蛋白质泛素化修饰的生物信息学研究进展

泛素-蛋白酶体系统(Ubiquitin-proteasome system, UPS)介导了真核生物80%~85%的蛋白质降解, 该蛋白质降解途径具有依赖ATP、高效、高度选择性的特点。除参与蛋白质降解之外, 泛素化修饰还可以直接影响蛋白质的活性和定位。由于泛素化修饰底物蛋白在细胞中的广泛存在, 泛素化修饰可以调控包括细胞周期、细胞凋亡、转录调控、DNA损伤修复以及免疫应答等在内的多种细胞活动。近年来, 泛素-蛋白酶体系统相关的蛋白质组学数据不断产出, 有效地管理、组织并合理分析这些数据显得尤为必要。文章综述了当前世界范围内针对蛋白质泛素化修饰展开的生物信息学研究, 总结了前人的工作结果, 包括UPS相关蛋白质数据的收录、泛素化修饰网络的构建和分析、泛素化修饰位点的预测及泛素化修饰motif的研究等方面内容, 并对该领域未来的发展方向进行了讨论。     

蛋白质泛素化系统

泛素化是单个或多个泛素在泛素激活酶,泛素结合酶及泛素蛋白质连接酶的作用下共价修饰底物蛋白质的过程,近年来的研究发现,许多含环指的蛋白质本身是蛋白质泛素连接酶,或是多亚基连接酶中的重要成分。由于细胞内可表达200以上的环指蛋白,并且多亚基连接酶可利用同一环指蛋白但不同的底物识别蛋白。这些研究极大地丰富了对泛素化系统酶的认识,也使进一步调节和干预连接酶与底物的相互作用成为可能,新近的研究还发现,泛素化不仅可导致蛋白质的降解,还可直接影响蛋白质的活性和细胞内定位,是调节细胞内蛋白质功能和水平的主要机制之一。     

线性泛素化研究进展

线性泛素化是一种新型泛素化修饰方式,不同于赖氨酸介导的多聚泛素化,其主要通过泛素分子的首尾相连对蛋白质进行翻译后修饰,以线性泛素化复合体(LUBAC)作为E3连接酶,参与细胞的抗凋亡、抗病毒作用,以及炎症反应等细胞生命活动.该文主要介绍了线性泛素化的组成、对蛋白质进行修饰的主要方式,其参与调控的体内生理活动信号通路,并...     

泛素、泛素链和蛋白质泛素化研究进展

蛋白质泛素化是以泛素单体和泛素链作为信号分子,共价修饰细胞内其他蛋白质的一种翻译后修饰形式。不同蛋白质底物、同一底物的不同氨基酸修饰位点以及同一位点上泛素链连接方式的不同均可导致细胞效应的差异。蛋白质泛素化在真核细胞内广泛存在,除了介导蛋白质的26S蛋白酶体降解途径之外,还广泛参与了基因转录、蛋白质翻译、信号传导、细胞周期控制以及生长发育等几乎所有的生命活动过程。泛素链的形成及其修饰过程的任何失调均可导致生物体内环境的紊乱,从而产生严重的疾病。文中结合实验室研究,综述了泛素的发现历史、基因特点、晶体结构,特别是泛素链的组装过程、结构、功能以及与人类相关疾病关系的新进展,可为这些疾病的治疗靶点和药物靶标的研究提供思路。     

非经典泛素化的研究进展

泛素化是真核生物最普遍最重要的翻译后修饰之一,控制基因转录表达、细胞生长死亡、分子运输、代谢、发育和免疫反应等大多数生理过程。经典泛素系统的通路和机制越来越明晰,同时非经典的泛素化也逐渐被发现。本文将对经典泛素系统进行简单回顾,并且对非蛋白底物泛素化、非赖氨酸位点泛素化、非经典E3泛素连接酶等最新非经典泛素化进行阐述。     

组蛋白的泛素化与去泛素化修饰

泛素在真核生物体内广泛存在,泛素化修饰是转录后的修饰方式之一;组蛋白是染色质的主要成分之一,与基因的表达有密切关系。组蛋白的泛素化修饰与经典的蛋白质的泛素调节途径不同,不会导致蛋白质的降解,但是能够招募核小体到染色体、参与X染色体的失活、影响组蛋白的甲基化和基因的转录。组蛋白的去泛素化修饰同样与染色质的结构及基因表达密切相关。组蛋白的泛素化和磷酸化、乙酰化、甲基化修饰之间还存在协同和级联效应。     

泛素化/去泛素化系统的生物化学和生物学功能

泛素是生物体内一种非常重要的小分子蛋白,在多种信号通路中都起重要的调节作用。该文对近两三年国际上对泛素化/去泛素化系统的研究进展做了简单的概述,并对未来的发展方向进行了预测。     

蛋白质泛素化介导的植物低温胁迫反应

低温胁迫限制植物的生长发育及其地理分布.不同物种在低温胁迫反应过程中发生了重大的转录组重新编程,许多蛋白质被认为是这种适应性反应的重要因子.泛素化是一种翻译后修饰,能够调控蛋白的丰度、活性、亚细胞区隔和转运,并参与低温应答过程.E3是泛素-蛋白酶体系统的主要组成部分,识别目标蛋白,并将泛素从E2上转移到目标蛋白上.由于...     

泛素化介导的非蛋白质降解功能

泛素因标记被26 S蛋白酶体降解的蛋白质而著名．然而近几年发现,泛素作用远不止此,不仅具有参与蛋白质降解这一重要“传统作用”,还起着比先前想象更多变的、更精美的细胞调控作用,是非常重要的细胞过程的多层面调节因子,具有许多重要的非蛋白质降解功能,包括DNA损伤修复、DNA复制、信号传导、转录调节、膜运输、胞吞、蛋白激酶活化、染色质重塑和病毒芽殖．这些功能涉及多聚泛素化和单泛素化及多泛素化．因此,泛素化异常可能涉及疾病的发生和发展．对这些功能的了解可以拓展人们对泛素的认识,有助于对多种细胞过程的深入理解,也有助于相关新药的研发．     

组蛋白泛素化与去泛素化对染色质和基因表达的研究进展

组蛋白或转录因子或辅助因子进行泛素化和去泛素化,能够介导某些生理和病理过程。泛素化和去泛素化的动态平衡确保染色质处于健康的稳定状态。组蛋白泛素化酶和去泛素化酶通过识别DNA损伤位点、传导信号和招募修复因子等方式参与维持染色质稳态。组蛋白泛素化修饰和去泛素化修饰通过抑制(多数)或促进(少数)基因转录,从而影响基因表达。本综述主要关注组蛋白泛素化修饰和去泛素化修饰与染色质稳态和基因转录的关系,探讨这些过程在发育调控和在某些疾病中的作用,为相关疾病的治疗提供理论依据。     

Prediction of functionally important sites from protein sequences using sparse kernel least squares classifiers

Identification of functionally important sites (FIS) in proteins is a critical problem and can have profound importance where protein structural information is limited. Machine learning techniques have been very useful in successful classification of many important biological problems. In this paper, we adopt the sparse kernel least squares classifiers (SKLSC) approach for classification and/or prediction of FIS using protein sequence derived features. The SKLSC algorithm was applied to 5435 FIS that have been extracted from 312 reliable alignments for a wide range of protein families. We obtained 68.28% sensitivity and 68.66% specificity for training dataset and 65.34% sensitivity and 66.88% specificity for testing dataset. Further, large scale benchmarking study using alignments of 101 protein families containing 1899 FIS showed that our method achieved an average ∼70% sensitivity in predicting different types of FIS, such as active sites, metal, ligand or protein binding sites. Our findings also indicate that active sites and metal binding sites are comparably easier to predict compared to the ligand and protein binding sites. Despite moderate success, our results suggest the usefulness and potential of SKLSC approach in prediction of FIS using only protein sequence derived information.     

DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning

Protein nitration and nitrosylation are essential post-translational modifications(PTMs)involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases.Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specificscoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65 for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively,demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%à42% improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http://deepnitro.renlab.org.     

基于机器学习的药物-靶标相互作用预测*

近年来,随着计算机硬件、软件工具和数据丰度的不断突破,以机器学习为代表的人工智能技术在生物、基础医学和药学等领域的应用不断拓展和融合,极大地推动了这些领域的发展,尤其是药物研发领域的变革。其中,药物-靶标相互作用(drug-target interactions, DTI)的识别是药物研发领域中的重要难题和人工智能技术交叉融合的热门方向,研究人员在DTI预测方面做了大量的工作,构建了许多重要的数据库,开发或拓展了各类机器学习算法和工具软件。对基于机器学习的DTI预测的基本流程进行了介绍,并对利用机器学习预测DTI的研究进行了回顾,同时对不同的机器学习方法运用于DTI预测的优缺点进行了简单总结,以期对开发更加有效的预测算法和DTI预测的发展提供帮助。     

Prediction of S-Glutathionylation Sites Based on Protein Sequences

S-glutathionylation, the reversible formation of mixed disulfides between glutathione(GSH) and cysteine residues in proteins, is a specific form of post-translational modification that plays important roles in various biological processes, including signal transduction, redox homeostasis, and metabolism inside cells. Experimentally identifying S-glutathionylation sites is labor-intensive and time consuming, whereas bioinformatics methods provide an alternative way to this problem by predicting S-glutathionylation sites in silico. The bioinformatics approaches give not only candidate sites for further experimental verification but also bio-chemical insights into the mechanism of S-glutathionylation. In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data. Promising results are obtained by our method with an AUC (area under ROC curve) score of 0.879 in 5-fold cross-validation, which demonstrates the predictive power of our proposed method. The datasets used in this work are available at http://csb.shu.edu.cn/SGDB.     

Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches

Longevity is one of the most basic and one of the most essential properties of all living organisms. Identification of genes that regulate longevity would increase understanding of the mechanisms of aging, so as to help facilitate anti-aging intervention and extend the life span. In this study, based on the network features and the biochemical/physicochemical features of the deletion network and deletion genes, as well as their functional features, a two-layer model was developed for predicting the deletion effects on yeast longevity. The first stage of our prediction approach was to identify whether the deletion of one gene would change the life span of yeast; if it did, the second stage of our procedure would automatically proceed to predict whether the deletion of one gene would increase or decrease the life span. It was observed by analyzing the predicted results that the functional features (such as mitochondrial function and chromatin silencing), the network features (such as the edge density and edge weight density of the deletion network), and the local centrality of deletion gene, would have important impact for predicting the deletion effects on longevity. It is anticipated that our model may become a useful tool for studying longevity from the angle of genes and networks. Moreover, it has not escaped our notice that, after some modification, the current model can also be used to study many other phenotype prediction problems from the angle of systems biology.     

Classification Performance-Based Feature Selection Algorithm for Machine Learning: P-Score

Feature selection algorithms are the cornerstone of machine learning. By increasing the properties of the samples and samples, the feature selection algorithm selects the significant features. The general name of the methods that perform this function is the feature selection algorithm. The general purpose of feature selection algorithms is to select the most relevant properties of data classes and to increase the classification performance. Thus, we can select features based on their classification performance. In this study, we have developed a feature selection algorithm based on decision support vectors classification performance. The method can work according to two different selection criteria. We tested the classification performances of the features selected with P-Score with three different classifiers. Besides, we assessed P-Score performance with 13 feature selection algorithms in the literature. According to the results of the study, the P-Score feature selection algorithm has been determined as a method which can be used in the field of machine learning.     

Sequence Conservation in the Prediction of Catalytic Sites

Predicting catalytic sites of a given enzyme is an important open problem of Bioinformatics. Recently, many machine learning-based methods have been developed which have the advantage that they can account for many sequential or structural features. We found that although many kinds of features are incorporated, protein sequence conservation is the main part of information they used and should play an important role in the future. So we tested several conservation features in their ability to predict catalytic sites by using the Support Vector Machine classifier. Our results suggest that position specific scoring matrix performs better than other features and incorporating conservation information of sequentially adjacent sites is more effective than that of structurally adjacent ones. Moreover, although conservation information is effective in predicting catalytic sites, it is a difficult problem to optimize the combination of conservation features and other ones.     

Feature Extraction and Classification of Heart Murmurs Based on Acoustic Qualities

Background and objectiveHeart murmur characterization is a crucial part of cardiac auscultation for determining the potential etiology and severity of heart diseases. One such helpful murmur characterization is the sonic qualities, which reflect both structural and hemodynamical states of the heart. Therefore, the objective is to develop a machine learning based solution for classifying murmur qualities.MethodsFour medically defined murmur qualities, namely the musical quality, blowing-like quality, coarse quality, and soft quality were examined. Feature was extracted from heart murmurs signals in their time domain, frequency domain, time-frequency domain, and phase space domain. Sequential forward floating selection (SFFS) was implemented along with three classifiers, including k-nearest neighbor (KNN), Naïve-Bayes (NB), and linear support vector machine (SVM).ResultsIt was found that multi-domain features are suited for better classification results and linear SVM was able to achieve a better balance between performance and the size of feature subsets among tested classifiers. Using the derived features, classification accuracies of 86%, 91%, 90%, and 84% were achieved for musical quality, blowing-like quality, coarse quality, and soft quality classifications respectively.ConclusionsThe study demonstrated that it is possible to effectively characterize heart murmur through its diagnostic characteristics instead of drawing direct conclusions, which is helpful for retaining versatility and generality found in the conventional cardiac auscultation.     

Prediction of Interacting Protein Pairs from Sequence Using a Bayesian Method

With the development of bioinformatics, more and more protein sequence information has become available. Meanwhile, the number of known protein–protein interactions (PPIs) is still very limited. In this article, we propose a new method for predicting interacting protein pairs using a Bayesian method based on a new feature representation. We trained our model using data on 6,459 PPI pairs from the yeast Saccharomyces cerevisiae core subset. Using six species of DIP database, our model demonstrates an average prediction accuracy of 93.67%. The result showed that our method is superior to other methods in both computing time and prediction accuracy.