Wilhelmsenblackwell7335

A mean absolute error of ∼4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. CDK activation This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.Layered double hydroxides (LDH) demonstrate significant potential across a range of applications, including as catalysts, delivery vehicles for pharmaceuticals, environmental remediation, and supercapacitors. Explaining the mechanism of LDH action at the atomic scale in these and other applications is challenging, however, due to the difficulty in precisely defining the bulk and surface structure and chemical compositions. Here, we focus on the determination of the structure of lithium-aluminum (Li-Al) LDH, which has shown promise in the catalytic depolymerization of lignin, both directly as the catalyst and as a support for gold nanoparticles. While the relative positions of the Li and Al metals are generally well resolved by X-ray crystallography, it is the structures of the anionic layers, consisting of water and carbonate, that are less well established. Combinatorial analyses of all possible positions and rotations of the water and carbonate in the three-layered Li-AL LDH polytope reveals that the phase space is much too large to examine in any reasonable time frame in a one-by-one structure exploration. To overcome this limitation, we develop and deploy a genetic algorithm (GA) wherein fitness is determined by matching a calculated X-ray diffraction (XRD) pattern for a given structure to the known experimental XRD pattern. The GA approach results in structures of high fitness that portend the bulk Li-Al LDH structure. Importantly, the GA approach offers the potential to determine the structures of other LDH, and more generally layered materials, which are generally difficult to describe given the large chemical and structural space to be explored.The well-designed microbial cell factory finds wide applications in chemical, pharmaceutical, and food industries due to its sustainable and environmentally friendly features. Recently, the clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (CRISPR-Cas) systems have been developed into powerful tools to perform genome editing and transcriptional regulation in prokaryotic and eukaryotic cells. Accordingly, these tools are useful to build microbial cell factories not only by reconstructing the genome but also by reprogramming the metabolic network. In this review, we summarize the recent significant headway and potential uses of the CRISPR technology in the construction of efficient microbial cell factories. Moreover, the future perspectives on the improvement and upgradation of CRISPR-based tools are also discussed.DNA writing (living sensing recorders) based whole-cell biosensors can capture transient signals and then convert them into readable genomic DNA changes. The primitive signals can be easily obtained by sequencing technology or analysis of protein activity (such as fluorescent protein). However, the functions of the current living sensing recorders still need to be expanded, and the difficulty of rewriting in complex biological environments has further limited their applications. In this study, we designed a long-term rewritable recording system using a CRISPR base editor-based synthetic genetic circuit, named CRISPR-istop. This system can convert stimuli into changes in the fluorescence intensity (reporter) and single-base mutations in genomic DNA (recording). Furthermore, we updated the biological circuit through the strategy of coupling the single-base mutation (record site) and the loss-of-function of the targeted protein (translation stopped by stop codon introduction), and we can remove edited bacteria from a population through selective sweeps upon applying a selective pressure. It successfully conducted the rewritable reporter and recording of the nutrient arabinose and pollutant arsenite with two rounds of continuous operation (10 passages/round, 12 h/passage). These observations indicated that the CRISPR-istop system can report and record stimuli over time; moreover, the recording can be manually erased and rewritten as needed. This method has great potential to be extended to more complicated recording systems to execute sophisticated tasks in inaccessible environments for synthetic biology and biomedical applications, such as monitoring disease-relevant physiological markers or other molecules.Poly-γ-glutamic acid (γ-PGA) is a decomposable polymer and has been useful in various industries. The biological functions of γ-PGA are closely linked with its molecular weight (MW). In this study, we established an efficient method to produce variable MWs of γ-PGA from renewable biomass (Jerusalem artichoke) by Bacillus amyloliquefaciens. First, a systematic engineering strategy was proposed in B. amyloliquefaciens to construct an optimal platform for γ-PGA overproduction, in which 24.95 g/L γ-PGA generation was attained. Second, 27.12 g/L γ-PGA with an MW of 20-30 kDa was obtained by introducing a γ-PGA hydrolase (pgdS) into the platform strain constructed above, which reveals a potential correlation between the expression level of pgdS and MW of γ-PGA. Then, a Clustered Regularly Interspaced Short Palindromic Repeats interference (CRISPRi) system was further designed to regulate pgdS expression levels, resulting in γ-PGA with variable MWs. Finally, a combinatorial approach based on three sgRNAs with different repression efficiencies was developed to achieve the dynamic regulation of pgdS and obtain tailor-made γ-PGA production in the MW range of 50-1400 kDa in one strain. This study illustrates a promising approach for the sustainable making of biopolymers with diverse molecular weights in one strain through the controllable expression of hydrolase using the CRISPRi system.