Cloning and Characterization of a Pyrethroid Pesticide Decomposing Esterase Gene, \u3cem\u3eEst3385\u3c/em\u3e, from \u3cem\u3eRhodopseudomonas palustris\u3c/em\u3e PSB-S

Full length open reading frame of pyrethroid detoxification gene, Est3385, contains 963 nucleotides. This gene was identified and cloned based on the genome sequence of Rhodopseudomonas palustris PSB-S available at the GneBank. The predicted amino acid sequence of Est3385 shared moderate identities (30–46%) with the known homologous esterases. Phylogenetic analysis revealed that Est3385 was a member in the esterase family I. Recombinant Est3385 was heterologous expressed in E. coli, purified and characterized for its substrate specificity, kinetics and stability under various conditions. The optimal temperature and pH for Est3385 were 35 °C and 6.0, respectively. This enzyme could detoxify various pyrethroid pesticides and degrade the optimal substrate fenpropathrin with a Km and Vmax value of 0.734 ± 0.013 mmol·l−1 and 0.918 ± 0.025 U·µg−1, respectively. No cofactor was found to affect Est3385 activity but substantial reduction of enzymatic activity was observed when metal ions were applied. Taken together, a new pyrethroid degradation esterase was identified and characterized. Modification of Est3385 with protein engineering toolsets should enhance its potential for field application to reduce the pesticide residue from agroecosystems

Revealing the role of fluoride‐rich battery electrode interphases by operando transmission electron microscopy

The solid electrolyte interphase (SEI), a complex layer that forms over the surface of electrodes exposed to battery electrolyte, has a central influence on the structural evolution of the electrode during battery operation. For lithium metallic anodes, tailoring this SEI is regarded as one of the most effective avenues for ensuring consistent cycling behavior, and thus practical efficiencies. While fluoride-rich interphases in particular seem beneficial, how they alter the structural dynamics of lithium plating and stripping to promote efficiency remains only partly understood. Here, operando liquid-cell transmission electron microscopy is used to investigate the nanoscale structural evolution of lithium electrodeposition and dissolution at the electrode surface across fluoride-poor and fluoride-rich interphases. The in situ imaging of lithium cycling reveals that a fluoride-rich SEI yields a denser Li structure that is particularly amenable to uniform stripping, thus suppressing lithium detachment and isolation. By combination with quantitative composition analysis via mass spectrometry, it is identified that the fluoride-rich SEI suppresses overall lithium loss through drastically reducing the quantity of dead Li formation and preventing electrolyte decomposition. These findings highlight the importance of appropriately tailoring the SEI for facilitating consistent and uniform lithium dissolution, and its potent role in governing the plated lithium's structure

The Reactants’ Phase State: A Nonnegligible Factor in Tetralin Hydrogenation Catalysts Evaluation

The effects of reactants’ phase states (gas-liquid and single gas phase) on tetralin hydrogenation were investigated in the fixed bed. The kinetics of tetralin hydrogenation under different phase states was analyzed. Results showed that, without phase transition, the tetralin conversion increased with the rise of temperature. However, it decreased dramatically around the dew point of the feed at which the reactants’ phase state transferred from gas-liquid phase into gas phase. It was also observed that the gas-liquid phase state was favorable to reduce the deactivation of catalyst in the tetralin hydrogenation

BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network

Abstract Background Automatic and accurate extraction of diverse biomedical relations from literature is a crucial component of bio-medical text mining. Currently, stacking various classification networks on pre-trained language models to perform fine-tuning is a common framework to end-to-end solve the biomedical relation extraction (BioRE) problem. However, the sequence-based pre-trained language models underutilize the graphical topology of language to some extent. In addition, sequence-oriented deep neural networks have limitations in processing graphical features. Results In this paper, we propose a novel method for sentence-level BioRE task, BioEGRE (BioELECTRA and Graph pointer neural net-work for Relation Extraction), aimed at leveraging the linguistic topological features. First, the biomedical literature is preprocessed to retain sentences involving pre-defined entity pairs. Secondly, SciSpaCy is employed to conduct dependency parsing; sentences are modeled as graphs based on the parsing results; BioELECTRA is utilized to generate token-level representations, which are modeled as attributes of nodes in the sentence graphs; a graph pointer neural network layer is employed to select the most relevant multi-hop neighbors to optimize representations; a fully-connected neural network layer is employed to generate the sentence-level representation. Finally, the Softmax function is employed to calculate the probabilities. Our proposed method is evaluated on three BioRE tasks: a multi-class (CHEMPROT) and two binary tasks (GAD and EU-ADR). The results show that our method achieves F1-scores of 79.97% (CHEMPROT), 83.31% (GAD), and 83.51% (EU-ADR), surpassing the performance of existing state-of-the-art models. Conclusion The experimental results on 3 biomedical benchmark datasets demonstrate the effectiveness and generalization of BioEGRE, which indicates that linguistic topology and a graph pointer neural network layer explicitly improve performance for BioRE tasks

Improving antibody optimization ability of generative adversarial network through large language model

Generative adversarial networks (GANs) have successfully generated functional protein sequences. However, traditional GANs often suffer from inherent randomness, resulting in a lower probability of obtaining desirable sequences. Due to the high cost of wet-lab experiments, the main goal of computer-aided antibody optimization is to identify high-quality candidate antibodies from a large range of possibilities, yet improving the ability of GANs to generate these desired antibodies is a challenge. In this study, we propose and evaluate a new GAN called the Language Model Guided Antibody Generative Adversarial Network (AbGAN-LMG). This GAN uses a language model as an input, harnessing such models’ powerful representational capabilities to improve the GAN’s generation of high-quality antibodies. We conducted a comprehensive evaluation of the antibody libraries and sequences generated by AbGAN-LMG for COVID-19 (SARS-CoV-2) and Middle East Respiratory Syndrome (MERS-CoV). Results indicate that AbGAN-LMG has learned the fundamental characteristics of antibodies and that it improved the diversity of the generated libraries. Additionally, when generating sequences using AZD-8895 as the target antibody for optimization, over 50% of the generated sequences exhibited better developability than AZD-8895 itself. Through molecular docking, we identified 70 antibodies that demonstrated higher affinity for the wild-type receptor-binding domain (RBD) of SARS-CoV-2 compared to AZD-8895. In conclusion, AbGAN-LMG demonstrates that language models used in conjunction with GANs can enable the generation of higher-quality libraries and candidate sequences, thereby improving the efficiency of antibody optimization. AbGAN-LMG is available at http://39.102.71.224:88/

BERT2DAb: a pre-trained model for antibody representation based on amino acid sequences and 2D-structure

ABSTRACTPrior research has generated a vast amount of antibody sequences, which has allowed the pre-training of language models on amino acid sequences to improve the efficiency of antibody screening and optimization. However, compared to those for proteins, there are fewer pre-trained language models available for antibody sequences. Additionally, existing pre-trained models solely rely on embedding representations using amino acids or k-mers, which do not explicitly take into account the role of secondary structure features. Here, we present a new pre-trained model called BERT2DAb. This model incorporates secondary structure information based on self-attention to learn representations of antibody sequences. Our model achieves state-of-the-art performance on three downstream tasks, including two antigen-antibody binding classification tasks (precision: 85.15%/94.86%; recall:87.41%/86.15%) and one antigen-antibody complex mutation binding free energy prediction task (Pearson correlation coefficient: 0.77). Moreover, we propose a novel method to analyze the relationship between attention weights and contact states of pairs of subsequences in tertiary structures. This enhances the interpretability of BERT2DAb. Overall, our model demonstrates strong potential for improving antibody screening and design through downstream applications

Genome-Wide Identification and Characterization of the Medium-Chain Dehydrogenase/Reductase Superfamily of <i>Trichosporon asahii</i> and Its Involvement in the Regulation of Fluconazole Resistance

The medium-chain dehydrogenase/reductase (MDR) superfamily contains many members that are widely present in organisms and play important roles in growth, metabolism, and stress resistance but have not been studied in Trichosporon asahii. In this study, bioinformatics and RNA sequencing methods were used to analyze the MDR superfamily of T. asahii and its regulatory effect on fluconazole resistance. A phylogenetic tree was constructed using Saccharomyces cerevisiae, Candida albicans, Cryptococcus neoformans, and T. asahii, and 73 MDRs were identified, all of which contained NADPH-binding motifs. T. asahii contained 20 MDRs that were unevenly distributed across six chromosomes. T. asahii MDRs (TaMDRs) had similar 3D structures but varied greatly in their genetic evolution at different phylum levels. RNA-seq and gene expression analyses revealed that the fluconazole-resistant T. asahii strain upregulates xylitol dehydrogenase, and downregulated alcohol dehydrogenase and sorbitol dehydrogenase concluded that the fluconazole-resistant T. asahii strain was less selective toward carbon sources and had higher adaptability to the environment. Overall, our study contributes to our understanding of TaMDRs, providing a basis for further analysis of the genes associated with drug resistance in T. asahii