海藻肽的化学结构特征和活性作用研究进展

海藻中的肽类化合物具有显著的生物活性和药理作用,对其氨基酸序列及活性作用的研究已经取得了一些重要进展。发现的海藻肽类化合物并确定其化学结构式的主要有二肽、环肽和脂肽,这些肽类化合物具有抗肿瘤、降血压、降血脂、抗凝血、促进神经细胞分化、抗氧化、抗菌和抗病毒等生物活性。预测海藻肽类化合物在疑难病症的治疗上将发挥重要作用。     

乳清蛋白生物活性肽研究进展

以乳清蛋白为原料,经过酶解或发酵等方法可以获得独特理化性质的生物活性肽。乳清蛋白生物活性肽来源广、活性强、分子量小,在食品和医药行业有很高的应用研究价值,已经成为研究热点。随着制备、分离纯化以及鉴定技术的不断发展和成熟,越来越多的乳清蛋白生物活性肽被发现。本研究主要综述了乳清蛋白生物活性肽的制备、分离纯化、鉴定方法以及生物功能,并展望了乳清蛋白生物活性肽应用前景,以期为功能性乳清蛋白生物活性肽产品的开发与应用提供参考。     

乳蛋白生物活性肽的序列及其功能

乳蛋白经消化产生的肽类除了具有营养作用外,还具有多种生物活性,包括阿片肽和阿片拮抗肽活性及免疫调节,抗高血压、抗血栓、抗菌抗病毒,促进矿质元素吸收,防止腹泻等作用。本文对近几年发现的乳蛋白生物活性肽的序列结构及功能做了综述。     

抗生素抗性基因连锁传播的侧翼保守元件分析

抗生素在医疗、畜牧和水产养殖业的大量使用造成了环境中耐药细菌和抗性基因的日益增加,也加速了抗性基因在环境细菌间的传播扩散.本研究以环境样本直接提取的总DNA为模板,运用热不对称交错PCR (thermal asymmetric interlaced PCR, Tail-PCR)技术直接扩增抗生素抗性基因上下游序列.通过优化Tail-PCR反应程序,单循环同时扩增出tetW基因的多条侧翼序列,包括6条上游序列和9条下游序列.基于序列的生物信息学分析发现,上游包括一段反向重复序列和已知的一段tetW调节肽序列以及一个已知的插入序列,下游包括一个保守的未知序列和一个开放式阅读框架(the open reading frame,ORF)编码甲基转移酶.结果不仅发现了可能协助tetW基因传播的功能元件,也提供了一个未知侧翼序列高效和便捷的研究方法,即采用Tail-PCR技术,一组样品即能便捷获得多条侧翼序列.     

Myostatin三维结构模建及分子进化分析

Myostatin(MST)为肌肉生长负调节因子,其功能受抑制可导致肌肉量增加.对MST核酸序列进行序列比对,构建进化树;采用同源模建方法首次模建MST成熟肽生物活性二聚体的四级结构,并预测MST与其受体ActRIIB的相互作用模式.进化树将肌肉生长抑制素基因(MSTN)分成4个亚家族:哺乳动物MSTN,鸟类MSTN以及鱼类MSTN 1和2.MST受纯化选择作用,在不同物种的直系同源基因具有较高的刚源性,其中哺乳动物、鸟类MST C端活性肽氨基酸序列高度保守.表明哺乳动物、鸟类MST的结构、功能类似,且信号传导路径可能一致;而鱼类MST的调控机制可能存在较大差异.MST结构及其表面静电势和疏水氨基酸分布表明静电力和疏水相互作用在MST与其受体结合过程中可能起到十分重要的作用.     

抗逆蛋白查询系统的初步建立

运用生物信息学的手段从NCBI获得不同的植物抗逆基因,对基因的名称,功能,该基因的数据来源,及相关文献作整理,初步建立了抗逆蛋白查询系统。此系统与很多生物软件兼容,利用这些软件可方便的在该数据库中进行序列比对、抗逆蛋白同源性的比较、未知蛋白功能预测、绘制分子树等多项功能。     

植物LTR类反转录转座子序列分析识别方法

LTR类反转录转座子(Long terminal repeat retrotransponson)是真核生物中的一类重要转座元件,具有分布广泛、异质性高等特点,在真核生物基因组进化中起着重要作用,现广泛应用于植物的基因功能分析和遗传多样性研究等方面。LTR类反转录转座子的序列识别是其应用的前提条件,因此对LTR类反转录转座子的序列鉴定和分析方法的研究具有重要的理论意义和实际应用价值。LTR类反转录转座子序列的生物信息学分析软件按原理可大致分为序列比对分析和相关序列保守区域识别鉴定两类。比对软件如BLAST、DNAstar等,是一种序列相似性搜索程序,通过与已知的反转录转座子序列比对后的序列相似性来判断未知序列是否是反转录转座子序列,但这类软件不能直接获得具体的LTR等特征序列的相关信息,不能对反转录转座子序列的全长进行识别。识别鉴定软件按原理可分为从头算起法、比较基因组法、同源搜索法和结构基础法4种,如LTR-Finder等基于从头算起法的识别鉴定软件,可对LTR类反转录转座子全序列进行较准确地预测和注释,RepeatMasker等基于同源搜索法的软件,通过与数据库中的序列的相似性比对后发现可能存在的LTR类反转录转座子。文章对不同的LTR类反转录转座子预测方法进行了比较和分析,在此基础上归纳总结出一套分析LTR类反转录转座子序列的操作流程,旨在为LTR类反转录转座子序列的分析提供参考。     

果蝇非经典剪接位点的生物信息学预测

目的:计算识别果蝇中新的非经典剪接位点,以探索未知的剪接机制。方法:基于黑腹果蝇表达序列标签(EST)与其基因组序列比对数据重构基因结构,从中发现非经典的剪接位点,并采用Weblogo软件分析非经典剪接位点上下游序列,以期发现剪接相关的特异性元件。结果:共得到265个非经典的剪接位点,这些剪接位点落在195个蛋白编码基因上。结论:应用生物信息学方法在果蝇中发现了上百个非经典剪接位点,为研究非经典剪接机制奠定了基础。     

糜蛋白酶提高膜蛋白质谱分析序列覆盖率的胶内消化方法

近年来,质谱技术在膜蛋白结构与功能研究中被广泛应用。由于膜蛋白的跨膜结构域含有大量疏水性氨基酸,常常导致液质串联质谱检测的序列覆盖率较低,从而限制了质谱技术在膜蛋白结构与功能研究中的应用。文中利用人的整合膜蛋白维生素K环氧化物还原酶为模型,优化胶内消化条件,建立了一种稳定提高膜蛋白质谱序列覆盖率的糜蛋白酶胶内消化方法。通过探索钙离子浓度、pH值和缓冲体系对序列覆盖率、检测特异肽段的总数和类型以及特异肽段大小的影响,发现在5–10 mmol/L钙离子浓度、pH 8.0–8.5的Tris-HCl缓冲液中,可以兼顾序列覆盖率和肽段的多样性。该方法可以使膜蛋白的质谱覆盖率达到80%以上,将在膜蛋白结构与功能、膜蛋白相互作用位点的鉴定以及膜蛋白与小分子药物结合位点的鉴定等研究中具有广泛的应用价值。     

参数序列比对算法研究

序列比对是生物信息学中的一项重要任务,通过序列比对可以发现生物序列中的功能、结构和进化的信息。序列比对结果的生物学意义与所选择的匹配、不匹配、插入和删除以及空隙的罚分函数密切相关。现介绍一种参数序列比对方法,该方法把最佳比对作为权值和罚分的函数,可以系统地得到参数的选择对最佳比对结果的影响。然后将其应用于RNA序列比对,分析不同的参数选择对序列比对结果的影响。最后指出参数序列比对算法的应用以及未来的发展方向。     

A novel knowledge-based approach to design inorganic-binding peptides

MOTIVATION: The discovery of solid-binding peptide sequences is accelerating along with their practical applications in biotechnology and materials sciences. A better understanding of the relationships between the peptide sequences and their binding affinities or specificities will enable further design of novel peptides with selected properties of interest both in engineering and medicine. RESULTS: A bioinformatics approach was developed to classify peptides selected by in vivo techniques according to their inorganic solid-binding properties. Our approach performs all-against-all comparisons of experimentally selected peptides with short amino acid sequences that were categorized for their binding affinity and scores the alignments using sequence similarity scoring matrices. We generated novel scoring matrices that optimize the similarities within the strong-binding peptide sequences and the differences between the strong- and weak-binding peptide sequences. Using the scoring matrices thus generated, a given peptide is classified based on the sequence similarity to a set of experimentally selected peptides. We demonstrate the new approach by classifying experimentally characterized quartz-binding peptides and computationally designing new sequences with specific affinities. Experimental verifications of binding of these computationally designed peptides confirm our predictions with high accuracy. We further show that our approach is a general one and can be used to design new sequences that bind to a given inorganic solid with predictable and enhanced affinity.     

BioPD: a web-based information center for bioactive peptides

Bioactive peptide database (BioPD) is a web-based knowledge base that contains more than 1100 protein sequences from human, mouse and rat, which are putative or are known to be bioactive peptides. In addition to peptide sequences and the annotation, the database also contains gene sequences with annotation, protein interaction and disease data related to the peptides. Each entry has as many references as possible to support the information represented. BioPD consists of six parts: PROTEIN, GENE, DISEASE, LINKS, INTERACTION, and REFERENCE. The database is searchable through keyword, gene and protein name, receptor name, etc. The links to PDB, InterPro, Pfam, OMIM, etc. are provided in each entry. Thus BioPD is formed as an information center for the bioactive peptide and serves as a gateway for exploration of bioactive peptides. The database can be accessed at http://biopd.bjmu.edu.cn.     

De novo peptide sequencing via tandem mass spectrometry.

Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics for identifying proteins. Because complete genome sequences are accumulating rapidly, the recent trend in interpretation of MS/MS spectra has been database search. However, de novo MS/MS spectral interpretation remains an open problem typically involving manual interpretation by expert mass spectrometrists. We have developed a new algorithm, SHERENGA, for de novo interpretation that automatically learns fragment ion types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The test data are used to construct optimal path scoring in the graph representations of MS/MS spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins and for validating the results of database search algorithms in fully automated, high-throughput peptide sequencing.     

Multifunctional peptides encrypted in milk proteins

Many bioactivities of milk are latent in that they are inactive within the protein sequence, requiring enzymatic proteolysis for release of bioactive peptides from milk proteins precursors. Bioactivities of peptides encrypted in major milk proteins are latent until released and activated, e.g. during gastrointestinal digestion or food processing. Bioactive peptides can be produced in vivo following intake of milk proteins, and the proteolytic system of bacterial species used in the production of fermented milk products and cheese can contribute to the liberation of bioactive peptides or precursors thereof. Activated peptides are potential modulators of various regulatory processes in the living system: immunomodulatory peptides stimulate the activities of cells of the immune system and several cytomodulatory peptides inhibit cancer cell growth, antimicrobial peptides kill sensitive microorganisms, angiotensin-I-converting enzyme (ACE)-inhibitory peptides exert an hypotensive effect, opioid peptides are opioid receptor ligands which can modulate absorption processes in the intestinal tract, mineral binding peptides may function as carriers for different minerals, especially calcium. Many milk-derived peptides reveal multifunctional properties, i.e. specific peptide sequences having two or more different biological activities have been reported. Milk protein-derived bioactive peptides are claimed to be health enhancing components that can be used to reduce the risk of disease or to enhance a certain physiological function.     

Informatics for protein identification by mass spectrometry

High throughput protein analysis (i.e., proteomics) first became possible when sensitive peptide mass mapping techniques were developed, thereby allowing for the possibility of identifying and cataloging most 2D gel electrophoresis spots. Shortly thereafter a few groups pioneered the idea of identifying proteins by using peptide tandem mass spectra to search protein sequence databases. Hence, it became possible to identify proteins from very complex mixtures. One drawback to these latter techniques is that it is not entirely straightforward to make matches using tandem mass spectra of peptides that are modified or have sequences that differ slightly from what is present in the sequence database that is being searched. This has been part of the motivation behind automated de novo sequencing programs that attempt to derive a peptide sequence regardless of its presence in a sequence database. The sequence candidates thus generated are then subjected to homology-based database search programs (e.g., BLAST or FASTA). These homology search programs, however, were not developed with mass spectrometry in mind, and it became necessary to make minor modifications such that mass spectrometric ambiguities can be taken into account when comparing query and database sequences. Finally, this review will discuss the important issue of validating protein identifications. All of the search programs will produce a top ranked answer; however, only the credulous are willing to accept them carte blanche.     

Peptide sequence tag-based blind identification of post-translational modifications with point process model

An important but difficult problem in proteomics is the identification of post-translational modifications (PTMs) in a protein. In general, the process of PTM identification by aligning experimental spectra with theoretical spectra from peptides in a peptide database is very time consuming and may lead to high false positive rate. In this paper, we introduce a new approach that is both efficient and effective for blind PTM identification. Our work consists of the following phases. First, we develop a novel tree decomposition based algorithm that can efficiently generate peptide sequence tags (PSTs) from an extended spectrum graph. Sequence tags are selected from all maximum weighted antisymmetric paths in the graph and their reliabilities are evaluated with a score function. An efficient deterministic finite automaton (DFA) based model is then developed to search a peptide database for candidate peptides by using the generated sequence tags. Finally, a point process model-an efficient blind search approach for PTM identification, is applied to report the correct peptide and PTMs if there are any. Our tests on 2657 experimental tandem mass spectra and 2620 experimental spectra with one artificially added PTM show that, in addition to high efficiency, our ab-initio sequence tag selection algorithm achieves better or comparable accuracy to other approaches. Database search results show that the sequence tags of lengths 3 and 4 filter out more than 98.3% and 99.8% peptides respectively when applied to a yeast peptide database. With the dramatically reduced search space, the point process model achieves significant improvement in accuracy as well. AVAILABILITY: The software is available upon request.     

Industrial-scale manufacturing of pharmaceutical-grade bioactive peptides

Recent studies have shown that most peptide sequences encrypted in food proteins confer bioactive properties after release by enzymatic hydrolysis. Such bioactivities, which include antithrombotic, antihypertensive, immunomodulatory and antioxidant properties, are among the traits that are of biological significance in therapeutic products. Bioactive peptides could therefore serve as potential therapeutic agents. Moreover, research has shown that peptide therapeutics are toxicologically safe, and present less side effects when compared to small molecule drugs. However, the major conventional methods i.e. the synthetic and biotechnological methods used in the production of peptide therapeutics are relatively expensive. The lack of commercially-viable processes for large-scale production of peptide therapeutics has therefore been a major hindrance to the application of peptides as therapeutic aids. This paper therefore discusses the plausibility of manufacturing pharmaceutical-grade bioactive peptides from food proteins; the challenges and some implementable strategies for overcoming those challenges.     

Bioportides: Bioactive cell-penetrating peptides that modulate cellular dynamics

The study and exploitation of cell-penetrating peptides (CPPs) now extends into a third exciting decade. Pharmacokinetic modulators, including the more common sequences Tat, penetratin and transportan-10, markedly enhance the intracellular delivery of small drugs, peptides, oligonucleotides and proteins. We introduced the term bioportide to distinguish cell penetrant peptides with intrinsic bioactivities from more typically inert CPP vectors. Our first examples included rhegnylogically organised bioportides, monomeric peptides presenting pharmacophores for both cellular internalization and bioactivity discontinuously distributed within the primary sequence. However, it is conceptually expedient to employ the same terminology to encompass s ychnologic bioportides that comprise an inert CPP vector conjugated to an otherwise impermeable bioactive peptide. In such cases the CPP provides an obvious address function whilst the bioactive cargo, often a protein mimetic sequence, is the message. Additional targeting sequences, usually added as chimeric extensions, can also be accommodated within the design of CPPs and bioportides to enable cell- and tissue-selective targeting. Thus, the identification and exploitation of bioportides provides further scope to employ CPPs as research tools, diagnostics and therapeutics spanning a range of pathologies.     

ProbIDtree: an automated software program capable of identifying multiple peptides from a single collision-induced dissociation spectrum collected by a tandem mass spectrometer

In MS/MS experiments with automated precursor ion, selection only a fraction of sequencing attempts lead to the successful identification of a peptide. A number of reasons may contribute to this situation. They include poor fragmentation of the selected precursor ion, the presence of modified residues in the peptide, mismatches with sequence databases, and frequently, the concurrent fragmentation of multiple precursors in the same CID attempt. Current database search engines are incapable of correctly assigning the sequences of multiple precursors to such spectra. We have developed a search engine, ProbIDtree, which can identify multiple peptides from a CID spectrum generated by the concurrent fragmentation of multiple precursor ions. This is achieved by iterative database searching in which the submitted spectra are generated by subtracting the fragment ions assigned to a tentatively matched peptide from the acquired spectrum and in which each match is assigned a tentative probability score. Tentatively matched peptides are organized in a tree structure from which their adjusted probability scores are calculated and used to determine the correct identifications. The results using MALDI-TOF-TOF MS/MS data demonstrate that multiple peptides can be effectively identified simultaneously with high confidence using ProbIDtree.     

Processing of chromogranin A in the parathyroid: generation of parastatin-related peptides

Chromogranin A (CgA) is a glycoprotein present in secretory granules of endocrine cells. In the parathyroid, it is costored and cosecreted with parathormone (PTH) in response to hypocalcemia. CgA is the precursor of several bioactive peptides including pancreastatin and betagranin. Parastatin (PARA, pCgA(347-419)) is a novel peptide that we generated in vitro by enzymatic digestion of pCgA. In vitro, it inhibits low Ca(2+)-stimulated parathyroid secretion. Full activity resides in its first 19 residues. In order to determine if PARA or PARA-derived peptides are natural products of the parathyroid, we generated an antiserum directed against pCgA(347-359) corresponding to the bioactive N-terminal sequence of pPARA (pPARA(1-13) antiserum), and developed a specific radioimmunoassay that we used in conjunction with various chromatographic separations. We identified small peptides carrying the pPARA(1-13) immunoactivity in extracts and secretion medium of porcine parathyroid glands. Continuous and pulse-chase radiolabeling studies, along with immunoprecipitation using PARA(1-13) antiserum demonstrate that a newly-synthesized PARA-related peptide fraction with a Mr of 11 kDa is secreted by the parathyroid cells and accumulates in the secretion medium. Edman degradation of the 11 kDa PARA-related peptide band by Edman degradation yielded three major N-terminal sequences: S-K-M-D-R-L-A-K-E-L-(residues 313-322), D-R-L-A-K-E-L-T-A-E-(residues 316-325), and A-K-E-L-T-A-E-K-R-L-(residues 319-329), in a molar ratio of approximately 1:2:1. The peptide bonds required to be cleaved to yield these peptides, Trp-Ser, Met-Asp and Leu-Ala, suggest that a chymotrypsin-like endopeptidase participated in their formation. The molecular size and the results of amino acid compositional analysis, indicate that the C-termini of these peptides extended variably to residues 384-401 of pCgA. These results demonstrate that processing of CgA by the parathyroid gland generates bioactive PARA-related peptides that could affect the gland's secretory activity.