蛋白质中三联氨基酸数与二级结构数的模型研究

蛋白质的一级结构或序列与二级结构的关系在蛋白质结构研究中是很重要的,通过建立模型的方法来研究这种关系.在文献中已有的模型(蛋白质一级结构中的二联氨基酸与蛋白质二级结构的模型)的基础上,建立了蛋白质一级结构中的三联氨基酸个数与蛋白质二级结构个数模型.该模型能够较准确地反映蛋白质的一级结构或序列与蛋白质的二级结构的关系,比较适合应用于氨基酸序列长度变化较大的建模数据,同二联氨基酸与二级结构模型比较,由于三联氨基酸含有更多氨基酸之间的耦合信息,该模型的拟合精度更高.由于蛋白质一级结构中的三联氨基酸的种类数很大(为4 200),用以建模的变量数就很大,同时从DSSP数据库得到的样本量也很大(为11 600),用以建模的数据量很大.研究结果表明,PLS变量筛选法是一种建立大数据模型有效的方法,可有效地处理变量数为4 200,样本数为11 600这样大数据量的建模问题.     

Curcin和curcin C与腺嘌呤互作位点的比较分析

麻疯树核糖体失活蛋白curcin和curcin C均具有抗肿瘤活性，但后者的活性水平显著高于前者，为了探索造成这一差异的结构基础，本文采用在线的同源建模预测软件SWISSMODEL，对两种麻疯树核糖体失活蛋白curcin、curcin C的三维结构模型进行预测，采用SYBYL对预测模型进行能量最小化优化，采用PROCHECK、VERIFY 3D和ERRAT软件对优化前后的模型进行质量评估，随后采用AutoDock软件将预测的模型与腺嘌呤进行分子对接分析.结果显示，两种蛋白采用与Ricin A类似的方式与腺嘌呤相互作用，但在相互作用的氨基酸残基种类、数量以及形成的氢键和疏水相互作用上还是存在差异，其中，curcin C与腺嘌呤具有最强的结合能力，curcin则最低.     

基于投票表决特征融合的蛋白质结构类预测

根据氨基酸的物化特性,基于氨基酸组成成分与氨基酸残基指数自相关函数相结合特征提取法,从非同源蛋白质序列中提取7个特征集(COMP、FINA、MAXF、NAKH、BIOV、OOBM、RICJ),采用有先验知识的投票表决特征融合算法融合这7个特征集,对蛋白质结构类进行预测.结果表明,投票表决融合算法的预测总精度及每一类别的预测精度与7个特征集相比较均有不同程度的提高,说明投票表决融合算法在一定程度上能较多地反映蛋白质的空间结构信息.     

利用圆二色谱法研究突变体BFR分子稳定性

通过引入人为突变,研究BFR纳米蛋白分子的稳定性与其一级结构的关系,探索蛋白质序列与结构的关系。选择可能与BFR构象密切相关的氨基酸残基引入突变,经表达、纯化后,利用圆二色谱仪检测突变体蛋白分子的结构变化。结果关键部位氨基酸的突变可以引起BFR分子中α螺旋含量及熔链温度发生了显著的变化。说明某些氨基酸的改变会引起BFR分子整体结构与稳定性的变化,有可能影响甚至改变蛋白质分子的功能。     

蛋白质二级结构预测的结构表达方法研究

蛋白质二级结构预测时,描述窗口内氨基酸残基序列的各种物理化学和分子结构参数的数量非常大,而且不能很好地直接体现氨基酸的连接顺序,这样就造成二级结构预测工作既费时效果又低下,因此,对这些数据预处理是非常必要的.研究发现,这些初始数据对应的变换矩阵的本征值谱与氨基酸残基序列情况无关,只与氨基酸的类别、数量性质有关,而其本征函数数据能够直接反映氨基酸残基的序列情况,利用有显著大小的本征值对应的本征函数作为描述参数,在进行蛋白质二级结构预测时,不但能够显著减少描述参数的个数,而且它体现了氨基酸顺序的变化,这将有效提高蛋白质二级结构预测研究效果.     

生物信息学及其在病毒研究中的应用

介绍生物信息学的原理与方法及其在病毒研究中的应用。保守序列承担着极其重要的功能，通过多重序列对齐搜寻保守序列，是生物信息学方法的基础；敏感位点是一种反映蛋白质或核酸功能的特定模式，因此，通过数量关系的优化推导敏感位点，可用于分离病毒蛋白质与核酸相互作用位点；核苷酸和氨基酸序列只有形成了三级或四级结构才能表现功能，通过同源建模预测蛋白质的高级结构，有助于疫苗的研制、抗病毒药物的筛选以及药物的分子设计。预测RNA的三级结构大多从RNA折叠入手．病毒蛋白质三级结构预测比较成功的是日本脑炎病毒包膜糖蛋白的三级结构。     

基于小波分析法的蛋白质结构研究

用小波的方法预测了一个已知结构蛋白质的二级结构，并把它同互连网蛋白质结构预测服务及其他结构预测软件得到的二级结构进行比较。结果表明：该方法可以较好地确定蛋白质的二级结构且不必进行同源蛋白质序列的联配。在预测未知结构的蛋白质序列方面，该方法与其他方法相比，预测结果并无显著差异，这说明小波分析法可以用于蛋白质结构研究，若与其他方法结合用于结构预测,将会起到更好的作用。     

水稻蛋白质二级结构预测

针对水稻蛋白质二级结构预测研究,查阅了国家水稻数据中心文献资源,基于国际蛋白质数据库(protein data bank, PDB),选择具有代表性的蛋白质(5XQI)作为样本,应用BP神经网络建模技术,对水稻蛋白质二级结构进行预测研究。结果表明：先用氨基酸描述子量化一级结构,再用主成分分析综合描述子,能简化模型结构,提高模拟预测准确度和运行速度;构建标量型的人工神经网络模型和仿真函数预测式,简捷直观,应用方便;适宜的模型结构为21∶20∶3,即21个输入层节点、20个隐含层神经元、3个输出层神元的BP神经网络模型结构;模型的整体拟合准确度为0.85,H、E、C三种二级结构的拟合准确度分别为0.92、0.79、0.81;整体预测准确度为0.72,三种二级结构的预测准确度分别为0.79、0.65、0.71。基于BP神经网络的水稻蛋白质二级结构预测模型的拟合、预测准确度比以往同类研究高,为水稻蛋白质二级结构预测提供了一种新的研究方法。     

面向同源蛋白质探测的一种新型混合深度学习模型

根据蛋白质氨基酸链探测其同源蛋白质,进而预测蛋白质的功能,是生物信息学研究领域的一个重要挑战,也是众多生物医学研究领域的基础研究内容,有着重要的科研价值和广泛的应用需求。其研究难点在于:(1)如何学习对同源蛋白质预测有效、有用的蛋白质特征信息;(2)如何更好地运用蛋白质特征信息,实现同源蛋白质的探测与识别。为了解决同源蛋白质探测与识别研究中的关键难点,本文提出一种基于混合深度学习架构的同源蛋白质探测与识别模型(HDLM-PHP)。通过采用统一的"管道式"深度学习架构,将蛋白质特征学习和探测识别统一为一个整体,提高同源蛋白质探测与识别的效能。采用多组并行的深度卷积神经网络,学习蛋白质的各种属性信息,以期获得丰富的待检测蛋白质和靶蛋白质的高级相关性特征,并通过全连接方式使用多层RBM结构融合和精炼这些相关性特征为全局相关性特征。通过统一的深度网络连接方式,以探测和识别任务为导向,学习到对于同源蛋白质预测最有效、最全面的蛋白质特征信息。在标准数据集SCOPe上,对所提模型进行性能与效率评测,结果表明:本文提出的模型能有效地学习到符合任务导向的蛋白质特征数据,提升同源蛋白质探测与识别的准确度和召回率,优于现有的模型和算法。     

氨基酸的突变及其对蛋白质二级结构的影响

本文对43种蛋白质的肽链进行了统计分析,计算了相邻三个氨基酸的构象参数R_α、R_β、R_c,并将其分类;在此基础上提出了氨基酸的突变对蛋白质二级结构影响的规律。研究了细胞色素C可变氨基酸的突变(不同种属之间)、血红蛋白(人)的异常。给出了所有可能的中间氨基酸可突变为半胱氨酸的氨基酸三联体。     

Atom-by-atom analysis of global downhill protein folding

Protein folding is an inherently complex process involving coordination of the intricate networks of weak interactions that stabilize native three-dimensional structures. In the conventional paradigm, simple protein structures are assumed to fold in an all-or-none process that is inaccessible to experiment. Existing experimental methods therefore probe folding mechanisms indirectly. A widely used approach interprets changes in protein stability and/or folding kinetics, induced by engineered mutations, in terms of the structure of the native protein. In addition to limitations in connecting energetics with structure, mutational methods have significant experimental uncertainties and are unable to map complex networks of interactions. In contrast, analytical theory predicts small barriers to folding and the possibility of downhill folding. These theoretical predictions have been confirmed experimentally in recent years, including the observation of global downhill folding. However, a key remaining question is whether downhill folding can indeed lead to the high-resolution analysis of protein folding processes. Here we show, with the use of nuclear magnetic resonance (NMR), that the downhill protein BBL from Escherichia coli unfolds atom by atom starting from a defined three-dimensional structure. Thermal unfolding data on 158 backbone and side-chain protons out of a total of 204 provide a detailed view of the structural events during folding. This view confirms the statistical nature of folding, and exposes the interplay between hydrogen bonding, hydrophobic forces, backbone conformation and side-chain entropy. From the data we also obtain a map of the interaction network in this protein, which reveals the source of folding cooperativity. Our approach can be extended to other proteins with marginal barriers (less than 3RT), providing a new tool for the study of protein folding.     

Applying genetic algorithms to the study of protein mutation theory

Genetic algorithms were applied to the study of simulation of protein mutation carried out on two_dimensional lattice model; to the study of effects of single mutation and double mutation on protein folding and protein structure stability. It is found that in two_dimensional lattice models, replacement of inner core hydrophobic residue by hydrophilic residue will result in reduction of protein stability; at the same time the number of residues of protein surface relatively grows with increase of protein chain length; and most mutations occur in residues of protein surface, these mutations are all neutral and have no effects on protein natural structure. The two mutations of double mutation are interactive and related to each other.     

Prediction of β -turns using double BP network with novel coding schemes of amino acids

Protein structure prediction is one of the most important problems in structural biology. β-turns are always at the turn of a protein tertiary structure and thus β-turn’s prediction is a key step in tertiary structure prediction. There are some methods to predict β-turns based on machine learning techniques such as k-nearest method, neural networks and support vector machine. In this paper, we construct a classifier using double BP networks and put forward two novel methods to code amino acids in the second network. When trained and tested on different datasets, they achieve more accuracy than other coding methods.      

人类乙型肝炎样病毒核心蛋白中第7位疏水性氨基酸重复肚段区域的突变可以抑制病毒核衣壳的组装

人类乙型肝炎病毒的核衣壳由核心蛋白的二聚体所组成．但是,核心蛋白亚单位与亚单位之间相互作用的机制至今尚不清楚．研究发现,在人类乙型肝炎样病毒──土拨鼠肝炎病毒（WHV）核心蛋白的氨基端,存在着4个保守的疏水氨基酸残基（氨基酸位置101～102）．它们分别是亮氨酸101,亮氨酸108,缬氨酸115和苯丙氨酸122．这4个疏水氨基酸残基以每隔6个氨基酸残基而重复出现1次．它们被称为“第7位疏水性氨基酸重复肽段（hhr）”．由于蛋白质中的疏水键往往在蛋白质的相互作用中起重要作用,因此就在培养细胞系统中研究WHV核心蛋白的hhr区域在…     

A surprising simplicity to protein folding

The polypeptide chains that make up proteins have thousands of atoms and hence millions of possible inter-atomic interactions. It might be supposed that the resulting complexity would make prediction of protein structure and protein-folding mechanisms nearly impossible. But the fundamental physics underlying folding may be much simpler than this complexity would lead us to expect folding rates and mechanisms appear to be largely determined by the topology of the native (folded) state, and new methods have shown great promise in predicting protein-folding mechanisms and the three-dimensional structures of proteins.     

Relationships of mRNA-protein secondary structures in the human β-globin gene HBB and four variants

Single nucleotide polymorphism is an interesting problem that can alter gene expression,recode amino acids and affect protein function.Protein structural changes have generally been attributed to amino acid replacements,and only a few research efforts have examined the effects of mRNA structural changes to the conformation of the corresponding protein coded by the mRNA.In the present study,the human β-globin HBB gene and four variants were examined.The mRNA secondary structures were constructed using the dynamic extended folding method and the encoded protein secondary structures were obtained from related databases.Comparisons were performed between these structures before and after mutations were introduced into the mature mRNAs and the proteins.We focused on the structural changes from mRNA to protein and found that regular protein conformations tend to match stable mRNA regions,whereas irregular protein conformations,such as β/γ turns and random coils,often match unstable mRNA regions.Mutations within unstable regions can alter the mRNA secondary structure and leave footprints in the protein structure.Comparison of the mRNA-protein secondary structure relationships represents a potential strategy to explore protein functional changes.     

A New Hidden Markov Model for Protein Quality Assessment Using Compatibility Between Protein Sequence and Structure

Protein structure Quality Assessment（QA） is an essential component in protein structure prediction and analysis. The relationship between protein sequence and structure often serves as a basis for protein structure QA.In this work, we developed a new Hidden Markov Model（HMM） to assess the compatibility of protein sequence and structure for capturing their complex relationship. More specifically, the emission of the HMM consists of protein local structures in angular space, secondary structures, and sequence profiles. This model has two capabilities：（1） encoding local structure of each position by jointly considering sequence and structure information, and（2）assigning a global score to estimate the overall quality of a predicted structure, as well as local scores to assess the quality of specific regions of a structure, which provides useful guidance for targeted structure refinement. We compared the HMM model to state-of-art single structure quality assessment methods OPUSCA, DFIRE, GOAP,and RW in protein structure selection. Computational results showed our new score HMM.Z can achieve better overall selection performance on the benchmark datasets.     

A cluster of cystic fibrosis mutations in the first nucleotide-binding fold of the cystic fibrosis conductance regulator protein.

The gene responsible for cystic fibrosis (CF) has recently been identified and is predicted to encode a protein of 1,480 amino acids called the CF transmembrane conductance regulator (CFTR). Several functional regions are thought to exist in the CFTR protein, including two areas for ATP-binding, termed nucleotide-binding folds (NBFs), a regulatory (R) region that has many possible sites for phosphorylation by protein kinases A and C, and two hydrophobic regions that probably interact with cell membranes. The most common CF gene mutation leads to omission of phenylalanine residue 508 in the putative first NBF, indicating that this region is functionally important. To determine whether other mutations occur in the NBFs of CFTR, we determined the nucleotide sequences of exons 9, 10, 11 and 12 (encoding the first NBF) and exons 20, 21 and 22 (encoding most of the second NBF) from 20 Caucasian and 18 American-black CF patients. One cluster of four mutations was discovered in a 30-base-pair region of exon 11. Three of these mutations cause amino-acid substitutions at residues that are highly conserved among the CFTR protein, the multiple-drug-resistance proteins and ATP-binding membrane-associated transport proteins. The fourth mutation creates a premature termination signal. These mutations reveal a functionally important region in the CFTR protein and provide further evidence that CFTR is a member of the family of ATP-dependent transport proteins.     

Modest stabilization by most hydrogen-bonded side-chain interactions in membrane proteins

Understanding the energetics of molecular interactions is fundamental to all of the central quests of structural biology including structure prediction and design, mapping evolutionary pathways, learning how mutations cause disease, drug design, and relating structure to function. Hydrogen-bonding is widely regarded as an important force in a membrane environment because of the low dielectric constant of membranes and a lack of competition from water. Indeed, polar residue substitutions are the most common disease-causing mutations in membrane proteins. Because of limited structural information and technical challenges, however, there have been few quantitative tests of hydrogen-bond strength in the context of large membrane proteins. Here we show, by using a double-mutant cycle analysis, that the average contribution of eight interhelical side-chain hydrogen-bonding interactions throughout bacteriorhodopsin is only 0.6 kcal mol(-1). In agreement with these experiments, we find that 4% of polar atoms in the non-polar core regions of membrane proteins have no hydrogen-bond partner and the lengths of buried hydrogen bonds in soluble proteins and membrane protein transmembrane regions are statistically identical. Our results indicate that most hydrogen-bond interactions in membrane proteins are only modestly stabilizing. Weak hydrogen-bonding should be reflected in considerations of membrane protein folding, dynamics, design, evolution and function.     

A new way of enhancing the thermostability of proteases

Thermostability of enzyme can be enhanced by single amino acid substitutions. Recent advances in genetic engineering have made it possible to create novel proteins in a predictable manner where structural information for the protein is available. This 'protein engineering' has already been used to enhance enzyme thermostability, but it is usually not clear which amino acid substitutions should be made. We consider that the following approach should be helpful in engineering proteins with enhanced thermostability: highly conserved residues should be left unchanged; the sequences of known mesophilic and thermophilic proteins should be used to suggest the kinds of changes likely to increase thermostability; and substitutions should be made that increase internal hydrophobicity and that stabilize helices for strong internal packing. We describe here the use of this approach to alter the thermostability of the thermostable neutral protease of Bacillus stearothermophilus, the sequence of which is known. Surprisingly we find that a single mutation that decreases thermostability can require two mutations that increase stability to compensate for it. The effects on stability are not additive, suggesting cooperativity.