PET hydrolase (PETase), which hydrolyzes polyethylene terephthalate (PET) into soluble building blocks, provides an attractive avenue for the bioconversion of plastics. Here we present the structures ...of a novel PETase from the PET-consuming microbe Ideonella sakaiensis in complex with substrate and product analogs. Through structural analyses, mutagenesis, and activity measurements, a substrate-binding mode is proposed, and several features critical for catalysis are elucidated.
Poly(ethylene terephthalate) (PET) is a class of plastic material widely used in modern society, but large amounts of PET waste cause severe environmental problems. Obtained from a PET‐consuming ...bacterium Ideonella sakaiensis, the enzyme PETase exhibits superb hydrolytic activity and substrate preference toward PET. Here, we summarize some recent advances in the crystallographic analysis of PETase. These reports uncover structural features of PETase that are involved in its catalytic activity. In comparison to homologous enzymes, PETase contains an additional disulfide bond as well as an extended β8‐α6 loop. More importantly, the crystal structures of PETase in complex with substrate and product analogs provide critical information for understanding the mechanism of action of PETase. In particular, the wobbling conformation of W156 is closely related to the binding of substrate and product. These new findings are of great importance for further in‐depth research and engineering development of PETase, and should advance the implementation of plastic biodegradation strategy.
Poly(ethylene terephthalate) (PET) plastic material waste causes severe environmental burden worldwide. PET biological decomposition, mediated by a specific enzyme called PETase from a bacterium which can utilize PET as a carbon source, has recently attracted much attention. In this review, the crystal structure of the novel PETase reported from several recent advanced studies is summarized.
As the RNA secondary structure is highly related to its stability and functions, the structure prediction is of great value to biological research. The traditional computational prediction for RNA ...secondary prediction is mainly based on the thermodynamic model with dynamic programming to find the optimal structure. However, the prediction performance based on the traditional approach is unsatisfactory for further research. Besides, the computational complexity of the structure prediction using dynamic programming is Formula: see text; it becomes Formula: see text for RNA structure with pseudoknots, which is computationally impractical for large-scale analysis.
In this paper, we propose REDfold, a novel deep learning-based method for RNA secondary prediction. REDfold utilizes an encoder-decoder network based on CNN to learn the short and long range dependencies among the RNA sequence, and the network is further integrated with symmetric skip connections to efficiently propagate activation information across layers. Moreover, the network output is post-processed with constrained optimization to yield favorable predictions even for RNAs with pseudoknots. Experimental results based on the ncRNA database demonstrate that REDfold achieves better performance in terms of efficiency and accuracy, outperforming the contemporary state-of-the-art methods.
Piwi-interacting RNAs (piRNAs) are a new class of small, non-coding RNAs, crucial in the regulation of gene expression. Recent research has revealed links between piRNAs, viral defense mechanisms, ...and certain human cancers. Due to their clinical potential, there is a great interest in identifying piRNAs from large genome databases through efficient computational methods. However, piRNAs lack conserved structure and sequence homology across species, which makes piRNA detection challenging. Current detection algorithms heavily rely on manually crafted features, which may overlook or improperly use certain features. Furthermore, there is a lack of suitable computational tools for analyzing large-scale databases and accurately identifying piRNAs. To address these issues, we propose LSTM4piRNA, a highly efficient deep learning-based method for predicting piRNAs in large-scale genome databases. LSTM4piRNA utilizes a compact LSTM network that can effectively analyze RNA sequences from extensive datasets to detect piRNAs. It can automatically learn the dependencies among RNA sequences, and regularization is further integrated to reduce the generalization error. Comprehensive performance evaluations based on piRNAs from the piRBase database demonstrate that LSTM4piRNA outperforms current advanced methods and is well-suited for analysis with large-scale databases.
Human Vγ9Vδ2 T cells respond to microbial infections and malignancy by sensing diphosphate-containing metabolites called phosphoantigens, which bind to the intracellular domain of butyrophilin 3A1, ...triggering extracellular interactions with the Vγ9Vδ2 T cell receptor (TCR). Here, we examined the molecular basis of this “inside-out” triggering mechanism. Crystal structures of intracellular butyrophilin 3A proteins alone or in complex with the potent microbial phosphoantigen HMBPP or a synthetic analog revealed key features of phosphoantigens and butyrophilins required for γδ T cell activation. Analyses with chemical probes and molecular dynamic simulations demonstrated that dimerized intracellular proteins cooperate in sensing HMBPP to enhance the efficiency of γδ T cell activation. HMBPP binding to butyrophilin doubled the binding force between a γδ T cell and a target cell during “outside” signaling, as measured by single-cell force microscopy. Our findings provide insight into the “inside-out” triggering of Vγ9Vδ2 T cell activation by phosphoantigen-bound butyrophilin, facilitating immunotherapeutic drug design.
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•The crystal structure of HMBPP-bound intracellular BTN3A1 was determined at 1.97 Å•HMBPP forms hydrogen bonds with H 351 for efficient Vγ9Vδ2 T cell activation•An asymmetric intracellular dimer is involved in HMBPP-mediated γδ T cell activation•HMBPP doubles the binding force between extracellular BTN3A and Vγ9Vδ2 TCR
Vγ9Vδ2 T cells sense tumor and microbial metabolites without MHC restriction. Yang et al. used a multifaceted approach to show how the highly potent microbial HMBPP binds to BTN3A1 and triggers inside-out signaling to activate Vγ9Vδ2 T cells. This study will have implications in emerging clinical applications of allogeneic Vγ9Vδ2 T cells.
Coronaviruses (CoVs) are positive single‐stranded RNA viruses that cause severe respiratory syndromes in humans, including severe acute respiratory syndrome (SARS) and Middle East respiratory ...syndrome (MERS). Coronavirus disease 2019 (COVID‐19) caused by a novel severe acute respiratory syndrome CoV (SARS‐CoV‐2) at the end of 2019 became a global pandemic. The 3C‐like cysteine protease (3CLpro) processes viral polyproteins to yield mature non‐structural proteins, thus playing an important role in the CoV life cycle, and therefore is considered as a prominent target for antiviral drugs. To date, many 3CLpro inhibitors have been reported, and their molecular mechanisms have been illustrated. Here, we briefly introduce the structural features of 3CLpro of the human‐related SARS‐CoV, MERS‐CoV and SARS‐CoV‐2, and explore the potency and mechanism of their cognate inhibitors. This information will shed light on the development and optimization of CoV 3CLpro inhibitors, which may benefit the further designation of therapeutic strategies for treating CoV diseases.
The 3C‐like main protease (3CLpro) of coronaviruses, which causes severe respiratory syndrome in humans, is important for the viral life cycle and is a promising antiviral target. This review summarizes the structural features of 3CLpro from three causative agents of human respiratory infection and compounds that were reported to inhibit protease activity and/or virus replication. This information should help guide drug development against COVID‐19 and other related viruses.
Cytochrome P450s are heme‐thiolate enzymes that participate in carbon source assimilation, natural compound biosynthesis and xenobiotic metabolism in all kingdoms of life. P450s can catalyze various ...reactions by using a wide range of organic compounds, thus exhibiting great potential in biotechnological applications. The catalytic reactions of P450s are driven by electron equivalents that are sourced from pyridine nucleotides and delivered by cognate or matching redox partners (RPs). The electron transfer (ET) route from RPs to P450s involves one or more redox center‐containing domains. As the rate of ET is one of the main determinants of P450 efficacy, an in‐depth understanding of the P450 ET pathway should increase our knowledge of these important enzymes and benefit their further applications. Here, the various P450 RP systems along with current understanding of their ET routes will be reviewed. Notably, state‐of‐the‐art structural studies of the two main types of self‐sufficient P450 will also be summarized.
Versatile P450s catalyze various reactions upon acquiring electrons from redox partners. Electrons transferred from the cofactor to heme are of central importance to P450 action, but the route electrons travel remains unveiled. This issue might be probed under the guidance of recent structural studies of self‐sufficient P450s.
This paper overviews the motion vector coding and block merging techniques in the Versatile Video Coding (VVC) standard developed by the Joint Video Experts Team (JVET). In general, inter-prediction ...techniques in VVC can be classified into two major groups: "whole block-based inter prediction" and "subblock-based inter prediction". In this paper, we focus on techniques for whole block-based inter prediction. As in its predecessor, High Efficiency Video Coding (HEVC), whole block-based inter prediction in VVC is represented by adaptive motion vector prediction (AMVP) mode or merge mode. Newly introduced features purely for AMVP mode include symmetric motion vector difference and adaptive motion vector resolution. The features purely for merge mode include pairwise average merge, merge with motion vector difference, combined inter-intra prediction and geometric partitioning mode. Coding tools such as history-based motion vector prediction and bidirectional prediction with coding unit weights can be applied on both AMVP mode and merge mode. This paper discusses the design principles and the implementation of the new inter-prediction methods. Using objective metrics, simulation results show that the methods overviewed in the paper can jointly achieve 6.2% and 4.7% BD-rate savings on average with the random access and low-delay configurations, respectively. Significant subjective picture quality improvements of some tools are also reported when comparing the resulting pictures at same bitrates.
Although it is well known that microbial populations can respond adaptively to challenges from antibiotics, empirical difficulties in distinguishing the roles of de novo mutation and natural ...selection have left several issues unresolved. Here, we explore the mutational properties of Escherichia coli exposed to long-term sublethal levels of the antibiotic norfloxacin, using a mutation accumulation design combined with whole-genome sequencing of replicate lines. The genome-wide mutation rate significantly increases with norfloxacin concentration. This response is associated with enhanced expression of error-prone DNA polymerases and may also involve indirect effects of norfloxacin on DNA mismatch and oxidative-damage repair. Moreover, we find that acquisition of antibiotic resistance can be enhanced solely by accelerated mutagenesis, i.e., without direct involvement of selection. Our results suggest that antibiotics may generally enhance the mutation rates of target cells, thereby accelerating the rate of adaptation not only to the antibiotic itself but to additional challenges faced by invasive pathogens.
Polyethylene terephthalate (PET) is among the most extensively produced plastics, but huge amounts of PET wastes that have accumulated in the environment have become a serious threat to the ...ecosystem. Applying PET hydrolytic enzymes to depolymerize PET is an attractive measure to manage PET pollution, and searching for more effective enzymes is a prerequisite to achieve this goal. A thermostable cutinase that originates from the leaf-branch compost termed ICCG is the most effective PET hydrolase reported so far. Here, we illustrated the crystal structure of ICCG in complex with the PET analogue, mono(2-hydroxyethyl)terephthalic acid, to reveal the enzyme–substrate interaction network. Furthermore, we applied structure-based engineering to modify ICCG and screened for variants that exhibit higher efficacy than the parental enzyme. As a result, several variants with the measured melting temperature approaching 99 °C and elevated PET hydrolytic activity were obtained. Finally, crystallographic analyses were performed to reveal the structural stabilization effects mediated by the introduced mutations. These results are of importance in the context of understanding the mechanism of action of the thermostable PET hydrolytic enzyme and shall be beneficial to the development of PET biodegradation platforms.
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