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学者姓名：鄢仁祥
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Abstract ：
BackgroundLysine crotonylation (Kcr) is a crucial protein post-translational modification found in histone and non-histone proteins. It plays a pivotal role in regulating diverse biological processes in both animals and plants, including gene transcription and replication, cell metabolism and differentiation, as well as photosynthesis. Despite the significance of Kcr, detection of Kcr sites through biological experiments is often time-consuming, expensive, and only a fraction of crotonylated peptides can be identified. This reality highlights the need for efficient and rapid prediction of Kcr sites through computational methods. Currently, several machine learning models exist for predicting Kcr sites in humans, yet models tailored for plants are rare. Furthermore, no downloadable Kcr site predictors or datasets have been developed specifically for plants. To address this gap, it is imperative to integrate existing Kcr sites detected in plant experiments and establish a dedicated computational model for plants.ResultsMost plant Kcr sites are located on non-histones. In this study, we collected non-histone Kcr sites from five plants, including wheat, tabacum, rice, peanut, and papaya. We then conducted a comprehensive analysis of the amino acid distribution surrounding these sites. To develop a predictive model for plant non-histone Kcr sites, we combined a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and attention mechanism to build a deep learning model called PlantNh-Kcr. On both five-fold cross-validation and independent tests, PlantNh-Kcr outperformed multiple conventional machine learning models and other deep learning models. Furthermore, we conducted an analysis of species-specific effect on the PlantNh-Kcr model and found that a general model trained using data from multiple species outperforms species-specific models.ConclusionPlantNh-Kcr represents a valuable tool for predicting plant non-histone Kcr sites. We expect that this model will aid in addressing key challenges and tasks in the study of plant crotonylation sites.
Keyword ：
Attention mechanism Attention mechanism Bidirectional long short-term memory Bidirectional long short-term memory Convolutional neural network Convolutional neural network Crotonylation Crotonylation Focal loss Focal loss
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| GB/T 7714 | Jiang, Yanming , Yan, Renxiang , Wang, Xiaofeng . PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants [J]. | PLANT METHODS , 2024 , 20 (1) . |
| MLA | Jiang, Yanming 等. "PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants" . | PLANT METHODS 20 . 1 (2024) . |
| APA | Jiang, Yanming , Yan, Renxiang , Wang, Xiaofeng . PlantNh-Kcr: a deep learning model for predicting non-histone crotonylation sites in plants . | PLANT METHODS , 2024 , 20 (1) . |
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Abstract ：
Bacillus amyloliquefaciens alpha-amylase (BAA) is one of well-known midrange thermostability amylases that are widely used in food and washing processes. However, the improvement of its catalytic properties by molecular modification is still lagging. To improve the activity of alpha-amylase BAA, mutants BAA28 and BAA294 are constructed via error-prone PCR and purified by column chromatography in the present study. The catalytic efficiencies (K-cat/K-m) of BAA28 and BAA294 are 2.42 and 2.73 mL mg(-1) s(-1), which are 43% and 61% higher than that of the wild-type BAA, respectively. Their specific activities are also increased by 40% and 62%, respectively, with no apparent changes of optimum temperature and pH. Homology modeling and molecular docking analysis suggest that the reduced steric hindrance is an important factor that enhances catalytic efficiencies and specific activities of the variants. These results may deepen the understanding of the mechanisms underlying the effects of each mutation on the catalytic efficiency of BAA and facilitate the construction of potent BAA mutants.
Keyword ：
Bacillus amyloliquefaciens alpha-amylase Bacillus amyloliquefaciens alpha-amylase protein structure protein structure random mutation random mutation specific activity specific activity
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| GB/T 7714 | Yuan, Susu , Li, Renkuan , Lin, Biyu et al. Engineering of Bacillus amyloliquefaciens α-Amylase for Improved Catalytic Efficiency by Error-Prone PCR [J]. | STARCH-STARKE , 2023 , 75 (11-12) . |
| MLA | Yuan, Susu et al. "Engineering of Bacillus amyloliquefaciens α-Amylase for Improved Catalytic Efficiency by Error-Prone PCR" . | STARCH-STARKE 75 . 11-12 (2023) . |
| APA | Yuan, Susu , Li, Renkuan , Lin, Biyu , Yan, Renxiang , Ye, Xiuyun . Engineering of Bacillus amyloliquefaciens α-Amylase for Improved Catalytic Efficiency by Error-Prone PCR . | STARCH-STARKE , 2023 , 75 (11-12) . |
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Abstract ：
The medium-temperature alpha-amylase of Bacillus amyloliquefaciens is widely used in the food and washing process. Enhancing the thermostability of alpha-amylases and investigating the mechanism of stability are important for enzyme industry development. The optimal temperature and pH of the wild-type BAA and mutant MuBAA (D28E/V118A/S187D/K370 N) were all 60 degrees C and 6.0, respectively. The mutant MuBAA showed better thermostability at 50 degrees C and 60 degrees C, with a specific activity of 206.61 U/mg, which was 99.1% greater than that of the wild-type. By analyzing predicted structures, the improving thermostability of the mutant MuBAA was mainly related to enhanced stabilization of a loop region in domain B via more calcium-binding sites and intramolecular interactions around Asp187. Furthermore, additional intramolecular interactions around sites 28 and 370 in domain A were also beneficial for improving thermostability. Additionally, the decrease of steric hindrance at the active cavity increased the specific activity of the mutant MuBAA. Improving the thermostability of BAA has theoretical refer-ence values for the modification of alpha-amylases. (c) 2023 Elsevier Inc. All rights reserved.
Keyword ：
Alpha-amylase Alpha-amylase Bacillus amyloliquefaciens Bacillus amyloliquefaciens Thermostability Thermostability Three-dimensional model Three-dimensional model
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| GB/T 7714 | Yuan, Susu , Yan, Renxiang , Lin, Biyu et al. Improving thermostability of Bacillus amyloliquefaciens alpha-amylase by multipoint mutations [J]. | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS , 2023 , 653 : 69-75 . |
| MLA | Yuan, Susu et al. "Improving thermostability of Bacillus amyloliquefaciens alpha-amylase by multipoint mutations" . | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 653 (2023) : 69-75 . |
| APA | Yuan, Susu , Yan, Renxiang , Lin, Biyu , Li, Renkuan , Ye, Xiuyun . Improving thermostability of Bacillus amyloliquefaciens alpha-amylase by multipoint mutations . | BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS , 2023 , 653 , 69-75 . |
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Abstract ：
蛋白质酶是生物体内最重要的生物分子之一.对酶的功能进行系统研究具有重要的科学研究价值和工业应用意义,近年来,以计算机技术为基础的酶功能预测的方法不断发展与完善.基于此背景,本文总结了基于计算方法的酶功能分析与预测的主要方法,包括酶结合位点、分子对接、动力学模拟以及分子设计等内容.同时,本文也对相应的发展趋势进行讨论和展望.
Keyword ：
功能分析 功能分析 生物信息学 生物信息学 酶 酶
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| GB/T 7714 | 苏绍玉 , 卢芷琳 , 史智凌 et al. 蛋白质酶功能分析和预测方法的进展和前瞻 [J]. | 生物信息学 , 2022 , 20 (4) : 227-234 . |
| MLA | 苏绍玉 et al. "蛋白质酶功能分析和预测方法的进展和前瞻" . | 生物信息学 20 . 4 (2022) : 227-234 . |
| APA | 苏绍玉 , 卢芷琳 , 史智凌 , 叶秀云 , 鄢仁祥 . 蛋白质酶功能分析和预测方法的进展和前瞻 . | 生物信息学 , 2022 , 20 (4) , 227-234 . |
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Ubiquitination is a very important protein post-translational modification in humans, which is closely related to many human diseases such as cancers. Although some methods have been elegantly proposed to predict human ubiquitination sites, the accuracy of these methods is generally not very satisfactory. In order to improve the prediction accuracy of human ubiquitination sites, we propose a new ensemble method HUbipPred, which takes the binary encoding and physicochemical properties of amino acids as training features, and integrates two intensively trained convolutional neural networks and two recurrent neural networks to build the model. Finally, HUbiPred achieves AUC values of 0.852 and 0.844 in five-fold cross-validation and independent tests, respectively, which greatly improves the prediction accuracy compared to previous predictors. We also analyze the physicochemical properties of amino acids around ubiquitination sites, study the important roles of architectures (i.e., convolution, long short-term memory (LSTM) and fully connected hidden layers) in the networks for prediction performance, and also predict potential ubiquitination sites in humans using HUbiPred. The training and test datasets, predicted human ubiquitination sites, and source codes of HUbiPred are publicly available at https://github.com/amituofo-xf/HUbiPred.
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| GB/T 7714 | Wang, Xiaofeng , Yan, Renxiang , Wang, Yongji . Computational identification of human ubiquitination sites using convolutional and recurrent neural networks [J]. | MOLECULAR OMICS , 2021 , 17 (6) : 948-955 . |
| MLA | Wang, Xiaofeng et al. "Computational identification of human ubiquitination sites using convolutional and recurrent neural networks" . | MOLECULAR OMICS 17 . 6 (2021) : 948-955 . |
| APA | Wang, Xiaofeng , Yan, Renxiang , Wang, Yongji . Computational identification of human ubiquitination sites using convolutional and recurrent neural networks . | MOLECULAR OMICS , 2021 , 17 (6) , 948-955 . |
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Abstract ：
As an important posttranslational protein modification, ubiquitination plays critical roles in plant physiology, including plant growth and development, biotic and abiotic stress, metabolism, and so on. A lot of ubiquitination site prediction models have been developed for human, mouse and yeast. However, there are few models to predict ubiquitination sites for the plant Arabidopsis thaliana. Based on this context, we proposed two convolutional neural network (CNN) based models for predicting ubiquitination sites in A. thaliana. The two models reach AUC (area under the ROC curve) values of 0.924 and 0.913 respectively in five-fold cross-validation, and 0.921 and 0.914 respectively in independent test, which outperform other models and demonstrate the competitive edge of them. We in-depth analyze the amino acid physicochemical properties in the neighboring sequence regions of the ubiquitination sites, and study the influence of the CNN structure to the prediction performance. Potential ubiquitination sites in the global Arbidopsis proteome are predicted using the two CNN models. To facilitate the community, the source code, training and test dataset, predicted ubiquitination sites in the Arbidopsis proteome are available at GitHub () for interest users.
Keyword ：
Arabidopsis thaliana Arabidopsis thaliana Convolutional neural network Convolutional neural network Prediction Prediction Ubiquitination site Ubiquitination site
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| GB/T 7714 | Wang, Xiaofeng , Yan, Renxiang , Chen, Yong-Zi et al. Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks [J]. | PLANT MOLECULAR BIOLOGY , 2021 , 105 (6) : 601-610 . |
| MLA | Wang, Xiaofeng et al. "Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks" . | PLANT MOLECULAR BIOLOGY 105 . 6 (2021) : 601-610 . |
| APA | Wang, Xiaofeng , Yan, Renxiang , Chen, Yong-Zi , Wang, Yongji . Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks . | PLANT MOLECULAR BIOLOGY , 2021 , 105 (6) , 601-610 . |
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A chitinase gene from Serratia marcescens was cloned and expressed in Escherichia coli BL21(DE3) and the properties of recombinant chitinase rCHI-2 were characterized. The optimum catalytic pH of rCHI-2 was 6.0. It was stable in the pH range of 6.0-9.0 and could maintain more than 90% of its relative enzyme activity after incubation at 37 degrees C for 1 h. The optimum catalytic temperature of the enzyme was 55 degrees C and 85% of enzyme activity was remained after incubation at 45 degrees C for 1 h. The activation energy of the thermal inactivation of the enzyme was 10.9 kJ/mol and the Michaelis-Menten constant was 3.2 g/L. The purified rCHI-2 was found to be highly stable at 45 degrees C with half-life (t(1/2)) of 289 min and thermodynamic parameters Delta H*, Delta G* and Delta S* revealed high affinity of rCHI-2 for chitin. Hg2+ was found to be able to inhibit the enzyme activity reversibly, while IC50 and inhibition constant of Hg2+ on the enzyme were 34.8 mu mol/L and 44.6 mu mol/L, respectively. Moreover, rCHI-2 could specifically hydrolyze colloidal chitin into GlcNAc(2) as the major product.
Keyword ：
Characterization Characterization Chitinase Chitinase Chitin oligosaccharides Chitin oligosaccharides Thermodynamics Thermodynamics
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| GB/T 7714 | Li, Jincheng , Zheng, Jiamin , Liang, Yanhui et al. Expression and characterization of a chitinase from Serratia marcescens [J]. | PROTEIN EXPRESSION AND PURIFICATION , 2020 , 171 . |
| MLA | Li, Jincheng et al. "Expression and characterization of a chitinase from Serratia marcescens" . | PROTEIN EXPRESSION AND PURIFICATION 171 (2020) . |
| APA | Li, Jincheng , Zheng, Jiamin , Liang, Yanhui , Yan, Renxiang , Xu, Xinqi , Lin, Juan . Expression and characterization of a chitinase from Serratia marcescens . | PROTEIN EXPRESSION AND PURIFICATION , 2020 , 171 . |
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Abstract ：
海量数据时代考察文本分析技术在生物信息学领域的应用具有重要的理论和现实价值。本文讨论了文本分析在蛋白质计算分析中的几个应用实例以及核心技术内容。文本分析技术应用于生物信息学领域可发挥引领和导向作用,在生物信息学中的应用又进一步促进了文本分析技术的发展。文本分析技术虽然广泛在生物信息学中应用,但是其发展仍然有需要尚待解决的几个问题,本文对此也进行了讨论。
Keyword ：
人工智能 人工智能 大数据 大数据 文本分析 文本分析 生物信息学 生物信息学 科技情报 科技情报 蛋白质计算 蛋白质计算
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| GB/T 7714 | 苏绍玉 , 徐婧 , 鄢仁祥 . 文本分析技术在蛋白质生物信息学中应用的案例综述 [J]. | 生物信息学 , 2020 , 18 (04) : 215-222 . |
| MLA | 苏绍玉 et al. "文本分析技术在蛋白质生物信息学中应用的案例综述" . | 生物信息学 18 . 04 (2020) : 215-222 . |
| APA | 苏绍玉 , 徐婧 , 鄢仁祥 . 文本分析技术在蛋白质生物信息学中应用的案例综述 . | 生物信息学 , 2020 , 18 (04) , 215-222 . |
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Due to rising development costs and stagnant product outputs of traditional drug discovery methods, drug repositioning, which discovers new indications for existing drugs, has attracted increasing interest. Computational drug repositioning can integrate prioritization information and accelerate time lines even further. However, most existing methods for predicting drug repositioning have low precisions. The present article proposed a new method named DDAPRED () for drug repositioning prediction. The method integrated multiple sources of drug similarity and disease similarity information, and it used the regularized logistic matrix decomposition method to significantly improve the prediction performance. In 5-fold cross-validation, the areas under the receiver operating characteristic curve (AUROC) and the precision-recall curve (AUPRC) of DDAPRED reached 0.932 and 0.438, respectively, exceeding other methods. The present study also analyzed the parameters influencing the model performance and the effect of different drug similarity information in-depth, and it verified the treatment relationship of the top 50 predictions with unknown relationships in the training set, further demonstrating the practicability of our method.
Keyword ：
Drug-disease association Drug-disease association Drug repositioning Drug repositioning Logistic matrix factorization Logistic matrix factorization Prediction Prediction
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| GB/T 7714 | Wang, Xiaofeng , Yan, Renxiang . DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization [J]. | JOURNAL OF MOLECULAR MODELING , 2020 , 26 (3) . |
| MLA | Wang, Xiaofeng et al. "DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization" . | JOURNAL OF MOLECULAR MODELING 26 . 3 (2020) . |
| APA | Wang, Xiaofeng , Yan, Renxiang . DDAPRED: a computational method for predicting drug repositioning using regularized logistic matrix factorization . | JOURNAL OF MOLECULAR MODELING , 2020 , 26 (3) . |
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Abstract ：
同一组织中的细胞往往具有类似的结构和功能,然而通过对单个细胞进行测序分析后,发现每个细胞都具有一定异质性.单细胞全基因组扩增技术是进行单细胞测序的前提,该技术可用于揭示单细胞基因组结构差异,同时在肿瘤研究、发育生物学、微生物学等研究中发挥重要作用,并成为生命科学研究技术的热点之一.单细胞全基因组扩增技术的难点在于单细胞的分离和全基因组的扩增.本文介绍了单细胞全基因组扩增技术中常用的单细胞分离技术和单细胞全基因组扩增技术,并对各技术间的优缺点进行比较,同时着重讨论该技术在肿瘤研究、发育生物学和微生物学研究中的应用.
Keyword ：
单细胞全基因组扩增技术 单细胞全基因组扩增技术 单细胞分离技术 单细胞分离技术 应用 应用 比较 比较
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| GB/T 7714 | 徐晓丽 , 吴凌娟 , 鄢仁祥 . 单细胞全基因组扩增技术与应用 [J]. | 生物化学与生物物理进展 , 2019 , 46 (04) : 342-352 . |
| MLA | 徐晓丽 et al. "单细胞全基因组扩增技术与应用" . | 生物化学与生物物理进展 46 . 04 (2019) : 342-352 . |
| APA | 徐晓丽 , 吴凌娟 , 鄢仁祥 . 单细胞全基因组扩增技术与应用 . | 生物化学与生物物理进展 , 2019 , 46 (04) , 342-352 . |
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