纳升电喷雾串联质谱技术对阻断泛素通路诱导凋亡相关蛋白质的鉴定

应用毛细管液相色谱 电喷雾 四极杆 飞行时间串联质谱和纳升电喷雾 四极杆 飞行时间串联质谱技术 ,对阻断白血病细胞泛素通路诱发的凋亡相关蛋白质进行了鉴定。通过双向电泳发现 ,蛋白质斑点H在阻断Mo7e白血病细胞泛素通路 2h之后 ,表达量明显增加 ,6h达到最高。该斑点经MALDI TOF MS肽质量指纹谱分析未获结果 ,但通过上述 2种串联质谱技术获得其胰蛋白酶水解肽段的串联质谱图和肽段的全长序列 ,经检索均确认为RhoGDIβ蛋白。进一步发现阻断泛素通路还诱发了另 2个斑点出现 ,位于H点附近 ,经鉴定为同一蛋白质 ,可能是不同翻译后修饰所造成     

基于数据非依赖采集的蛋白质组质谱数据解析方法研究进展

数据非依赖采集（DIA）是蛋白质组学领域近年来快速发展的质谱采集技术，其通过无偏碎裂隔离窗口内的所有母离子采集二级谱图，理论上可实现蛋白质样品的深度覆盖，同时具有高通量、高重现性和高灵敏度的优点。现有的DIA数据采集方法可以分为全窗口碎裂方法、隔离窗口序列碎裂方法和四维DIA数据采集方法（4D-DIA）3大类。针对DIA数据的不同特点，主要数据解析方法包括谱库搜索方法、蛋白质序列库直接搜索方法、伪二级谱图鉴定方法和从头测序方法4大类。解析得到的肽段鉴定结果需要进行可信度评估，包括使用机器学习方法的重排序和对报告结果集合的假发现率估计两个步骤，实现对数据解析结果的质控。本文对DIA数据的采集方法、数据解析方法及软件和鉴定结果可信度评估方法进行了整理和综述，并展望了未来的发展方向。     

基于数据非依赖采集的以肽为中心分析算法和软件的研究进展

数据非依赖采集(data-independent acquisition,DIA)是一种高通量、无偏性的质谱数据采集方法,具有定量结果重现性好,对低丰度蛋白质友好的特点,是近年来进行大队列蛋白质组研究的首选方法之一。由于DIA产生的二级谱是混合谱,包含了多个肽段的碎片离子信息,使得蛋白质鉴定和定量更加困难。目前,DIA数据分析方法分为两大类,即以肽为中心和以谱图为中心。其中,以肽为中心的分析方法鉴定更灵敏,定量更准确,已成为DIA数据解析的主流方法。其分析流程包括构建谱图库、提取色谱峰群、特征打分和结果质控4个关键步骤。本文综述了以肽为中心的DIA数据分析流程,介绍了基于此流程的数据分析软件及相关比较评估工作,进一步总结了已有的算法改进工作,最后对未来发展方向进行了展望。     

规模化蛋白质鉴定中母离子的准确检测技术研究

蛋白质组学的基础研究之一是蛋白质鉴定.规模化的蛋白质鉴定通常采用"鸟枪法",即选择一些酶切肽段(母离子)碎裂生成二级谱图,通过二级谱图及其母离子质量鉴定肽段,再推断对应的蛋白质.在鉴定过程中,母离子质量是一个关键参数.母离子是否是肽段的单同位素峰决定了正确肽段是否能进入候选,母离子的质量精度决定了候选肽段的数目.本文从判断单同位素峰和系统误差校准这两个角度研究了母离子的准确检测技术.判断单同位素峰的技术在蛋白质上已有研究,包括电荷判断、单同位素峰判断和重叠同位素峰判断.可以借鉴蛋白质水平的技术研究母离子的单同位素峰判断方法.同时母离子的系统误差校准也有较为成熟的方法.这两个角度的研究有助于提高规模化蛋白质的鉴定率.     

混合谱图所引发的肽段质谱数据分析的问题及其解决方案

基于数据依赖性采集模式(DDA)的质谱分析是进行规模化蛋白质组学研究的常用方法。由于这类方法往往是液相色谱与质谱联用,因此肽段分离及其质量鉴定是该方法的核心指标。对于复杂肽段混合物而言,混合质谱图是广泛存在的;它们给肽段定性和定量都带来严重的问题。在当前的技术条件下,开发用于识别和分析混合质谱图的生物信息学工具是解决这一问题的可行方案。本文主要介绍了混合质谱图产生的原因和影响因素,以及识别和分析混合谱图的相关生物信息学工具的最新开发进展。     

N-糖肽的规模化质谱解析方法进展

蛋白质糖基化修饰的鉴定是蛋白质翻译后修饰分析中最具挑战性的任务之一,近几年尤其受到关注.快速发展的质谱技术为规模化的蛋白质糖基化修饰研究提供了有效的手段.与其他基于质谱技术的翻译后修饰鉴定相比,糖基化鉴定的难点在于糖链是大分子而且存在微观不均一性,另外糖链本身可以在串联质谱中碎裂且与肽段的碎裂规律不同,导致蛋白质组学的质谱解析方法和软件难以完整地鉴定肽段序列和糖链结构.完整N-糖肽的鉴定是糖基化分析的热点内容之一,针对N-糖肽的鉴定,近年来,人们开发了多种多样的质谱解析方法,其中包括用N-糖酰胺酶切除糖链后鉴定N-糖基化位点的方法、基于电子转运裂解的糖肽肽段鉴定、基于高能碰撞裂解与电子转运裂解联用或碰撞诱导裂解与三级谱联用的完整N-糖肽鉴定等等.本文对这些质谱解析方法进行了整理和综述,简要指出了目前完整糖肽鉴定软件存在的一些不足,展望了未来的发展方向.     

串联质谱数据的从头解析与蛋白质的数据库搜索鉴定

蛋白质的鉴定是蛋白质组学研究中必不可少的一步。用串联质谱 (tandemmassspectrometry ,MS/MS)可以进行多肽的从头测序 (denovosequencing) ,并搜索数据库以鉴定蛋白质。用图论以及真实谱 理论谱联配 (alignment)的方法对串联质谱得到的多肽图谱进行从头解析 ,得到了可靠的多肽序列 ,并应用到数据库搜索中鉴定了相应的蛋白质。同时 ,还用统计的方法对SwissProt以及TrEMBL蛋白质数据库进行了详细的分析。结果表明 ,3个四肽或者 2个五肽或者 1个八肽一般可以唯一地确定一个蛋白质     

高通量质谱法用于小鼠脑部神经肽的鉴定

神经肽在参与调控人体各种生理功能上发挥着重要的作用,如痛觉、睡眠、情绪、学习与记忆等生理活动都受到神经肽的影响。神经肽主要存在于机体的神经组织内,其他体液和器官中也有少量的分布。目前对全脑组织神经肽高通量鉴定的研究仍不足,高通量检测这些神经肽对了解神经肽的组成和功能具有重要的意义。本研究通过对小鼠全脑组织内源性肽段的萃取,运用液相串联质谱(LC-MS/MS)技术对全脑组织的神经肽进行检测,共鉴定到1 830条内源性肽段和99条预测神经肽肽段。这些内源性肽段的鉴定在疾病的治疗和机制研究以及药物的研发方面提供了参考价值,也为研究新的神经肽及其功能奠定了基础。     

生物活性肽蛋白质组分分离与鉴定方法的建立

目的:评估大规模蛋白质组分离与鉴定技术策略在生物活性肽研究中的应用价值。方法:采用全溶液酶解及胶内分离与酶解方法分离生物活性肽;肽段离子阱串联质谱鉴定时,采用碰撞诱导解离与电子转移解离2种互补的肽段碎裂模式。结果:几种方法联用,鉴定到复杂生物活性肽样品中236个多肽大分子;不同分离和鉴定策略显示出良好的互补性,基于凝胶电泳分离的策略提供了最好的鉴定效果。结论:大规模的蛋白质组学分离与鉴定技术策略可以有效应用于生物活性肽组分的表征。     

rhIL-12二硫键、N-糖基化位点及C端氨基酸序列分析

重组人白细胞介素12（rhIL-12）是一种已经用于治疗肿瘤,寄生虫、病毒性感染及造血障碍等疾病研究的异二聚体糖蛋白。结构确证是质量控制的重要内容,此研究对CHO细胞表达的rhIL-12二硫键配对方式、N-糖基化位点以及C端氨基酸序列进行了分析,使用Trypsin、Chymotrypsin和Glu-C三种酶分别对rhIL-12进行非还原酶解,尽可能地在其所有半胱氨酸残基之间断裂而形成二硫键相连的肽段,然后使用LC-MS/MS对酶解后的肽段样品进行分析,确定了rhIL-12样品中存在和理论配对方式相符的7对二硫键。将rhIL-12二硫键还原后并烷基化修饰保护,分别采用Trypsin,Chymotrypsin和GluC进行酶解,并用LC-MS/MS对酶解后肽段进行了质谱肽图及C端氨基酸序列分析,确定了rhIL-12 p35亚基C端氨基酸序列的8个氨基酸、p40亚基C端氨基酸序列的15个氨基酸。对rhIL-12样品还原及烷基化后用Trypsin变性酶解,所得肽段在H₂O及H₂¹⁸O水中分别用PNGase F糖苷酶处理酶切产物。并通过二级质谱分析脱糖后糖肽段分子量变化,从而确定了rhIL-12的3个N糖基化修饰位点,分别为p35亚基的71位和85位以及p40亚基的200位。通过建立酶解结合二级质谱鉴定的方法,证明了新药rhIL-12的二硫键位点、C端氨基酸序列和糖基化位点与理论一致。     

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications.     

Three models of song learning: Evidence from behavior

Research on avian song learning has traditionally been based on an instructional model, as exemplified by the sensorimotor model of song development. Several large-scale, species-wide field studies of learned birdsongs have revealed that variation is narrowly restricted to certain aspects of song structure. Other aspects are sufficiently stereotyped and so widely shared by species' members that they qualify as species-specific universals. The limitations on natural song variation are difficult to reconcile with a fully open, instructive model of song learning. An alternative model based on memorization by selection postulates a system of innate neural templates that facilitate the recognition and rapid memorization of conspecific song patterns. Behavioral evidence compatible with this model includes learning preferences, rapid conspecific song learning, and widespread ocurrence of species-specific song universals that are recognized innately but fail to develop in songs of social isolates. A third model combines instruction, in the memorization phase, with selection during song production. An overproduced repertoire of plastic songs previously memorized by instruction is winnowed by selection imposed during social interactions at the time of adult song crystallization. Selection during production is well established as a factor in the song development of several species, in the form of action-based learning. The possible role of selective processes in song memorization merits further neurobiological investigation. © 1997 John Wiley & Sons, Inc. J Neurobiol 33: 501–516, 1997     

Learning set formation in a group of Guinea baboons (Papio papio): Importance of the behavioral variable measured

A recent report of one-trial learning in a group of saddle-back tamarins (Saguinus fuscicollis) conflicted with views of learning set formation based on traditional techniques employing isolated subjects. An experiment is reported in which a group of Guinea baboons (Papio papio) was given a series of discrimination and habit reversal tasks. Both one-trial learning and learning set formation occurred. Analysis of spatial behavior revealed that the group learned in a single trial to discriminate stocked from unstocked zones. Improvement in successes (reinforced “digging”) was progressive but not rapid, indicating learning set formation. It appears that outcomes depend on the behavioral variables chosen.     

Selectivity in social and asocial learning: investigating the prevalence,effect and development of young children's learning preferences

Culture evolution requires both modification and faithful replication of behaviour, thus it is essential to understand how individuals choose between social and asocial learning. In a quasi-experimental design, 3- and 5-year-olds (176), and adults (52) were presented individually with two novel artificial fruits, and told of the apparatus'' relative difficulty (easy versus hard). Participants were asked if they wanted to attempt the task themselves or watch an experimenter attempt it first; and then had their preference either met or violated. A significant proportion of children and adults (74%) chose to learn socially. For children, this request was efficient, as observing a demonstration made them significantly quicker at the task than learning asocially. However, for 5-year-olds, children who selected asocial learning were also found to be highly efficient at the task, showing that by 5 years children are selective in choosing a learning strategy that is effective for them. Adults further evidenced this trend, and also showed selectivity based on task difficulty. This is the first study to examine the rates, performance outcomes and developmental trajectory of preferences in asocial and social learning, ultimately informing our understanding of innovation.     

Temporal learning of predation risk by embryonic amphibians

For prey species that rely on learning to recognize their predators, natural selection should favour individuals able to learn as early as possible. The earliest point at which individuals can gather information about the identity of their potential predators is during the embryonic stage. Indeed, recent experiments have demonstrated that amphibians can learn to recognize predators prior to hatching. Here, we conditioned woodfrog embryos to recognize predatory salamander cues either in the morning or in the evening, and subsequently exposed the two-week-old tadpoles to salamander cues either in the morning or in the evening, and recorded the intensity of their antipredator behaviour. The data indicate that amphibians learn to recognize potential predators while still in the egg, and also learn the temporal component of this information, which they use later in life, to adjust the intensity of their antipredator responses throughout the day.     

Investigating the molecular mechanisms of learning and memory using Caenorhabditis elegans

Learning is an essential biological process for survival since it facilitates behavioural plasticity in response to environmental changes. This process is mediated by a wide variety of genes, mostly expressed in the nervous system. Many studies have extensively explored the molecular and cellular mechanisms underlying learning and memory. This review will focus on the advances gained through the study of the nematode Caenorhabditis elegans. C. elegans provides an excellent system to study learning because of its genetic tractability, in addition to its invariant, compact nervous system (~300 neurons) that is well-characterised at the structural level. Importantly, despite its compact nature, the nematode nervous system possesses a high level of conservation with mammalian systems. These features allow the study of genes within specific sensory-, inter- and motor neurons, facilitating the interrogation of signalling pathways that mediate learning via defined neural circuits. This review will detail how learning and memory can be studied in C. elegans through behavioural paradigms that target distinct sensory modalities. We will also summarise recent studies describing mechanisms through which key molecular and cellular pathways are proposed to affect associative and non-associative forms of learning.

     

The evolutionary basis of human social learning

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.     

转变学习方式,在探究活动中体会成功的乐趣

学生的学习方式一般有接受和发现两种,传统的学习方式过分突出和强调接受与掌握,冷落和忽视发现与探究,从而导致了对学生认识过程的极端处理,摧残了学生的学习兴趣和热情,而以培养创新精神和实践能力为主要目的的探究式学习方式则在培养学生多方面能力的同时给予他们以成功的乐趣。     

Recruiting machine learning methods for molecular simulations of proteins

Abstract

Molecular dynamics (MD) simulations are critical to understanding the movements of proteins in time. Yet, MD simulations are limited due to the availability of high-resolution protein structures, accuracy of the underlying force-field, computational expense, and difficulty in analysing big data-sets. Machine learning algorithms are now routinely used to circumvent many of these limitations and computational biophysicists are continuously making progress in developing novel applications. Here, we discuss some of these methods, varying from traditional dimensionality reduction approaches to more recent abstractions such as transfer learning and reinforcement learning, and how they have been used to deal with the challenges in MD. We conclude with the prospective issues in the application of machine learning methods in MD, to increase accuracy and efficiency of protein dynamics studies in general.