蛋白质亚细胞定位的生物信息学研究

细胞中蛋白质合成后被转运到特定的细胞器中,只有转运到正确的部位才能参与细胞的各种生命活动,如果定位发生偏差,将会对细胞功能甚至生命产生重大影响.蛋白质的亚细胞定位是蛋白质功能研究的重要方面,也是生物信息学中的热点问题,数据库的构建和亚细胞定位分析及预测加速了蛋白质结构和功能的研究.     

蛋白质亚细胞定位预测中的序列编码技术

蛋白质序列的编码是亚细胞定位预测问题中的关键技术之一。该文较为详细地介绍了目前已有的蛋白质序列编码算法;并指出了序列编码中存在的一些问题及可能的发展方向。     

基于氨基酸约化和统计特征的蛋白质亚细胞定位预测

蛋白质亚细胞定位预测对蛋白质的功能、相互作用及调控机制的研究具有重要意义。本文基于物化性质和结构性质对氨基酸的约化,描述序列局部和全局信息的"组成"、"转换"和"分布"特征,并利用氨基酸亲疏水性的数值统计特征,提出了一种新的蛋白质特征表示方法(NSBH)。分别使用三种分类器KNN、SVM及BP神经网络进行蛋白质亚细胞定位预测,比较了几种方法和特征融合方法的预测结果,显示融合特征表示及结合SVM分类器时能够达到更好的预测准确率。同时,还详细讨论了不同参数对实验结果的影响,具体的实验及比较结果显示了该方法的有效性。     

基于多层次稀疏编码预测蛋白质亚细胞定位

文中提出了一种简单有效的蛋白质亚细胞区间定位预测方法,为进一步了解蛋白质的功能和性质提供理论基础。运用稀疏编码,结合氨基酸组成信息提取蛋白质序列特征,基于不同字典大小对得到的特征进行多层次池化整合,并送入支持向量机进行分类。经Jackknife检验,在数据集ZD98、CH317和Gram1253上的预测成功率分别达到95.9%、93.4%和94.7%。实验证明基于多层次稀疏编码的分类预测算法能显著提高蛋白质亚细胞区间定位的预测精度。     

蛋白质体积对其亚细胞定位的影响

蛋白质亚细胞定位信息对深入研究蛋白质的细胞生物学功能十分重要.通过Helix Systems在线计算程序和Vor计算程序两种方法讨论了蛋白质的体积对其亚细胞定位的影响,发现定位于细胞外的蛋白质体积显著小于定位于细胞核、细胞膜和细胞质的蛋白体积,证实了体积参数对区分蛋白质的亚细胞定位是有效的.     

蛋白质亚叶绿体和亚线粒体定位预测研究进展

蛋白质合成后被转运到特定的细胞器中,只有转运到正确的部位才能参与细胞的各种生命活动,有效地发挥功能,因此蛋白质的功能与其亚细胞定位有着密切的联系,通过确定蛋白质在细胞中的位置可以获取蛋白质功能和结构的信息。在近二十年中,蛋白质亚细胞定位预测算法研究已经取得很大的成绩,在此基础上,蛋白质在细胞器内亚结构的定位预测研究,如对蛋白质亚线粒体和亚叶绿体定位的研究成为更深层次的问题,本文简要介绍国内外在蛋白质亚叶绿体和亚线粒体定位预测方面的研究进展。     

酵母蛋白质相互作用与基因表达谱和亚细胞定位的相关性

研究酵母（yeast）蛋白质相互作用与基因表达谱和蛋白质亚细胞定位的关系.首先,构建了蛋白质相互作用正样本集、负样本集、随机组对负样本集和混合样本集.然后,对于4个数据集中的所有蛋白质对,通过比较它们的基于距离的基因共表达的分布以及它们中具有已知亚细胞定位的蛋白质对的共定位出现率,实现了这些高通量数据的交叉量化分析.结果揭示,与非相互作用蛋白质对相比,相互作用蛋白质对的基因表达谱具有较高的相似性;相互作用蛋白质对更倾向于具有相同的亚细胞定位.结果还揭示出这些蛋白质特征相关的总体趋势.     

用离散增量结合支持向量机方法预测蛋白质亚细胞定位

对未知蛋白的功能注释是蛋白质组学的主要目标。一个关键的注释是蛋白质亚细胞定位的预测。本文应用离散增量结合支持向量机(ID_SVM)的方法,对阳性革兰氏细菌蛋白的5类亚细胞定位点进行预测。在独立检验下,其总体预测成功率为89.66%。结果发现ID_SVM算法对预测的成功率有很大改进。     

蛋白质相互作用调控蛋白质核转运及其功能

蛋白质的核转运是真核生物细胞内发生的重要过程之一,是一大群蛋白质发挥其功能的前提,与细胞正常功能的维持密切相关。蛋白质的核运输通常采用核受体介导的方式进行。此过程非常复杂,需要多种蛋白质的参与,涉及到大量的蛋白质相互作用。本文将综合近年来本领域取得的进展,就蛋白质相互作用参与蛋白质核转运来调节蛋白质的亚细胞定位,进一步在多方面影响细胞以及生物体生理功能的变化进行阐述。     

植物蛋白质亚细胞定位相关研究概述

蛋白质在植物细胞内的定位是了解蛋白质功能、基因调控和蛋白质-蛋白质相互作用的关键。近年来随着各种蛋白质亚细胞定位方法的快速发展和技术的不断提升,蛋白质亚细胞定位实现了高通量、活体动态研究。本文总结了植物蛋白质亚细胞定位的常用技术,以及常用细胞器特异性标记的研究进展,并对此领域研究的发展前景做出了展望。     

真核蛋白质的亚细胞位点预测研究进展

研究真核蛋白质的亚细胞位点是了解真核蛋白质功能,深入研究蛋白质相关信号通路内在机制的基础。同时,可以为了解 疾病发病机制及为新药研发提供帮助。因此,研究真核蛋白质的亚细胞位点意义十分重大。随着基因组测序的完成,真核蛋白质 序列信息增长迅速,为真核蛋白质亚细胞位点的研究提出了更多的挑战。传统的实验法难以满足蛋白质信息量迅速增长的需求。 而采用生物信息学手段处理大规模数据的计算预测方法,可在较短时间内获得大量真核蛋白质亚细胞位点信息,弥补了实验法 的不足。因此,运用计算预测法预测真核蛋白质的亚细胞位点成为生物信息学领域的研究热点之一。本文主要从提取真核蛋白质 的特征信息、计算预测方法及预测效果的评价三个方面,介绍近年来真核蛋白质亚细胞位点预测的研究进展。     

Protein localization as a principal feature of the etiology and comorbidity of genetic diseases

Proteins targeting the same subcellular localization tend to participate in mutual protein–protein interactions (PPIs) and are often functionally associated. Here, we investigated the relationship between disease‐associated proteins and their subcellular localizations, based on the assumption that protein pairs associated with phenotypically similar diseases are more likely to be connected via subcellular localization. The spatial constraints from subcellular localization significantly strengthened the disease associations of the proteins connected by subcellular localizations. In particular, certain disease types were more prevalent in specific subcellular localizations. We analyzed the enrichment of disease phenotypes within subcellular localizations, and found that there exists a significant correlation between disease classes and subcellular localizations. Furthermore, we found that two diseases displayed high comorbidity when disease‐associated proteins were connected via subcellular localization. We newly explained 7584 disease pairs by using the context of protein subcellular localization, which had not been identified using shared genes or PPIs only. Our result establishes a direct correlation between protein subcellular localization and disease association, and helps to understand the mechanism of human disease progression.     

Predicting subcellular localization with AdaBoost Learner

Protein subcellular localization, which tells where a protein resides in a cell, is an important characteristic of a protein, and relates closely to the function of proteins. The prediction of their subcellular localization plays an important role in the prediction of protein function, genome annotation and drug design. Therefore, it is an important and challenging role to predict subcellular localization using bio-informatics approach. In this paper, a robust predictor, AdaBoost Learner is introduced to predict protein subcellular localization based on its amino acid composition. Jackknife cross-validation and independent dataset test were used to demonstrate that Adaboost is a robust and efficient model in predicting protein subcellular localization. As a result, the correct prediction rates were 74.98% and 80.12% for the Jackknife test and independent dataset test respectively, which are higher than using other existing predictors. An online server for predicting subcellular localization of proteins based on AdaBoost classifier was available on http://chemdata.shu. edu.cn/sl12.     

Definitive localization of intracellular proteins: Novel approach using CRISPR-Cas9 genome editing,with glucose 6-phosphate dehydrogenase as a model

Studies to determine subcellular localization and translocation of proteins are important because subcellular localization of proteins affects every aspect of cellular function. Such studies frequently utilize mutagenesis to alter amino acid sequences hypothesized to constitute subcellular localization signals. These studies often utilize fluorescent protein tags to facilitate live cell imaging. These methods are excellent for studies of monomeric proteins, but for multimeric proteins, they are unable to rule out artifacts from native protein subunits already present in the cells. That is, native monomers might direct the localization of fluorescent proteins with their localization signals obliterated. We have developed a method for ruling out such artifacts, and we use glucose 6-phosphate dehydrogenase (G6PD) as a model to demonstrate the method's utility. Because G6PD is capable of homodimerization, we employed a novel approach to remove interference from native G6PD. We produced a G6PD knockout somatic (hepatic) cell line using CRISPR-Cas9 mediated genome engineering. Transfection of G6PD knockout cells with G6PD fluorescent mutant proteins demonstrated that the major subcellular localization sequences of G6PD are within the N-terminal portion of the protein. This approach sets a new gold standard for similar studies of subcellular localization signals in all homodimerization-capable proteins.     

PLPD: reliable protein localization prediction from imbalanced and overlapped datasets

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003).     

Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods

The subcellular localization of a protein can provide important information about its function within the cell. As eukaryotic cells and particularly mammalian cells are characterized by a high degree of compartmentalization, most protein activities can be assigned to particular cellular compartments. The categorization of proteins by their subcellular localization is therefore one of the essential goals of the functional annotation of the human genome. We previously performed a subcellular localization screen of 52 proteins encoded on human chromosome 21. In the current study, we compared the experimental localization data to the in silico results generated by nine leading software packages with different prediction resolutions. The comparison revealed striking differences between the programs in the accuracy of their subcellular protein localization predictions. Our results strongly suggest that the recently developed predictors utilizing multiple prediction methods tend to provide significantly better performance over purely sequence-based or homology-based predictions.     

Ribonucleoprotein remodeling during RNA localization

Cytoplasmic RNA localization is a means to create polarity by restricting protein expression to a discrete subcellular location. RNA localization is a multistep process that begins with the recognition of cis-acting sequences within the RNA by specific trans-factors, and RNAs are localized in ribonucleoprotein (RNP) complexes that contain both the RNA and numerous protein components. Components of the localization machinery transport the RNP complex, usually in a translationally repressed state, to a distinct subcellular region, resulting in spatially restricted gene expression. Recent efforts to identify both the cis- and trans-factors required for RNA localization have elucidated RNA-protein interactions that are remodeled during localization.     

Multi-label multi-kernel transfer learning for human protein subcellular localization

Recent years have witnessed much progress in computational modelling for protein subcellular localization. However, the existing sequence-based predictive models demonstrate moderate or unsatisfactory performance, and the gene ontology (GO) based models may take the risk of performance overestimation for novel proteins. Furthermore, many human proteins have multiple subcellular locations, which renders the computational modelling more complicated. Up to the present, there are far few researches specialized for predicting the subcellular localization of human proteins that may reside in multiple cellular compartments. In this paper, we propose a multi-label multi-kernel transfer learning model for human protein subcellular localization (MLMK-TLM). MLMK-TLM proposes a multi-label confusion matrix, formally formulates three multi-labelling performance measures and adapts one-against-all multi-class probabilistic outputs to multi-label learning scenario, based on which to further extends our published work GO-TLM (gene ontology based transfer learning model for protein subcellular localization) and MK-TLM (multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization) for multiplex human protein subcellular localization. With the advantages of proper homolog knowledge transfer, comprehensive survey of model performance for novel protein and multi-labelling capability, MLMK-TLM will gain more practical applicability. The experiments on human protein benchmark dataset show that MLMK-TLM significantly outperforms the baseline model and demonstrates good multi-labelling ability for novel human proteins. Some findings (predictions) are validated by the latest Swiss-Prot database. The software can be freely downloaded at http://soft.synu.edu.cn/upload/msy.rar.