Biology subjects

Tsuda, K.

Publications and source records attributed to Tsuda, K..

25 records · Page 2Linked to original sources

bioRxiv2021

Machine-learning-guided library design cycle for directed evolution of enzymes: the effects of training data composition on sequence space exploration

Machine learning (ML) is becoming an attractive tool in mutagenesis-based protein engineering because of its ability to design a variant library containing proteins with a desired function. However, it remains unclear how ML guides directed evolution in sequence space depending on the composition of training data. Here, we present a ML-guided directed evolution study of an enzyme to investigate the effects of a known "highly positive" variant (i.e., variant known to have high enzyme activity) in training data. We performed two separate series of ML-guided directed evolution of Sortase A with and without a known highly positive variant called 5M in training data. In each series, two rounds of ML were conducted: variants predicted by the first round were experimentally evaluated, and used as additional training data for the second-round prediction. The improvements in enzyme activity were comparable between the two series, both achieving enzyme activity 2.2-2.5 times higher than 5M. Intriguingly, the sequences of the improved variants were largely different between the two series, indicating that ML guided the directed evolution to the distinct regions of sequence space depending on the presence/absence of the highly positive variant in the training data. This suggests that the sequence diversity of improved variants can be expanded not only by conventional ML using the whole training data, but also by ML using a subset of the training data even when it lacks highly positive variants. In summary, this study demonstrates the importance of regulating the composition of training data in ML-guided directed evolution.

bioengineering↗

bioRxiv2021

Dissecting the co-transcriptome landscape of plants and microbiota members

Interactions between plants and neighboring microbial species are fundamental elements that collectively determine the structure and function of the plant microbiota. However, the molecular basis of such interactions is poorly characterized. Here, we monocolonized Arabidopsis leaves with nine plant- associated bacteria from all major phyla of the plant microbiota and profiled co- transcriptomes of plants and bacteria. We detected both common and distinct co- transcriptome signatures among plant-commensal pairs. In planta responses of commensals were similar to those of a disarmed pathogen characterized by the suppression of genes involved in general metabolism in contrast to a virulent pathogen. We identified genes that are enriched in the genome of plant-associated bacteria and induced in planta, which may be instrumental for bacterial adaptation to the host environment and niche separation. This study provides insights into how plants discriminate among bacterial strains and lays the foundation for in- depth mechanistic dissection of plant-microbiota interactions.

plant biology↗

bioRxiv2021

Prefusion conformation of SARS-CoV-2 receptor-binding domain favors interactions with human receptor ACE2

A new coronavirus pandemic COVID-19, caused by Severe Acute Respiratory Syndrome coronavirus (SARS-CoV-2), poses a serious threat across continents, leading the World Health Organization to declare a Public Health Emergency of International Concern. In order to block the entry of the virus into human host cells, major therapeutic and vaccine design efforts are now targeting the interactions between the SARS-CoV-2 spike (S) glycoprotein and the human cellular membrane receptor angiotensin-converting enzyme, hACE2. By analyzing cryo-EM structures of SARS-CoV-2 and SARS-CoV-1, we report here that the homotrimer SARS-CoV-2 S receptor-binding domain (RBD) that binds with hACE2 has expanded in size, undergoing a large conformational change relative to SARS-CoV-1 S protein. Protomer with the up-conformational form of RBD, which binds with hACE2, exhibits higher intermolecular interactions at the RBD-ACE2 interface, with differential distributions and the inclusion of specific H-bonds in the CoV-2 complex. Further interface analysis has shown that interfacial water promotes and stabilizes the formation of CoV-2/hACE2 complex. This interaction has caused a significant structural rigidification, favoring proteolytic processing of S protein for the fusion of the viral and cellular membrane. Moreover, conformational dynamics simulations of RBD motions in SARS-CoV-2 and SARS-CoV-1 point to the role in modification in the RBD dynamics and their likely impact on infectivity.

bioinformatics↗

bioRxiv2020

Gene expression evolution in pattern-triggered immunity within Arabidopsis thaliana and across Brassicaceae species

Plants recognize surrounding microbes by sensing microbe-associated molecular patterns (MAMPs) to activate pattern-triggered immunity (PTI). Despite their significance for microbial control, the evolution of PTI responses remains largely uncharacterized. Employing comparative transcriptomics of six Arabidopsis thaliana accessions and three additional Brassicaceae species for PTI responses to the MAMP flg22, we identified a set of genes with expression changes under purifying selection in the Brassicaceae species and genes exhibiting species-specific expression signatures. Variation in flg22-triggered transcriptome and metabolome responses across Brassicaceae species was incongruent with their phylogeny while expression changes were strongly conserved within A. thaliana, suggesting directional selection for some species-specific gene expression. We found the enrichment of WRKY transcription factor binding sites in 5-regulatory regions in conserved and species-specific responsive genes, linking the emergence of WRKY-binding sites with the evolution of gene responses in PTI. Our findings advance our understanding of transcriptome evolution during biotic stress.

plant biology↗

bioRxiv2020

Solution structure of Gaussia Luciferase with five disulfide bonds and identification of a putative coelenterazine binding cavity by heteronuclear NMR

Gaussia luciferase (GLuc) is the smallest luciferase (18.2kDa; 168 residues) reported so far and is thus attracting much attention as a reporter protein, but the lack of structural information is hampering further application. Here, we report the first solution structure of a fully active, recombinant GLuc determined by heteronuclear multidimensional NMR. We obtained a natively folded GLuc by bacterial expression and efficient refolding using a solubility tag. Almost perfect assignments of GLuc’s 1H, 13C and 15N backbone signals were obtained. GLuc structure was determined using CYANA, which automatically identified over 2500 NOEs of which > 570 were long-range. GLuc is an all-alpha-helix protein made of nine helices. The region spanning residues 10–18, 36-81, 96-145 and containing eight out of the nine helices was determined with a Cα-atom RMSD of 1.39 Å± 0.39 Å. The structure of GLuc is novel and unique. Two homologous sequential repeats form two anti-parallel bundles made by 4 helices and tied together by three disulfide bonds. The N-terminal helix 1 is grabbed by these 4 helices. Further, we found a hydrophobic cavity where several residues responsible for bioluminescence were identified in previous mutational studies, and we thus hypothesize that this is a catalytic cavity, where the hydrophobic coelenterazine binds and the bioluminescence reaction takes place.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biophysics↗

bioRxiv2019

In planta bacterial multi-omics analysis illuminates regulatory principles underlying plant-pathogen interactions

Understanding how gene expression is regulated in plant pathogens is crucial for pest control and thus global food security. An integrated understanding of bacterial gene regulation in the host is dependent on multi-omic datasets, but these are largely lacking. Here, we simultaneously characterized the transcriptome and proteome of a foliar bacterial pathogen, Pseudomonas syringae, in Arabidopsis thaliana and identified a number of bacterial processes influenced by plant immunity at the mRNA and the protein level. We found instances of both concordant and discordant regulation of bacterial mRNAs and proteins. Notably, the tip component of bacterial type III secretion system was selectively suppressed by the plant salicylic acid pathway at the protein level, suggesting protein-level targeting of the bacterial virulence system by plant immunity. Furthermore, gene co-expression analysis illuminated previously unknown gene regulatory modules underlying bacterial virulence and their regulatory hierarchy. Collectively, the integrated in planta bacterial omics approach provides molecular insights into multiple layers of bacterial gene regulation that contribute to bacterial growth in planta and elucidate the role of plant immunity in controlling pathogens.

plant biology↗

bioRxiv2018

A machine-learning-guided mutagenesis platform for accelerated discovery of novel functional proteins

Molecular evolution based on mutagenesis is widely used in protein engineering. However, optimal proteins are often difficult to obtain due to a large sequence space that requires high costs for screening experiments. Here, we propose a novel approach that combines molecular evolution with machine learning. In this approach, we conduct two rounds of mutagenesis where an initial library of protein variants is used to train a machine-learning model to guide mutagenesis for the second-round library. This enables to prepare a small library suited for screening experiments with high enrichment of functional proteins. We demonstrated a proof-of-concept of our approach by altering the reference green fluorescent protein (GFP) so that its fluorescence is changed to yellow while improving its fluorescence intensity. Using 155 and 78 variants for the initial and the second-round libraries, respectively, we successfully obtained a number of proteins showing yellow fluorescence, 12 of which had better fluorescence performance than the reference yellow fluorescent protein (YFP). These results show the potential of our approach as a powerful platform for accelerated discovery of functional proteins.

bioengineering↗