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| 1 | Parthenolide reveals an allosteric mode to inhibit the delSGylation activity of SARS-CoV-2 papain-like protease显示文摘The coronavirus papain-like protease(PLpro)of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is responsible for viral polypeptide cleavage and the deISGylation of interferon-stimulated gene 15(ISG15),which enable it to participate in virus replication and host innate immune pathways.Therefore,PLpro is considered an attractive antiviral drug target.Here,we show that parthenolide,a germacrane sesquiterpene lactone,has SARS-CoV-2 PLpro inhibitory activity.Parthenolide covalently binds to Cys-191 or Cys-194 of the PLpro protein,but not the Cys-111 at the PLpro catalytic site.Mutation of Cys-191 or Cys-194 reduces the activity of PLpro.Molecular docking studies show that parthenolide may also form hydrogen bonds with Lys-192,Thr-193,and Gln-231.Furthermore,parthenolide inhibits the deISGylation but not the deubiquitinating activity of PLpro in vitro.These results reveal that parthenolide inhibits PLpro activity by allosteric regulation. | Zhihui Zou Huizhuang Shan Demeng Sun Li Xia Yulong Shi Jiahui Wan Aiwu Zhou Yunzhao Wu Hanzhang Xu Hu Lei Zhijian Xu Yingli Wu | 2022 | Acta Biochimica et Biophysica Sinica2022，54，8： | 0 |
| 2 | EEG Emotion Recognition Using an Attention Mechanism Based on an Optimized Hybrid Model显示文摘Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features. | Huiping Jiang Demeng Wu Xingqun Tang Zhongjie Li Wenbo Wu | 2022 | Computers, Materials & Continua2022，，11： | 0 |
| 3 | The putative propeptide of MycP1 in mycobacterial type VII secretion system does not inhibit protease activity but improves protein stability显示文摘Mycosin-1 protease(MycP1)is a serine protease anchored to the inner membrane of Mycobacterium tuberculosis,and is essential in virulence factor secretion through the ESX-1 type VII secretion system(T7SS).Bacterial physiology studies demonstrated that MycP1 plays a dual role in the regulation of ESX-1 secretion and virulence,primarily through cleavage of its secretion substrate EspB.MycP1 contains a putative N-terminal inhibitory propeptide and a catalytic triad of Asp-His-Ser,classic hallmarks of a sub-tilase family serine protease.The MycP1 propeptide was previously reported to be initially inactive and activated after prolonged incubation.In this study,we have deter-mined crystal structures of MycP1 with(MycP124-422)and without(MycP1^(63-422))the propeptide,and conducted EspB cleavage assays using the two proteins.Very high struc-tural similarity was observed in the two crystal structures.Interestingly,protease assays demonstrated positive EspB cleavage for both proteins,indicating that the putative propeptide does not inhibit protease activity.Molecu-lar dynamic simulations showed higher rigidity in regions guarding the entrance to the catalytic site in MycP124-422 than in MycP1^(63-422),suggesting that the putative propeptide might contribute to the conformational stability of the active site cleft and surrounding regions. | Demeng Sun Qing Liu Yao He Chengliang Wang Fangming Wu Changlin Tian Jianye Zang | 2013 | Protein & Cell2013，4，12： | 0 |
| 4 | Chemical Synthesis of Structurally Defined Phosphorylated Ubiquitins Suggests Impaired Parkin Activation by Phosphorylated Ubiquitins with a Non-Phosphorylated Distal Unit显示文摘Mutations in genes encoding PINK1(PTEN-induced kinase 1)and Parkin(E3 ubiquitin ligase)are identified in familial Parkinson’s disease.However,it remains unclear whether the phosphorylated Ub chains activate wild-type Parkin(w-Parkin)or phosphorylated Parkin(p-Parkin),with the consequent expulsion of the damaged mitochondria. | Man Pan Qingyun Zheng Shuai Gao Qian Qu Yuanyuan Yu Ming Wu Huan Lan Yulei Li Sanling Liu Jiabin Li Demeng Sun Lining Lu Tian Wang Wenhao Zhang Jiawei Wang Yiming Li Hong-Gang Hu Changlin Tian Lei Liu | 2019 | CCS Chemistry2019，1，5： | 0 |
| 5 | Emotion Analysis: Bimodal Fusion of Facial Expressions and EEG显示文摘With the rapid development of deep learning and artificial intelligence,affective computing,as a branch field,has attracted increasing research attention.Human emotions are diverse and are directly expressed via nonphysiological indicators,such as electroencephalogram(EEG)signals.However,whether emotion-based or EEG-based,these remain single-modes of emotion recognition.Multi-mode fusion emotion recognition can improve accuracy by utilizing feature diversity and correlation.Therefore,three different models have been established:the single-mode-based EEG-long and short-term memory(LSTM)model,the Facial-LSTM model based on facial expressions processing EEG data,and the multi-mode LSTM-convolutional neural network(CNN)model that combines expressions and EEG.Their average classification accuracy was 86.48%,89.42%,and 93.13%,respectively.Compared with the EEG-LSTM model,the Facial-LSTM model improved by about 3%.This indicated that the expression mode helped eliminate EEG signals that contained few or no emotional features,enhancing emotion recognition accuracy.Compared with the Facial-LSTM model,the classification accuracy of the LSTM-CNN model improved by 3.7%,showing that the addition of facial expressions affected the EEG features to a certain extent.Therefore,using various modal features for emotion recognition conforms to human emotional expression.Furthermore,it improves feature diversity to facilitate further emotion recognition research. | Huiping Jiang Rui Jiao Demeng Wu Wenbo Wu | 2021 | Computers, Materials & Continua2021，，8： | 0 |
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