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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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药物分子计算机辅助设计是一种在计算机或者理论上通过构建具有一定潜在药理活性的新化学实体的分子模拟方法。近十几年来，高通量组学技术的快速发展为生物和化学药物分子设计提供了良好的数据支撑和研究契机。另外，现代社会对生物制药合理性以及作用机理理解的要求越来越高，行业普遍要求药物需要有高效、无毒或者低毒以及靶向性强等特点。随着越来越多与药物靶点相关的蛋白质结构通过实验方法解析出来，基于蛋白质受体的药物分子设计方法可行性进一步提高，其方法也变得越来越重要。基于蛋白质受体的药物分子设计方法，一般是以蛋白质以及配体的三维结构出发进行分析，这让药物分子先导物的发现更加理性化。随着相关实验数据的积累以及深度学习等算法的发展，从而可以进行更加科学的药物分子设计，这在一定程度上加快了新药研发的进程，并更有利于探索相应的分子机理。本文对基于蛋白质受体的药物分子设计方法的常用策略进行系统的回顾、总结和展望。
Keyword ：
分子模拟 分子模拟 蛋白质受体 蛋白质受体 计算机辅助药物分子设计 计算机辅助药物分子设计
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| GB/T 7714 | 袁素素 , 叶秀云 , 鄢仁祥 . 基于蛋白质受体的药物分子计算机辅助设计策略 [J]. | 生物信息学 , 2024 , 22 (03) : 159-173 . |
| MLA | 袁素素 等. "基于蛋白质受体的药物分子计算机辅助设计策略" . | 生物信息学 22 . 03 (2024) : 159-173 . |
| APA | 袁素素 , 叶秀云 , 鄢仁祥 . 基于蛋白质受体的药物分子计算机辅助设计策略 . | 生物信息学 , 2024 , 22 (03) , 159-173 . |
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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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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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酶分子的生物学功能很大程度上是由其三维空间结构和所处溶剂环境共同决定的。因此，优化酶分子的结构性质以及探索其性质最优的溶剂环境是改善酶分子功能以及进行理性设计的一个可行途径。从实际应用的角度来看，分子设计方法可以为酶工程提供一种有效的解决方案。目前，酶分子设计有两个重要的研究方向，包括提高酶分子的催化活力和优化其稳定性。同时，对酶分子设计方法的研究也有助于对蛋白质生物学机理的探索。在近些年的学术界酶分子设计案例中，生物信息学方法得到广泛的应用。本文系统地总结基于生物信息学的酶分子设计方法的背景、策略和一些经典案例。
Keyword ：
酶分子设计 酶分子设计 酶功能分析 酶功能分析 酶性质优化 酶性质优化
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| GB/T 7714 | 苏绍玉 , 叶秀云 , 鄢仁祥 . 酶分子设计常用策略和进展 [J]. | 生物信息学 , 2023 , 21 (04) : 233-246 . |
| MLA | 苏绍玉 et al. "酶分子设计常用策略和进展" . | 生物信息学 21 . 04 (2023) : 233-246 . |
| APA | 苏绍玉 , 叶秀云 , 鄢仁祥 . 酶分子设计常用策略和进展 . | 生物信息学 , 2023 , 21 (04) , 233-246 . |
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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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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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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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