Biology subjects

Bu, K.

Publications and source records attributed to Bu, K..

5 recordsLinked to original sources

bioRxiv2024

DDX3 regulates the cap-independent translation of the Japanese encephalitis virus via its interactions with PABP1 and the untranslated regions of the viral genome

The translation of global cellular proteins is almost completely repressed in cells with flavivirus infection, while viral translation remains efficient. The mechanisms of flaviviruses evade host translational shutoff are largely unknown. Here, we identified viral elements and host factors associated with JEV evasion of host shutoff. JEV 5'UTR lacked IRES or IRES-like activity, while noncapped 5'UTR initiated translation in the presence of 3'UTR. Furthermore, the elements DB2 and sHP-SL within 3'UTR were involved in the regulation of cap-independent translation, which is conserved in the genus Orthoflavivirus. By RNA affinity purification and mass spectrometry analysis, cellular DDX3 and PABP1 were identified as key factors in regulating cap-independent translation of JEV via their interactions with DB2 and sHP-SL RNA structures. Mechanistically, we revealed that DDX3 could bind to both 5'UTR and 3'UTR of the JEV genome to establish a closed-loop architecture, recruit eIF4G/eIF4A to form the DDX3/PABP1/eIF4G/eIF4A tetrameric complex via its interaction with PABP1, thereby recruiting 43S PIC to the 5'-end of the JEV genome to start translation. Our findings demonstrated a noncanonical translation strategy employed by JEV and further revealed the regulatory roles of DDX3 and PABP1 in this mechanism. These results expand our knowledge of the translation initiation regulation in flaviviruses under the state of host translational shutoff, which provides a conserved antiviral target against orthoflavivirus.

microbiology↗

bioRxiv2024

Generalizable Quantum Computing Pipeline for Real World Drug Discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing an advanced quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the pragmatic application of quantum computation and propels it towards practical industrial adoption. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.

bioinformatics↗

bioRxiv2023

Quantum entropy reveals chromosomal disorder of ancestry tracts in genetic admixture

Ancestry tracts are contiguous haplotype blocks inherited from distinct groups of common ancestors. The genomic distribution of ancestry tracts (or local ancestry) provides rich information about evolutionary mechanisms shaping the genetic composition of hybrids. The correlation structure of ancestry tracts has been particularly useful in both empirical and theoretical studies, but there is a lack of descriptive measures operating on arbitrarily large genomic blocks to summarize this correlation structure without imposing too many assumptions about admixture. We here develop an approach inspired by quantum information theory to quantify this correlation structure. The key innovation is to represent local ancestry as quantum states, where less correlation in local ancestry leads to elevated quantum entropy. By leveraging a variety of entropy measures on local ancestry signals, we show that entropy is deeply connected to co-ancestry probabilities between and within haplotypes, so that ancestral recombination graphs become pivotal to the study of entropy dynamics in admixture. We use this approach to characterize a standard neutral admixture model with an arbitrary number of sources, and recover entropic laws governing the dynamics of ancestry tracts under recombination and genetic drift, which resembles the second law of thermodynamics. In application, entropy is well-defined on arbitrarily large genomic blocks with either phased or unphased local ancestry, and is insensitive to a small amount of noise. These properties are superior to simple statistics on ancestry tracts such as tract length and junction density. Finally, we construct an entropic index reflecting the degree of intermixing among ancestry tracts over a chromosomal block. This index confirms that the Z chromosome in a previously studied butterfly hybrid zone has the least potential of ancestry mixing, thus conforming to the "large-X/Z" effect in speciation. Together, we show that quantum entropy provides a useful framework for studying ancestry tract dynamics in both theories and real systems.

genetics↗

bioRxiv2022

Solving Anscombe's Quartet using a Transfer Learning Approach

Analysis of high-dimensional datasets often involves usage of summary statistics, one of which is the correlation coefficient. These values are then used to inform downstream analysis, whether in feature selection or in subsequent construction of networks and heatmaps. Condensing pairwise scatterplots into these singular values however, often results in a loss of information. Originally proposed by F. J. Anscombe in his famous Anscombes Quartet, this phenomenon has been canonically used to demonstrate the importance of plotting and the limitations of summary statistics such as correlation or variance [F.J. Anscombe, (1973) American Statistician. 27 (1), 17-21]. While numerous methods exist for the generation of visually distinct datasets that share similar summary statistics, the converse has not been extensively studied. To address this gap, we propose ICLUST (Image CLUSTering), an image classifier tool that can visually distinguish correlations with similar summary statistics in simulations and identify meaningful clusters in real data. Such a tool can potentially benefit those performing exploratory analysis or feature selection in a complementary fashion by identifying relationships between variables that traditional summary metrics cannot provide. Significance StatementDistilling large-scale, multidimensional datasets via analysis of pairwise relationships often employs a single value to describe the relationship between variables. However, as demonstrated through simulations, such summarization fails to retain the nuances of the data. Characteristics such as the type of relationship (linear versus nonlinear, etc.) and the spread of the data are commonly lost when using correlations. Here we propose a transfer learning framework, borrowing from image clustering and classification software, to visually classify graphs. We apply our method towards separation of scatterplots with similar correlation statistics but visually distinctive patterns in both simulations and real data, demonstrating its broad applicability.

bioinformatics↗

bioRxiv2019

Dynamics of Auxilin1 and GAK in clathrin-1 mediated traffic

Clathrin coated vesicles formed at the plasma membrane lose their clathrin lattice within seconds of pinching off, through the action of the Hsc70 \"uncoating ATPase\". The J-domain containing proteins, auxilin1 (Aux1) and auxilin2/cyclin-G dependent kinase (GAK), recruit Hsc70. Aux1 and GAK are closely related homologs, each with a phosphatase- and tensin-like (PTEN-like) domain, a clathrin-binding region, and a C-terminal J-domain; GAK has an additional, N-terminal Ser/Thr kinase domain. The PTEN-like domain has no phosphatase activity, but it can recognize phosphatidylinositol phosphate head groups. Aux1 and GAK appear on coated vesicles in successive transient bursts, immediately after dynamin mediated membrane scission has released the vesicle from the plasma membrane. We show here that these bursts represent recruitment of a very small number of auxilins such that even 4-6 molecules are sufficient to mediate uncoating. In contrast, we could not detect auxilins in abortive pits or at any time during coated-pit assembly. We have also shown previously that clathrin coated vesicles have a dynamic phosphoinositide landscape, and we have proposed that lipid head group recognition might determine the timing of Aux1 and GAK appearance. We now show that differential recruitment of Aux1 and GAK correlates with temporal variations in phosphoinositide composition, consistent with a lipid-switch timing mechanism.

cell biology↗