应用马尔科夫链模型对草地螟发生程度的预测

草地螟Loxostege stictialis L.是我国北方农牧业生产上一种重要迁飞性、暴发性害虫,一旦暴发会给当地农牧生产造成严重危害.根据康保县1977-2008年1代草地螟幼虫发生程度的时间序列资料,应用马尔科夫链的转移概率预测法,构建了1～3阶转移概率矩阵,组建模型对该县2009-2011年1代草地螟发生程度进行了预测,结果与大田实际发生情况完全一致,准确率100％.对1980-2011年的历史资料进行回检,历史符合率89.9％,该方法可对草地螟进行长期预报,为草地螟长期预报提供了一种准确有效的方法,对草地螟发生程度的长期预报具有重要指导意义.     

基于马尔科夫链的医疗决策

在临床实践中,医生和患者均面临决策,由于医生和患者个体知识经验的局限性,仅依赖个人经验的决策判断难以全面评估治疗方案的好坏,而通过马尔科夫链模型可以帮助医生和患者对复杂疾病建立抽象模型,便于对疾病的各治疗效果进行决策分析。马尔科夫链模型是处理离散事件的随机过程,通过当前设定的信息,预测将来的情况。本文总结了马尔科夫链在医疗决策中的应用的基本原理,梳理了在医疗决策领域常用的马尔科夫链模型的分类,针对医疗决策的特点探讨不同类型马尔科夫链的矩阵法、队列法以及蒙特卡洛模拟分析方法的适用范围和优缺点。针对疾病进展的三状态模型以及是否使用某药物的实际决策案例,分析比较队列法与蒙特卡洛模拟法的具体应用,总结归纳队列法与蒙特卡洛模拟法的优缺点。     

基于马尔科夫链模型的长江源区土地覆盖格局变化特征

利用长江源区1986、2000与2014年3期的遥感影像,结合实地野外考察获得该地区在这3个时间点的土地覆盖类型图。根据各时期之间的土地覆盖格局的变化确定土地类型之间的转移概率,进一步完成对该地区马尔科夫链模型的构建、检验与预测。结果表明:1986—2014年,长江源区的土地覆盖格局的变化特征符合马尔科夫过程,通过马尔科夫链模型能够对该地区的覆盖格局变化过程进行有效的模拟;长江源区的土地覆被退化趋势明显,湿地、中高覆盖草地等面积不断下降,裸地、沙地以及低覆盖草地等面积则一直在增加;2000年以后,由于三江源区自然保护区的建立以及降水量的增加等因素影响,长江源区的植被退化状况得到明显改善。     

鳞翅目基因组IIB型内切酶位点的统计分析

IIB型限制内切酶能够识别并切割特异酶切位点两端特定距离的DNA,形成粘性末端的30 bp左右的等长DNA片段。利用其特性与限制性酶切位点关联测序技术(RAD)相结合发展出2b-RAD简化基因组测序技术,应用于遗传图谱构建、种群遗传结构分析、性状定位以及细菌分型等多种研究领域。构建2b-RAD测序文库之前,需要对基因组中的IIB型限制内切酶位点进行预测与统计分析,制定有效的测序文库构建方案。本文利用Python语言构建分析基因组中IIB型限制内切酶位点的流程,预测并统计6个鳞翅目代表物种基因组含有的8个商业化IIB型限制内切酶的酶切位点,比较了各个基因组与IIB型限制内切酶之间含有的酶切位点总量、重复序列数量以及酶切间隔长度的关系,为在昆虫基因组中进一步试行2b-RAD研究提供了参考。     

葛根糖基转移酶蛋白肽段序列的分离与鉴定

目的:分离鉴定葛根糖基转移酶蛋白肽段序列.方法:从野葛根部提取糖基转移酶,分离后对符合糖基转移酶分子量大小的5条蛋白条带进行酶解,经过高效液相色谱分离纯化后,电喷离子化质谱测定获得蛋白.结果:共获得440蛋白,发现有明确功能的蛋白有325个;具有催化活性的蛋白质有247个.根据报道的糖基转移酶分子量和等电点分布的特点,筛选出6个可能为糖基转移酶的蛋白.结论:初步推测葛根糖基转移酶的蛋白肽段序列,为其在葛根素生物合成中的作用研究奠定基础.     

一种新的B链C端去四肽胰岛素的研制方法

报道了将单体胰岛素前体(MIP)经胰蛋白酶和羧肽酶B两步连续酶切获得B链C端去四肽胰岛素(DTI)的方法。MIP由甲醇酵母表达,最高发酵表达量达到150mg/L。发酵液中MIP通过疏水层析,分子筛初步纯化后直接进行酶切,在胰蛋白酶酶切3h后加入抑制剂paminobenzamidine处理15min,然后直接加入羧肽酶B酶切6h,再通过反相柱纯化即可得到纯品DTI,从分子筛到最后DTI,总纯化得率达到77%。按中国药典小白鼠惊厥法测定得DTI的生物活力为22IU/mg,是胰岛素的80%,在Superdex G-75分子筛上测定DTI的解离聚合曲线,证明其是单体。     

提高Xa因子酶切效率的策略

为提高Xa因子对融合蛋白CBD-IGF和CBD-PACAP的酶切效率 ,以便高效释放非融合的重组多肽 ,利用基因工程技术在两个融合蛋白中Xa因子识别位点 (Ile-Glu-Gly-Arg↓ )前均引入 7个氨基酸组成的富含甘氨酸柔性短肽 (Gly-Thr-Gly-Gly-Gly-Ser-Gly)。纤维素亲和层析纯化各个融合蛋白 ,比较Xa因子对引入短肽前、后融合蛋白的酶切效率。比较结果表明 :短肽的引入不同程度地提高了融合蛋白CBD-IGF和CBD-PACAP对Xa因子的敏感性 ;但总体上CBD-IGF对Xa因子的敏感性比CBD-PACAP低。此研究结果提供了一种提高Xa因子酶切效率的策略。     

蛋白质二级结构预测: 基于词条的最大熵马尔科夫方法

提出了一种新的蛋白质二级结构预测方法. 该方法从氨基酸序列中提取出和自然语言中的“词”类似的与物种相关的蛋白质二级结构词条, 这些词条形成了蛋白质二级结构词典, 该词典描述了氨基酸序列和蛋白质二级结构之间的关系. 预测蛋白质二级结构的过程和自然语言中的分词和词性标注一体化的过程类似. 该方法把词条序列看成是马尔科夫链, 通过Viterbi算法搜索每个词条被标注为某种二级结构类型的最大概率, 其中使用词网格描述分词的结果, 使用最大熵马尔科夫模型计算词条的二级结构概率. 蛋白质二级结构预测的结果是最优的分词所对应的二级结构类型. 在4个物种的蛋白质序列上对这种方法进行测试, 并和PHD方法进行比较. 试验结果显示, 这种方法的Q3准确率比PHD方法高3.9%, SOV准确率比PHD方法高4.6%. 结合BLAST搜索的局部相似的序列可以进一步提高预测的准确率. 在50个CASP5目标蛋白质序列上进行测试的结果是: Q3准确率为78.9%, SOV准确率为77.1%. 基于这种方法建立了一个蛋白质二级结构预测的服务器, 可以通过http://www.insun.hit.edu.cn:81/demos/biology/index.html来访问.     

脊髓灰质炎病毒各代表株壳蛋白二级结构及亲水概率的电子计算机预测

本文用脊髓灰质炎病毒3个型6个强弱代表株壳蛋白一级结构,借助电子计算机预测和计算出病毒壳蛋白的二级结构和亲水概率。     

Prediction of MHC Class I Binding Peptides by a Query Learning Algorithm Based on Hidden Markov Models

A query learning algorithm based on hidden Markov models (HMMs) isdeveloped to design experiments for string analysis and prediction of MHCclass I binding peptides. Query learning is introduced to aim at reducingthe number of peptide binding data for training of HMMs. A multiple numberof HMMs, which will collectively serve as a committee, are trained withbinding data and used for prediction in real-number values. The universeof peptides is randomly sampled and subjected to judgement by the HMMs.Peptides whose prediction is least consistent among committee HMMs aretested by experiment. By iterating the feedback cycle of computationalanalysis and experiment the most wanted information is effectivelyextracted. After 7 rounds of active learning with 181 peptides in all,predictive performance of the algorithm surpassed the so far bestperforming matrix based prediction. Moreover, by combining the bothmethods binder peptides (log Kd < -6) could be predicted with84% accuracy. Parameter distribution of the HMMs that can be inspectedvisually after training further offers a glimpse of dynamic specificity ofthe MHC molecules.     

Prediction of Side Chain Orientations in Proteins by Statistical Machine Learning Methods

Abstract

We develop ways to predict the side chain orientations of residues within a protein structure by using several different statistical machine learning methods. Here side chain orientation of a given residue i is measured by an angle Ω_i between the vector pointing from the center of the protein structure to the C^α _i atom and the vector pointing from the C^α _i atom to the center of its side chain atoms. To predict the Ω_i angles, we construct statistical models by using several different methods such as general linear regression, a regression tree and bagging, a neural network, and a support vector machine. The root mean square errors for the different models range only from 36.67 to 37.60 degrees and the correlation coefficients are all between 30% and 34%. The performances of different models in the test set are, thus, quite similar, and show the relative predictive power of these models to be significant in comparison with random side chain orientations.     

Structure Prediction for Peptides Capable of Inducing Antibody Formation in Mice

A simple method for the sequence prediction of peptides capable of thein vivo stimulation of antibody production in mice without conjugation with protein carriers was proposed on the basis of literature data on the structure of T-helper epitopes active in vivo. According to this approach, a potentially active peptide should contain a nine-membered sequence with a hydrophobic amino acid residue in the first position and a positively charged residue in the ninth position. The efficiency of this approach was confirmed by the presence of such sequences in the previously described synthetic peptides with immune activities, by the application of this approach to the choice of immunogenic fragments within the sequences of various proteins that exhibited further the specific activity, and by the construction of immunogenic peptides on the basis of inactive natural sequences.     

The Evaluation of Protein Structure Prediction Results

Methods for protein structure prediction are flourishing and becoming widely available to both experimentalists and computational biologists. However, how good are they? What is their range of applicability and how can we know which method is better suited for the task at hand? These are the questions that this review tries to address, by describing the worldwide Critical Assessment of techniques for protein Structure Prediction (CASP) initiative and focusing on the specific problems of assessing the quality of a protein 3D model.     

基于马尔可夫链的基因芯片特异性探针选择方法

对于基因表达芯片,特异性探针的选择是探针设计的重要环节,由于基因组序列数据量极大,不可能对每个候选探针都在全序列中进行特异性评价并进行取舍。对此问题,提出了一种采用马尔可夫链概率准则的探针特异性选择方法,即把基因组序列看作马尔可夫链,任何探针序列的互补序列作为它的一个子序列,都具有一定的出现概率,概率越小,越可能具有特异性。据此,选择其中概率最小的N个候选探针,能够大大减少进行特异性评价的探针数量,缩短探针设计的计算时间。对实际数据的测试结果表明,该方法选择的探针具有很高的特异性。     

Mapping of the Synaptosome-Binding Regions on the Heavy Chain of Botulinum Neurotoxin A By Synthetic Overlapping Peptides Encompassing the Entire Chain

The purpose of this work was to map, on the heavy (H) chain of botulinum neurotoxin A (BoNT/A), the regions that bind to mouse brain synaptosomes (snps). We prepared 60 synthetic overlapping peptides that had uniform size and overlaps and encompassed the entire H chain (residues 499 to 1296) of BoNT/A. The ability of each peptide to inhibit the binding of ¹²⁵I-labeled BoNT/A to mouse brain snps was studied. The binding of ¹²⁵I-labeled BoNT/A to mouse brain snps was completely inhibited by free unlabeled BoNT/A, but not by unrelated proteins, indicating that the binding of BoNT/A to mouse brain snps was a specific event. Inhibition studies with the individual peptides showed that, on the H_N domain, inhibitory activities greater than 10% were exhibited, in decreasing order, by peptides 799–817, 659–677, 729–747, 533–551, 701–719, and 757–775. Lower inhibitory activities (between 5.6% and 8.7%) were exhibited by five other peptides, 463–481, 505–523, 519–537, 603–621 and 645–663. The remaining 18 H_N peptides had little or no inhibitory activity. In the H_C domain, peptides 1065–1083, 1163–1181 and 1275–1296 had the highest inhibitory activities (between 25% and 29%), followed (10–12% inhibitory activity) by peptides 1107–1125, 1191–1209 and 1233–1251. Two other peptides, 1079–1097 and 1177–1195, had very low (5.8% and 4.9 %) inhibitory activities. The remaining 23 H_C peptides had no inhibitory activity. Inhibition with mixtures of equimolar quantities of the most active 6 peptides of H_N, 5 of H_C or all 11 of H_N and H_C revealed that the peptides contain independent non-competing binding regions. Comparison of the locations of the snp-binding regions on the H-subunit with the regions that bind blocking mouse anti-BoNT/A Abs helped explain the protecting ability of these Abs. In the three-dimensional structure of BoNT/A, the snp-binding regions that completely coincide or significantly overlap with the antigenic regions occupy surface locations and most of them reside in the last half of the H_C domain. But some of the regions reside in the H_N domain and might play a role in the translocation event.     

A Multiple Binary Markov Chain Model for Explaining the Behaviour of Diverse Biological Phenomena

The behaviour of many biological systems can be attributed to that of a large number of units, with each unit swinging between two competing states. During the past few years efforts have been made (e.g., Chung and Kennedy , 1996) to describe such discrete systems using a multiple binary Markov chain model. Here we explore the gamut of these models and classify their behaviour into five qualitatively distinct types, corresponding to subregions of the parameter space. It is suggested that these model behaviours may correspond to behaviours observed in nature. A simple method for fitting the model to data is presented.     

基于氨基酸序列预测蛋白质功能性点突变位点

突变是研究蛋白质结构和功能的重要方法。点突变实验中,突变位点的选择随机性大,若能对突变后蛋白质功能是否发生变化做出预测,将大大减少实验的盲目性。为此,作者设计了一个基于信号处理的单点替换突变预测模型,对序列上每个位点所有可能的氨基酸替换的效果进行估计。使用蛋白质突变数据库(Protein Mutant Database,PMD)里的11个蛋白共2600多个点突变的数据集,对以上模型进行了验证。结果表明正确率高达81.2%,并且推荐出的替换选择位点仅占所有可能替换突变的3.1%。在体外定点突变实验中,使用本模型推荐的高可能性功能突变位点将有助于提高实验的成功率。该模型使用蛋白质的氨基酸序列信息,特别是对未知结构的蛋白质同样适用。然而,由于缺乏足够的突变实验数据,本模型的应用仍需进一步完善和验证。     

Sequence Representation and Prediction of Protein Secondary Structure for Structural Motifs in Twilight Zone Proteins

Characterizing and classifying regularities in protein structure is an important element in uncovering the mechanisms that regulate protein structure, function and evolution. Recent research concentrates on analysis of structural motifs that can be used to describe larger, fold-sized structures based on homologous primary sequences. At the same time, accuracy of secondary protein structure prediction based on multiple sequence alignment drops significantly when low homology (twilight zone) sequences are considered. To this end, this paper addresses a problem of providing an alternative sequences representation that would improve ability to distinguish secondary structures for the twilight zone sequences without using alignment. We consider a novel classification problem, in which, structural motifs, referred to as structural fragments (SFs) are defined as uniform strand, helix and coil fragments. Classification of SFs allows to design novel sequence representations, and to investigate which other factors and prediction algorithms may result in the improved discrimination. Comprehensive experimental results show that statistically significant improvement in classification accuracy can be achieved by: (1) improving sequence representations, and (2) removing possible noise on the terminal residues in the SFs. Combining these two approaches reduces the error rate on average by 15% when compared to classification using standard representation and noisy information on the terminal residues, bringing the classification accuracy to over 70%. Finally, we show that certain prediction algorithms, such as neural networks and boosted decision trees, are superior to other algorithms.This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).     

基于序列拓扑和二阶隐马尔可夫模型的跨膜蛋白亚细胞定位预测

现有蛋白质亚细胞定位方法针对水溶性蛋白质而设计,对跨膜蛋白并不适用。而专门的跨膜拓扑预测器,又不是为亚细胞定位而设计的。文章改进了跨膜拓扑预测器TMPHMMLoc的模型结构,设计了一个新的二阶隐马尔可夫模型;采用推广到二阶模型的Baum-Welch算法估计模型参数,并把将各个亚细胞位置建立的模型整合为一个预测器。数据集上测试结果表明,此方法性能显著优于针对可溶性蛋白设计的支持向量机方法和模糊k最邻近方法,也优于TMPHMMLoc中提出的隐马尔可夫模型方法,是一个有效的跨膜蛋白亚细胞定位预测方法。