

bioRxiv2026

Physiological mapping and prediction reveal stress-responsive translation initiation landscapes

Translation initiation defines the protein output of the transcriptome, but its physiological dynamics in cells and tissues remain incompletely characterized. Here we develop TISCA2, an inhibitor-free method that captures eIF5B-associated initiating ribosomes to map translation initiation sites genome-wide. By directly identifying start sites rather than inferring translation from elongating-ribosome periodicity, TISCA2 improves the detection of short and overlapping translons. Translation of a subset of TISCA2-identified translons is supported by N-terminal proteomics and HLA-I immunopeptidomics. The arsenite-induced integrated stress response broadly redistributes start-site usage, reducing upstream initiation in favor of downstream initiation. TISCA2 further reveals tissue-specific initiation landscapes in vivo, with increased detection of downstream candidate start sites in the testis. Finally, we develop IncepTIS, a sequence-based deep learning model that predicts position-resolved and condition-dependent initiation landscapes. IncepTIS-guided optimization generates de novo mRNA 5' leaders with stress-responsive initiation patterns. Together, TISCA2 and IncepTIS provide an experimental and computational framework for mapping, predicting and engineering translation initiation in mammalian cells and tissues.

molecular biology↗

bioRxiv2026

Massively parallel quantification and prediction of monomeric protein yield in Escherichia coli

Obtaining sufficient quantities of purified proteins in their intended molecular states is essential for biological research and protein engineering. Advances in computational protein design have expanded the functional capabilities of de novo proteins and accelerated candidate generation. However, reliable methods for ranking expected monomeric yield of candidate de novo designs, including molecular binders, fluorescent proteins and enzymes, have yet to be established. Standardized measurements of monomeric yield across diverse sequences remain scarce, limiting our understanding of its determinants and the development of accurate predictors for candidate selection. Here we developed a scalable pooled assay combining peptide barcodes, size-exclusion chromatography and mass spectrometry to quantify monomeric yield for over 39,000 natural protein domain variants, de novo proteins and nanobodies under uniform and standardized Escherichia coli production conditions. We used this dataset to (1) quantify the relationship between folding stability and monomeric yield, (2) benchmark inverse-folding methods, (3) evaluate the utility of structure-prediction confidence metrics in selecting high-yield design candidates, and (4) identify sequence and structural patterns in nanobodies associated with monomeric yield and monomer-to-dimer yield ratios. We further developed ESMC-MonoYield, a protein language model fine-tuned to predict monomeric yield from amino acid sequence alone, and prospectively demonstrated enrichment of high-yield candidates among BindCraft-designed binders. Our approach enables standardized, large-scale measurement of monomeric yield across diverse proteins, and establishes monomeric yield as a sequence-encoded property that can be learned, predicted and ultimately designed.

biophysics↗

bioRxiv2026

Accurate protein stability prediction for small domains using mega-scale experiments

Predicting absolute protein folding stability is a long-standing challenge in biophysics, with broad applications in protein design and in understanding genetic variation and evolution. Physics-based simulations have shown limited success at predicting stability and are often computationally intractable, and machine learning methods have been constrained by the lack of sufficiently large experimental datasets. We recently introduced cDNA display proteolysis, a cell-free approach that can measure folding stability for nearly one million protein domains in parallel. Here, we applied this method to measure stability for 1.8 million diverse protein domains 60-80 amino acids in length primarily taken from the MGnify metagenomic database and spanning over 200,000 sequence families. Using this new "MGnify Stability dataset", we developed the predictive models SaProt{Delta}G and ESM3{Delta}G, which accurately predict absolute folding stability for small domains with root mean squared error of 0.8 kcal/mol over a 6 kcal/mol range (Spearman rank correlation of 0.88). These predictors show high accuracy at predicting effects of substitutions, insertions, and deletions, successfully identify global trends toward higher stability in thermophilic organisms, and improve discrimination of stable and unstable computationally designed proteins. Our results illustrate how megascale biophysical measurements can complement existing evolutionary and structural data to enable accurate absolute stability prediction for small domains.

biophysics↗

bioRxiv2025

Large-scale discovery, analysis, and design of protein energy landscapes

All folded proteins continuously fluctuate between their low-energy native structures and higher energy conformations that can be partially or fully unfolded. These rare states influence protein function, interactions, aggregation, and immunogenicity, yet they remain far less understood than protein native states. Although native protein structures are now often predictable with impressive accuracy, conformational fluctuations and their energies remain largely invisible and unpredictable, and experimental challenges have prevented large-scale measurements that could improve machine learning and physics-based modeling. Here, we introduce a multiplexed experimental approach to analyze the energies of conformational fluctuations for hundreds of protein domains in parallel using intact protein hydrogen-deuterium exchange mass spectrometry. We analyzed 5,778 domains 28-64 amino acids in length, revealing hidden variation in conformational fluctuations even between sequences sharing the same fold and global folding stability. Site-resolved hydrogen exchange NMR analysis of 13 domains showed that these fluctuations often involve entire secondary structural elements with lower stability than the overall fold. Computational modeling of our domains identified structural features that correlated with the experimentally observed fluctuations, enabling us to design mutations that stabilized low-stability structural segments. Our dataset enables new machine learning-based analysis of protein energy landscapes, and our experimental approach promises to reveal these landscapes at unprecedented scale.

biophysics↗

bioRxiv2025

Rational engineering of lipid-binding probes via high-throughput protein-lipid interaction screening

Lipid-binding domains, originally isolated from natural proteins, are useful tools essential for analyzing membrane lipids in cells, and their applications are varied. Yet, there is no general strategy for engineering lipid-binding domains. Here, we present a robust method for monitoring protein-lipid interactions, named the Cell surface Liposome Binding (CLiB) assay. Using this technique, we isolated high-affinity lipid-binding domains and nanobodies that preferentially bind to phosphatidylinositol phosphates. Furthermore, by combining the CLiB assay with next-generation sequencing, allowing the analysis of more than 10,000 clones in parallel, we identified novel variants with enhanced binding to phosphatidylinositol phosphates and uncovered a common structural feature: a positively charged pocket necessary for binding, formed by the three loop regions. This study opens a new avenue for the rational design and generation of lipid-binding probes on demand.

cell biology↗

bioRxiv2024

Implicit modeling of the conformational landscape and sequence allows scoring and generation of stable proteins

Generative protein modeling provides advanced tools for designing diverse protein sequences and structures. However, accurately modeling the conformational landscape and designing sequences--ensuring that the designed sequence folds into the target structure as its most stable structure--remains a critical challenge. In this study, we present a systematic analysis of jointly optimizing P(structure|sequence) and P(sequence|structure), which enables us to find optimal solutions for modeling the conformational landscape. We support this approach with experimental evidence that joint optimization is superior for (1) designing stable proteins using a joint model (TrROS (TrRosetta) and TrMRF) (2) achieving high accuracy in stability prediction when jointly modeling (half-masked ESMFold pLDDT+ ESM2 Pseudo-likelihood). We further investigate features of sequences generated from the joint model and find that they exhibit higher frequencies of hydrophilic interactions, which may help maintain both secondary structure registry and pairing.

biophysics↗

bioRxiv2022

Mega-scale experimental analysis of protein folding stability in biology and protein design

Advances in DNA sequencing and machine learning are illuminating protein sequences and structures on an enormous scale. However, the energetics driving folding are invisible in these structures and remain largely unknown. The hidden thermodynamics of folding can drive disease, shape protein evolution, and guide protein engineering, and new approaches are needed to reveal these thermodynamics for every sequence and structure. We present cDNA display proteolysis, a new method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of ~850,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 354 natural and 188 de novo designed protein domains 40-72 amino acids in length. Using this immense dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate, and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability. One-Sentence SummaryMassively parallel measurement of protein folding stability by cDNA display proteolysis

biophysics↗

bioRxiv2022

A canonical chaperone and a heat-resistant obscure (Hero) protein mediate similar conformational modulation and aggregation suppression of TDP-43

Protein misfolding and aggregation are characteristic features of neurodegenerative diseases. While molecular chaperones are well-known suppressors of these aberrant events, we recently reported that highly disordered, hydrophilic and charged heat-resistant obscure (Hero) proteins may have similar effects. Specifically, Hero proteins can maintain the activity of other proteins from denaturing conditions in vitro, while their overexpression can suppress cellular aggregation and toxicity associated with aggregation-prone proteins. However, it is unclear how these protective effects are achieved. Here, we utilized single-molecule FRET to monitor the conformations of the aggregation-prone prion-like low complexity domain (LCD) of TAR DNA-binding protein 43 (TDP-43). While we observed high conformational heterogeneity in wild-type LCD, the ALS-associated mutation A315T promoted collapsed conformations. In contrast, an Hsp40 chaperone, DNAJA2, and a Hero protein, Hero11 stabilized extended states of the LCD, consistent with their ability to suppress the aggregation of TDP-43. Our results link single-molecule effects on conformation to macro effects on bulk aggregation, where a Hero protein, like a chaperone, can maintain the conformational integrity of a client protein to prevent its aggregation.

biophysics↗

bioRxiv2022

Stabilization of recombinant proteins by fusion with heat-resistant obscure (Hero) proteins

Although recombinant proteins are widely used in biotechnology and pharmaceutical industries, improving their solubility and stability is often a challenging issue. We recently discovered a class of highly unstructured heat-resistant obscure (Hero) proteins, which function to protect other "client" proteins in trans from various stresses in vitro and in vivo. Here, we show that fusion of Hero proteins in cis can enhance the molecular property of recombinant proteins. Fusion with Hero11 improved the otherwise challenging production of TAR DNA-binding protein of 43 kDa (TDP-43) in Escherichia coli. Moreover, fusing with Hero9 strongly protected the activity of firefly luciferase bearing destabilizing mutations against heat and other stress conditions. These data suggest that Hero proteins have the potential to be used as versatile stabilization tags for recombinant protein production.

biochemistry↗

bioRxiv2021

Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation

Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet some protein folds are easier to design than others. Previous work identified the 43-residue {square}{beta}{beta}{square} fold as especially challenging: the best designs had only a 2% success rate, compared to 39-87% success for other simple folds (1). This suggested the {square}{beta}{beta}{square} fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over ten thousand new {square}{beta}{beta}{square} proteins and found over three thousand of them to fold into stable structures using a high-throughput protease-based assay. Nuclear magnetic resonance, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed {square}{beta}{beta}{square} structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the {square}{beta}{beta}{square} topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding. SignificanceMost computationally designed proteins fail to fold into their designed structures. This low success rate is a major obstacle to expanding the applications of protein design. In previous work, we discovered a small protein fold that was paradoxically challenging to design (only a 2% success rate) even though the fold itself is very simple. Here, we used a recently developed high-throughput approach to comprehensively examine the design rules for this simple fold. By designing over ten thousand proteins and experimentally measuring their folding stability, we discovered the key biophysical properties that determine the stability of these designs. Our results illustrate general lessons for protein design and also demonstrate how high-throughput stability studies can quantify the importance of different biophysical forces.

synthetic biology↗

bioRxiv2019

A widespread family of heat-resistant obscure (Hero) proteins protect against protein instability and aggregation

Proteins are typically denatured and aggregated by heat. Exceptions to this principle include highly disordered and heat-resistant proteins found in extremophiles, which help these organisms tolerate extreme conditions such as drying, freezing, and high salinity. In contrast, the functions of heat-soluble proteins in non-extremophilic organisms including humans remain largely unexplored. Here we report that heat-resistant obscure (Hero) proteins, which remain soluble after boiling at 95{degrees}C, are widespread in Drosophila and humans. Hero proteins are hydrophilic and highly charged, and function to stabilize various "client" proteins, protecting them from denaturation even under stress conditions such as heat shock, desiccation, and exposure to organic solvents. Hero proteins can also block several different types of pathological protein aggregations in cells and in Drosophila strains that model neurodegenerative diseases. Moreover, Hero proteins can extend lifespan of Drosophila. Our study reveals that organisms naturally use Hero proteins as molecular shields to stabilize protein functions, highlighting their biotechnological and therapeutic potential.

molecular biology↗