膜蛋白跨膜区预测方法的评价

基因组计划所产生的大量蛋白质序列迫切需要从理论上预测跨膜区。对现有预测跨膜区的方法进行评价 ,不仅可以帮助生物学家选择合适的方法 ,而且可以为生物信息学家发展新算法提供指导。采用了最新的膜蛋白数据库作为基本测试集合并选择了水溶性蛋白序列作为对照组 ,对目前已经公开发表且提供网上服务的跨膜区预测方法进行了评价和分析。经过分析比较 ,HMMTOP在所有的方法中综合预测效果最佳     

基于小波分析的膜蛋白跨膜区段序列分析和预测

膜蛋白是一类结构独特的蛋白质,在各种细胞中普遍存在,发挥着重要的生理功能。目前仅有少数膜蛋白听结构被实验测出,因此用计算机预测膜蛋白的结构是蛋白质结构预测的主要研究内容之一。膜蛋白一般在膜上形成保守的跨膜螺旋结构,序列特征明显,比较适合用预测的方法确定跨膜螺旋区段的位置。国际上已有一些研究者用人工神经网络方法、多序列比对方法和统计方法进行了预测尝试,取得了一定的成功经验。我们对蛋白质序列数据库中的     

α-螺旋跨膜蛋白拓扑结构预测方法的评价

在基因组数据中,有20%～30%的产物被预测为跨膜蛋白,本文通过对膜蛋白拓扑结构预测方法进行分析,并评价其结果,为选择更合适的拓扑结构预测方法预测膜蛋白结构。通过对目前已有的拓扑结构预测方法的评价分析,可以为我们在实际工作中提供重要的参考。比如对一个未知拓扑结构的跨膜蛋白序列,我们可以先进行是否含有信号肽的预测,参考Polyphobius和SignalP两种方法,若两种方法预测结果不一致,综合上述对两种方法的评价,Polyphobius预测的综合能力较好,可取其预测的结果,一旦确定含有信号肽,则N端必然位于膜外侧。然后结合序列的长度,判断蛋白是单跨膜还是多重跨膜,即可参照上述评价结果,选择合适的拓扑结构预测方法进行预测。     

干扰素诱导跨膜蛋白抗病毒感染研究进展

干扰素诱导跨膜蛋白(interferon-inducing transmembrane proteins,IFITM)是一类宿主限制性因子(host restrictive factor,HRF),在抗病毒感染过程中发挥重要作用。IFITM作为一类氨基酸高度保守的抗病毒蛋白,在机体内广泛分布。随着对其研究的深入,发现该IFITM以多种通路/机制产生抗病毒感染的作用,使其作为研发抗病毒治疗药物的作用靶点成为可能。现就IFITM拓扑结构,抗病毒机制和抗病毒谱的研究作一概述。     

四次跨膜蛋白在病毒感染中作用的研究进展

四次跨膜蛋白（Tetraspanins,Tspans）是一个作为“细胞膜主要组织者”的跨膜糖蛋白家族。它们与其他膜蛋白横向结合形成富含Tspans的微结构域（Tetraspanin-enriched microdomains,TEMs）,在细胞表面建立了独特的平台,从而直接或间接参与病毒感染的多个阶段。Tspans在调控病毒进入、转录、复制和组装释放等阶段均发挥了重要作用。研究Tspans在病毒感染中的作用,可以为病毒感染疾病的临床药物研发提供新靶点。本文综述了Tspans在病毒感染中作用的研究进展。     

神经系统中富亮氨酸重复序列跨膜蛋白的功能研究进展

富亮氨酸重复序列(leucine-rich repeat, LRR)是一种常见的蛋白质结构域．含有富亮氨酸重复序列的蛋白质简称LRR蛋白．LRR蛋白在真核生物和原核生物的细胞和组织中广泛分布,其定位的特异性以及与之相互作用蛋白质的复杂性,决定了LRR蛋白功能的多样性．许多LRR蛋白相对特异性表达于神经系统,绝大多数在神经系统中高表达的LRR蛋白属于跨膜蛋白,它们主要作为细胞黏附分子或配体结合蛋白参与突触的形成、神经突起的生长发育、神经递质的转移和释放等神经系统正常生理活动．LRR蛋白的异常表达将会导致神经、精神系统疾病的发生．     

干扰素诱导的跨膜蛋白抗病毒作用的研究进展

干扰素诱导的跨膜蛋白(Interferon-induced Transmembrane Proteins,IFITMs)是20世纪80年代发现的一种宿主限制因子蛋白,1996年发现该蛋白具有抗病毒作用,目前该蛋白的抗病毒作用及其作用机制已成为研究热点。研究表明IFITM能抑制多种病毒的复制,包括甲型流感病毒、人类免疫缺陷病毒-1、丙型肝炎病毒、埃博拉病毒和西尼罗病毒等。IFITM蛋白主要在病毒生命周期的早期,即病毒进入细胞质之前,发挥抑制病毒复制的作用。近来的研究表明,IFITM蛋白通过影响病毒包膜与内涵体膜的融合抑制病毒复制,但具体机制尚不明确。本文对IFITM的发现、结构、抗病毒作用以及潜在的作用机制进行了综述。     

四次跨膜蛋白超家族成员与肿瘤的研究进展

Tetraspanins又称四次跨膜蛋白超家族(transmembrane 4 superfamily,TM4SF),包含33个家族成员,通过形成二聚体或异二聚体,或与其他蛋白质分子如整合素、黏附分子、主要组织相容性复合体II类抗原(major histocompatibility complex class II,MHC II)、T细胞受体等相互作用,调控细胞黏附、增殖、组织分化、免疫反应等生物学过程。越来越多研究表明,一些TM4SF分子也与肿瘤发生发展密切相关,参与迁移、上皮?间质转化、血栓形成、肿瘤干细胞及外泌体信号转导等多阶段过程。对能够促进或抑制肿瘤发生发展的TM4SF功能和调控机制的深入了解,将为未来有针对性的靶向干预提供新的策略。     

膜蛋白的拓扑学

膜蛋白的拓扑学是研究膜蛋白三维结构的出发点．利用融合蛋白和化学修饰等实验技术已确定了很多膜蛋白的拓扑学．对膜蛋白的转运与插膜的研究确定可能存在两类插膜元件．对已知拓扑学的膜蛋白的统计分析以及蛋白质工程的研究表明存在膜蛋白拓扑学的内正规则．目前已形成预测膜蛋白的拓扑学的比较可靠的策略,这在反向生物学上具有重要意义.但要进行三维结构的预测还有许多路要走．     

Simultaneous prediction of protein secondary structure and transmembrane spans

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α‐helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three‐state secondary structure prediction, and 94.8% for three‐state transmembrane span prediction. These accuracies are comparable to state‐of‐the‐art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org . Proteins 2013; 81:1127–1140. © 2013 Wiley Periodicals, Inc.     

TMSEG: Novel prediction of transmembrane helices

Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bridged the gap by predicting transmembrane helices (TMHs), but room for improvement remains. Here, we present TMSEG, a novel method identifying TMPs and accurately predicting their TMHs and their topology. The method combines machine learning with empirical filters. Testing it on a non‐redundant dataset of 41 TMPs and 285 soluble proteins, and applying strict performance measures, TMSEG outperformed the state‐of‐the‐art in our hands. TMSEG correctly distinguished helical TMPs from other proteins with a sensitivity of 98 ± 2% and a false positive rate as low as 3 ± 1%. Individual TMHs were predicted with a precision of 87 ± 3% and recall of 84 ± 3%. Furthermore, in 63 ± 6% of helical TMPs the placement of all TMHs and their inside/outside topology was correctly predicted. There are two main features that distinguish TMSEG from other methods. First, the errors in finding all helical TMPs in an organism are significantly reduced. For example, in human this leads to 200 and 1600 fewer misclassifications compared to the second and third best method available, and 4400 fewer mistakes than by a simple hydrophobicity‐based method. Second, TMSEG provides an add‐on improvement for any existing method to benefit from. Proteins 2016; 84:1706–1716. © 2016 Wiley Periodicals, Inc.     

Analysis of protein transmembrane helical regions by a neural network.

Neural networks were used to generalize common themes found in transmembrane-spanning protein helices. Various-sized databases were used containing nonoverlapping sequences, each 25 amino acids long. Training consisted of sorting these sequences into 1 of 2 groups: transmembrane helical peptides or nontransmembrane peptides. Learning was measured using a test set 10% the size of the training set. As training set size increased from 214 sequences to 1,751 sequences, learning increased in a nonlinear manner from 75% to a high of 98%, then declined to a low of 87%. The final training database consisted of roughly equal numbers of transmembrane (928) and nontransmembrane (1,018) sequences. All transmembrane sequences were entered into the database with respect to their lipid membrane orientation: from inside the membrane to outside. Generalized transmembrane helix and nontransmembrane peptides were constructed from the maximally weighted connecting strengths of fully trained networks. Four generalized transmembrane helices were found to contain 9 consensus residues: a K-R-F triplet was found at the inside lipid interface, 2 isoleucine and 2 other phenylalanine residues were present in the helical body, and 2 tryptophan residues were found near the outside lipid interface. As a test of the training method, bacteriorhodopsin was examined to determine the position of its 7 transmembrane helices.     

Evaluation of transmembrane helix predictions in 2014

Experimental structure determination continues to be challenging for membrane proteins. Computational prediction methods are therefore needed and widely used to supplement experimental data. Here, we re‐examined the state of the art in transmembrane helix prediction based on a nonredundant dataset with 190 high‐resolution structures. Analyzing 12 widely‐used and well‐known methods using a stringent performance measure, we largely confirmed the expected high level of performance. On the other hand, all methods performed worse for proteins that could not have been used for development. A few results stood out: First, all methods predicted proteins in eukaryotes better than those in bacteria. Second, methods worked less well for proteins with many transmembrane helices. Third, most methods correctly discriminated between soluble and transmembrane proteins. However, several older methods often mistook signal peptides for transmembrane helices. Some newer methods have overcome this shortcoming. In our hands, PolyPhobius and MEMSAT‐SVM outperformed other methods. Proteins 2015; 83:473–484. © 2014 Wiley Periodicals, Inc.     

Conductance and amantadine binding of a pore formed by a lysine-flanked transmembrane domain of SARS coronavirus envelope protein

The coronavirus responsible for the severe acute respiratory syndrome (SARS-CoV) contains a small envelope protein, E, with putative involvement in host cell apoptosis and virus morphogenesis. It has been suggested that E protein can form a membrane destabilizing transmembrane (TM) hairpin, or homooligomerize to form a regular TM alpha-helical bundle. We have shown previously that the topology of the alpha-helical putative TM domain of E protein (ETM), flanked by two lysine residues at C and N termini to improve solubility, is consistent with a regular TM alpha-helix, with orientational parameters in lipid bilayers that are consistent with a homopentameric model. Herein, we show that this peptide, reconstituted in lipid bilayers, shows sodium conductance. Channel activity is inhibited by the anti-influenza drug amantadine, which was found to bind our preparation with moderate affinity. Results obtained from single or double mutants indicate that the organization of the transmembrane pore is consistent with our previously reported pentameric alpha-helical bundle model.     

Comparing side chain packing in soluble proteins,protein‐protein interfaces,and transmembrane proteins

We compare side chain prediction and packing of core and non‐core regions of soluble proteins, protein‐protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high‐resolution crystal structures of these 3 protein classes. We show that the solvent‐inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein‐protein interfaces and in the membrane‐exposed regions of transmembrane proteins can be predicted by the hard‐sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent‐inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within ) up to a relative solvent accessibility, , for all 3 protein classes. Our results show that % of the interface regions in protein complexes are “core”, that is, densely packed with side chain conformations that can be accurately predicted using the hard‐sphere model. We propose packing fraction as a metric that can be used to distinguish real protein‐protein interactions from designed, non‐binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins.     

Progress and challenges in high-resolution refinement of protein structure models

Achieving atomic level accuracy in de novo structure prediction presents a formidable challenge even in the context of protein models with correct topologies. High-resolution refinement is a fundamental test of force field accuracy and sampling methodology, and its limited success in both comparative modeling and de novo prediction contexts highlights the limitations of current approaches. We constructed four tests to identify bottlenecks in our current approach and to guide progress in this challenging area. The first three tests showed that idealized native structures are stable under our refinement simulation conditions and that the refinement protocol can significantly decrease the root mean square deviation (RMSD) of perturbed native structures. In the fourth test we applied the refinement protocol to de novo models and showed that accurate models could be identified based on their energies, and in several cases many of the buried side chains adopted native-like conformations. We also showed that the differences in backbone and side-chain conformations between the refined de novo models and the native structures are largely localized to loop regions and regions where the native structure has unusual features such as rare rotamers or atypical hydrogen bonding between beta-strands. The refined de novo models typically have higher energies than refined idealized native structures, indicating that sampling of local backbone conformations and side-chain packing arrangements in a condensed state is a primary obstacle.     

Distinct protein interfaces in transmembrane domains suggest an in vivo folding model

Given the known high-resolution structures of alpha-helical transmembrane domains, we show that there are statistically distinct classes of transmembrane interfaces which relate to the folding and oligomerization of transmembrane domains. Distinct types of interfaces have been categorized and refer to those between: the same polypeptide chain, different polypeptide chains, helices that are sequential neighbors, and those that are nonsequential. These different interfaces may reflect different phases in the mechanism of transmembrane domain folding and are consistent with the current experimental evidence pertaining to the folding and oligomerization of transmembrane domains. The classes of helix-helix interfaces have been identified in terms of the numbers and different types of pairwise amino acid interactions. The specific measures used are interaction entropy, the information content of interacting partners compared to a random set of contacts, the amino acid composition of the classes and the abundances of specific amino acid pairs in close contact. Knowledge of the clear differences in the types of helix-helix contacts helps with the derivation of knowledge-based constraints which until now have focused on only the interiors of transmembrane domains as compared to the exterior. Taken together, an in vivo model for membrane protein folding is presented, which is distinct from the familiar two-stage model. The model takes into account the different interfaces of membrane helices defined herein, and the available data regarding folding in the translocation channel.     

融合蛋白TAT-RIG-I-GFP的原核表达及其跨膜递送

本研究旨在探讨融合蛋白TAT-RIG-I-GFP原核表达载体的构建并验证TAT在跨膜递送中的作用。首先设计了4对特异性引物,克隆了绿头鸭AnasplatyrhynchosRIG-I基因,构建了pET-TAT-RIG-I-GFP和pET-RIG-I-GFP原核表达载体;转化至感受态DE3细胞,经IPTG诱导表达,利用His60镍亲和层析柱纯化,进行SDS-PAGE;然后,将纯化后的上述两种表达蛋白分别孵育DF-1细胞;最后利用荧光显微镜观察是否在DF-1细胞产生相应的荧光。结果证实,携带有TAT的pET-TAT-RIG-I-GFP融合蛋白在DF-1细胞中显示出明显的绿色荧光;而不具有TAT的pET-RIG-I-GFP蛋白却不能显示绿色荧光。这表明携带TAT的融合蛋白已成功进入DF-1细胞,并在跨膜递送过程中发挥了关键作用。上述为进一步研制家禽的抗病毒药物奠定了基础。