蛋白质亚细胞定位

我们知道,对蛋白质在细胞中的特异定位,可以增进我们对该蛋白质以及与该蛋白互作的其他蛋白功能的了解,确定蛋白质位置的一个方法是。在其上加一特异抗体。即对其贴上标签,然后加入能同此抗体特异结合的探针,最后用显微镜确定此探针在细胞中的位置。     

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。     

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.     

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。     

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。     

蛋白质亚细胞定位的识别

根据蛋白质的亚细胞定位,将蛋白质分为12类,用离散量的数学理论,以蛋白质中400个氨基酸二联体数目构成离散源,通过计算离散增量预测蛋白质的亚细胞定位,用Self-consistency和Jackknife两种方法测试均获得较高的预测成功率。结果表明：Self-consistency方法预测成功率为84．5％,Jackknife方法预测成功率为81．1％。     

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.     

基于序列保守性和蛋白质相互作用的真核蛋白质亚细胞定位预测

蛋白质的亚细胞定位是进行蛋白质功能研究的重要信息.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.尝试了将保守序列及蛋白质相互作用数据的编码信息结合传统的氨基酸组成编码,采用支持向量机进行蛋白质亚细胞定位预测,在真核生物中5轮交叉验证精度达到91.8%,得到了显著的提高.     

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。     

Using radial basis function on the general form of Chou's pseudo amino acid composition and PSSM to predict subcellular locations of proteins with both single and multiple sites

Prediction of protein subcellular location is a meaningful task which attracted much attention in recent years. A lot of protein subcellular location predictors which can only deal with the single-location proteins were developed. However, some proteins may belong to two or even more subcellular locations. It is important to develop predictors which will be able to deal with multiplex proteins, because these proteins have extremely useful implication in both basic biological research and drug discovery. Considering the circumstance that the number of methods dealing with multiplex proteins is limited, it is meaningful to explore some new methods which can predict subcellular location of proteins with both single and multiple sites. Different methods of feature extraction and different models of predict algorithms using on different benchmark datasets may receive some general results. In this paper, two different feature extraction methods and two different models of neural networks were performed on three benchmark datasets of different kinds of proteins, i.e. datasets constructed specially for Gram-positive bacterial proteins, plant proteins and virus proteins. These benchmark datasets have different number of location sites. The application result shows that RBF neural network has apparently superiorities against BP neural network on these datasets no matter which type of feature extraction is chosen.     

Signal-anchor sequences are an essential factor for the Golgi-plasma membrane localization of type II membrane proteins

Despite studies of the mechanism underlying the intracellular localization of membrane proteins, the specific mechanisms by which each membrane protein localizes to the endoplasmic reticulum, Golgi apparatus, and plasma membrane in the secretory pathway are unclear. In this study, a discriminant analysis of endoplasmic reticulum, Golgi apparatus and plasma membrane-localized type II membrane proteins was performed using a position-specific scoring matrix derived from the amino acid propensity of the sequences around signal-anchors. The possibility that the sequence around the signal-anchor is a factor for identifying each localization group was evaluated. The discrimination accuracy between the Golgi apparatus and plasma membrane-localized type II membrane proteins was as high as 90%, indicating that, in addition to other factors, the sequence around signal-anchor is an essential component of the selection mechanism for the Golgi and plasma membrane localization. These results may improve the use of membrane proteins for drug delivery and therapeutic applications.     

MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM

Given an uncharacterized protein sequence, how can we identify whether it is a membrane protein or not? If it is, which membrane protein type it belongs to? These questions are important because they are closely relevant to the biological function of the query protein and to its interaction process with other molecules in a biological system. Particularly, with the avalanche of protein sequences generated in the Post-Genomic Age and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to help address these questions. In this study, a 2-layer predictor, called MemType-2L, has been developed: the 1st layer prediction engine is to identify a query protein as membrane or non-membrane; if it is a membrane protein, the process will be automatically continued with the 2nd-layer prediction engine to further identify its type among the following eight categories: (1) type I, (2) type II, (3) type III, (4) type IV, (5) multipass, (6) lipid-chain-anchored, (7) GPI-anchored, and (8) peripheral. MemType-2L is featured by incorporating the evolution information through representing the protein samples with the Pse-PSSM (Pseudo Position-Specific Score Matrix) vectors, and by containing an ensemble classifier formed by fusing many powerful individual OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) classifiers. The success rates obtained by MemType-2L on a new-constructed stringent dataset by both the jackknife test and the independent dataset test are quite high, indicating that MemType-2L may become a very useful high throughput tool. As a Web server, MemType-2L is freely accessible to the public at http://chou.med.harvard.edu/bioinf/MemType.     

Filling-in Void and Sparse Regions in Protein Sequence Space by Protein-Like Artificial Sequences Enables Remarkable Enhancement in Remote Homology Detection Capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like “linker” sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be “plugged-into” routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.     

苹果果实发育过程中α-淀粉酶的活性、数量变化和亚细胞定位

淀粉降解代谢与种子萌发、叶片光合作用、块根贮藏及肉质果实的发育密切相关.α-淀粉酶是催化淀粉水解的重要酶之一,然而由于它在生活细胞中经常定位于叶绿体或质体之外,与淀粉基质在亚细胞水平上相互隔离,所以该酶在植物活体内的生理功能至今不完全清楚.研究表明,在苹果(Malus domestica Borkh cv. Starkrimson)果实发育过程中,α-淀粉酶活性由低到高,与淀粉含量大致呈现互为消长的变化.Western blotting实验证明,在果实发育过程中,α-淀粉酶的表观数量也是由少到多,与活性的变化一致.利用胶体金免疫电镜定位技术证明,果实内α-淀粉酶主要定位于质体内,其他亚细胞区域内α-淀粉酶分布很少;尤其在果实发育中后期,围绕质体内淀粉粒有高密度的α-淀粉酶分布,说明该酶主要分布于细胞内功能区域.α-淀粉酶优先定位于质体内的亚细胞分布特点在果实整个生长发育期没有变化.随着果实发育的推进,质体内胶体金分布密度显著增加,此结果与Western blotting实验相互印证.推测α-淀粉酶参与了果实细胞内质体中淀粉的水解过程.     

人工神经网络在发酵工业中的应用

人工神经网络技术具有很强的非线性映射能力,用于系统的非线性建模,具有无可比拟的优势,广泛应用于发酵过程中培养基的优化和系统建模与控制方面,本主要介绍了人工神经网络的基本原理与使用方法,以及BP神经网络在非线性函数逼近的优点,详细介绍了其在发酵培养基优化,连续搅拌反应器神经网络估计,分批发酵及补料分批发酵过程建模与控制优化中的应用实例。     

PC-1分子在细胞内定位的深入探讨

为了寻找影响PC-1分子进入细胞核的序列,将该分子不同区段分别与EGFP融合表达,通过观察绿色荧光蛋白在细胞内的分布,发现PC-1分子的第112～190区段是一独立的功能区,缺失这一区段后,分子的其余部分能够进入细胞核并在其中积累,这一区段使PC-1分子被排斥于细胞核之外,并在细胞质中浓缩成许多点状结构.运用以SOS恢复系统为基础的酵母双杂交方法,发现PC-1分子能够定位于细胞膜上,通过缺失突变发现该分子的第112～190区段是一独立的细胞膜定位信号序列,这一序列将其定位于细胞的胞膜上.