DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

The enzyme turnover rate, kcat, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, kcat values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict kcat and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on kcat values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems

Myosin Va binding to neurofilaments is essential for correct myosin Va distribution and transport and neurofilament density

The identification of molecular motors that modulate the neuronal cytoskeleton has been elusive. Here, we show that a molecular motor protein, myosin Va, is present in high proportions in the cytoskeleton of mouse CNS and peripheral nerves. Immunoelectron microscopy, coimmunoprecipitation, and blot overlay analyses demonstrate that myosin Va in axons associates with neurofilaments, and that the NF-L subunit is its major ligand. A physiological association is indicated by observations that the level of myosin Va is reduced in axons of NF-L–null mice lacking neurofilaments and increased in mice overexpressing NF-L, but unchanged in NF-H–null mice. In vivo pulse-labeled myosin Va advances along axons at slow transport rates overlapping with those of neurofilament proteins and actin, both of which coimmunoprecipitate with myosin Va. Eliminating neurofilaments from mice selectively accelerates myosin Va translocation and redistributes myosin Va to the actin-rich subaxolemma and membranous organelles. Finally, peripheral axons of dilute-lethal mice, lacking functional myosin Va, display selectively increased neurofilament number and levels of neurofilament proteins without altering axon caliber. These results identify myosin Va as a neurofilament-associated protein, and show that this association is essential to establish the normal distribution, axonal transport, and content of myosin Va, and the proper numbers of neurofilaments in axons

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook.

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production

Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade‐off between secondary and primary metabolism

Abstract Lactiplantibacillus plantarum is a probiotic bacterium widely used in food and health industries, but its gene regulatory information is limited in existing databases, which impedes the research of its physiology and its applications. To obtain a better understanding of the transcriptional regulatory network of L. plantarum, independent component analysis of its transcriptomes was used to derive 45 sets of independently modulated genes (iModulons). Those iModulons were annotated for associated transcription factors and functional pathways, and active iModulons in response to different growth conditions were identified and characterized in detail. Eventually, the analysis of iModulon activities reveals a trade‐off between regulatory activities of secondary and primary metabolism in L. plantarum

Parallel Yuan-Ming Yuan Imperial Garden: from digital twin garden to metaverse smart heritage parkMengzhen KANG

International audienceYuan-Ming Yuan Imperial Garden is a historically royal garden; it not only occupies an important position in the history of Chinese garden, but also enjoys a high reputation in the world. The historical and cultural values contained in Yuan-Ming Yuan Imperial Garden needs to be widely understood by the Chinese people and be remembered by the world through a new way. Focusing on the work policy for cultural relics of “protection first, strict management, mining value, rational utilization and good transmission”, a new solution of parallel Yuan-Ming Yuan Imperial Garden was proposed, which provided technical reference for the construction of the smart Yuan-Ming Yuan Imperial Garden. Parallel Yuan-Ming Yuan Imperial Garden is the application of ACP theory in the operation and management. Descriptive intelligence will be used to construct a virtual Yuan-Ming Yuan Imperial Garden, predictive intelligence will be used to conduct large-scale computational experiments in the virtual Yuan-Ming Yuan Imperial Garden, and prescriptive intelligence and parallel execution will be used to outbreak the geographical limit and lead to the smart management of Yuan-Ming Yuan Imperial Garden. It is expected that the development and enrichment of the Yuan-Ming Yuan Imperial Garden in the virtual world, and the increasingly parallel interaction and integration with the real world, will bring about a new operating mod

Machine learning aided construction of the quorum sensing communication network for human gut microbiota

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database (http://www.qshgm.lbci.net/) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases