基于序列疏水值震荡的折叠速率预测

蛋白质折叠速率的正确预测对理解蛋白质的折叠机理非常重要。本文从伪氨基酸组成的方法出发,提出利用序列疏水值震荡的方法来提取蛋白质氨基酸的序列顺序信息,建立线性回归模型进行折叠速率预测。该方法不需要蛋白质的任何二级结构、三级结构信息或结构类信息,可直接从序列对蛋白质折叠速率进行预测。对含有62个蛋白质的数据集,经过Jack．knife交互检验验证,相关系数达到0．804,表示折叠速率预测值与实验值有很好的相关性,说明了氨基酸序列信息对蛋白质折叠速率影响重要。同其他方法相比,本文的方法具有计算简单,输入参数少等特点。     

从氨基酸序列预测蛋白质折叠速率

蛋白质折叠速率预测是当今生物物理学最具挑战性的课题之一．近年来,许多科研工作者开展了大量的研究工作来探索折叠速率的决定因素,许多参数和方法被相继提出．但氨基酸残基间的相互作用、氨基酸的序列顺序等信息对折叠速率的影响从未被提及．采用伪氨基酸组成的方法提取氨基酸的序列顺序信息,利用蒙特卡洛方法选择最佳特征因子,建立线性回归模型进行折叠速率预测．该方法能在不需要任何(显示)结构信息的情况下,直接从蛋白质的氨基酸序列出发对折叠速率进行预测．在Jackknife交互检验方法的验证下,对含有99个蛋白质的数据集,发现折叠速率的预测值与实验值有很好的相关性,相关系数能达到0.81,预测误差仅为2.54．这一精度明显优于其他基于序列的方法,充分说明蛋白质的序列顺序信息是影响蛋白质折叠速率的重要因素．     

残基相互作用网络特征与木聚糖酶耐热性的关系

残基相互作用网络是体现蛋白质中残基与残基之间协同和制约关系的重要形式。残基相互作用网络的拓扑性质以及社团结构与蛋白质的功能和性质有密切的关系。本文在构建一系列耐热木聚糖酶和常温木聚糖酶的残基相互作用网络后,通过计算网络的度、聚类系数、连接强度、特征路径长度、接近中心性、介数中心性等拓扑参数来确定网络拓扑结构与木聚糖酶耐热性的关系。识别残基相互作用网络的hub点,分析hub点的亲疏水性、带电性以及各种氨基酸在hub点中所占的比例。进一步使用GA-Net算法对网络进行社团划分,并计算社团的规模、直径和密度。网络的高平均度、高连接强度、以及更短的最短路径等表明耐热木聚糖酶残基相互作用网络的结构更加紧密;耐热木聚糖酶网络中的hub节点比常温木聚糖酶网络hub节点具有更多的疏水性残基,hub点中Phe、Ile、Val的占比更高。社团检测后发现,耐热木聚糖酶网络拥有更大的社团规模、较小的社团直径和较大的社团密度。社团规模越大表明耐热木聚糖酶的氨基酸残基更倾向于形成大的社团,而较小的社团直径和较大的社团密度则表明社团内部氨基酸残基的相互作用比常温木聚糖酶更强。     

残基相互作用网络特征与木聚糖酶耐热性的关系

残基相互作用网络是体现蛋白质中残基与残基之间协同和制约关系的重要形式。残基相互作用网络的拓扑性质以及社团结构与蛋白质的功能和性质有密切的关系。本文在构建一系列耐热木聚糖酶和常温木聚糖酶的残基相互作用网络后,通过计算网络的度、聚类系数、连接强度、特征路径长度、接近中心性、介数中心性等拓扑参数来确定网络拓扑结构与木聚糖酶耐热性的关系。识别残基相互作用网络的hub点,分析hub点的亲疏水性、带电性以及各种氨基酸在hub点中所占的比例。进一步使用GA-Net算法对网络进行社团划分,并计算社团的规模、直径和密度。网络的高平均度、高连接强度、以及更短的最短路径等表明耐热木聚糖酶残基相互作用网络的结构更加紧密;耐热木聚糖酶网络中的hub节点比常温木聚糖酶网络hub节点具有更多的疏水性残基,hub点中Phe、Ile、Val的占比更高。社团检测后发现,耐热木聚糖酶网络拥有更大的社团规模、较小的社团直径和较大的社团密度。社团规模越大表明耐热木聚糖酶的氨基酸残基更倾向于形成大的社团,而较小的社团直径和较大的社团密度则表明社团内部氨基酸残基的相互作用比常温木聚糖酶更强。     

蛋白质氨基酸网络研究进展

氨基酸网络是运用复杂网络工具对蛋白质结构-功能关系研究的新方法。本文回顾了氨基酸网络中常用网络参量的计算方法,如：度分布,聚集系数,平均最短路径等。结合本研究小组的工作,介绍了常用的网络构建和分析方法,并总结了氨基酸网络在蛋白质折叠以及蛋白质分子对接问题中的应用。最后,分析了氨基酸网络研究目前存在的主要问题,并对未来的工作进行了展望。     

蛋白质氨基酸网络研究进展

氨基酸网络是运用复杂网络工具对蛋白质结构-功能关系研究的新方法。本文回顾了氨基酸网络中常用网络参量的计算方法,如:度分布,聚集系数,平均最短路径等。结合本研究小组的工作,介绍了常用的网络构建和分析方法,并总结了氨基酸网络在蛋白质折叠以及蛋白质分子对接问题中的应用。最后,分析了氨基酸网络研究目前存在的主要问题,并对未来的工作进行了展望。     

从胰岛素研究看等构相互作用在分子识别中的作用

蛋白质分子识别的结构基础是蛋白质的三维结构或折叠,其信息存在于氨基酸序列中。在比较功能已知或未知蛋白质的结构同源性时,常常是根据氨基酸残基的疏水性或亲水性。胰岛素定位突变的研究显示出了“等构相互作用”＾＃在考虑蛋白质同源性中的重要性。“等构相互作用”这一概念使得蛋白质结构与功能研究的视野更加开阔,同时在制药工业中开发多肽与蛋白质的类似物或模拟物有更多的选择。     

氨基酸的亲、疏水性研究 Ⅰ.各种氨基酸及其残基的疏水性标度

蛋白质由20种氨基酸组成。它们的亲、疏水性的相互作用是维持蛋白质三级结构最重要的作用力之一。有些变性的蛋白质(即随机松散分布的多肽链)能够自动地重新折叠起来,成为具有生物活性、高度有序的天然构象,就是疏水基起着关键作用。因此,研究各种氨基酸侧链亲水性或疏水性的大小及其相互作用(该方面的工作是七十年代才开展起来的),对于深入了解蛋白质、核酸等生物大分子的结构与功能,具有重要意义。     

由残基计算蛋白质的可及表面积

蛋白质的高级结构是由一级结构决定的,这就是说,蛋白质中氨基酸的排列顺序及其相互作用决定了蛋白质的结构和功能。本文从这一基本原则出发,分析并计算了氨基酸残基在折叠和非折叠状态下的暴露和埋藏,得到了一种计算蛋白质可及表面积的新方法。     

最小Hamilton路算法在蛋白质结构预测中的应用

本文对蛋白质loop结构进行了反向研究,即对由n个残基构成的loop已知其空间结构,求匹配的n个氨基酸残基序列.把loop的3D信息转化为一个加权完全图Kn模型,然后求加权Kn图的最小Hamilton路.这条H路对应与寻找一个氨基酸残基序列,使该序列能够折叠成这个立体结构模型.根据Bayesian定律得到一个加权表,应用对loop的预测问题,取得预期的结果.     

Assortative mixing in Protein Contact Networks and protein folding kinetics

MOTIVATION: Starting from linear chains of amino acids, the spontaneous folding of proteins into their elaborate 3D structures is one of the remarkable examples of biological self-organization. We investigated native state structures of 30 single-domain, two-state proteins, from complex networks perspective, to understand the role of topological parameters in proteins' folding kinetics, at two length scales--as 'Protein Contact Networks (PCNs)' and their corresponding 'Long-range Interaction Networks (LINs)' constructed by ignoring the short-range interactions. RESULTS: Our results show that, both PCNs and LINs exhibit the exceptional topological property of 'assortative mixing' that is absent in all other biological and technological networks studied so far. We show that the degree distribution of these contact networks is partly responsible for the observed assortativity. The coefficient of assortativity also shows a positive correlation with the rate of protein folding at both short- and long-contact scale, whereas, the clustering coefficients of only the LINs exhibit a negative correlation. The results indicate that the general topological parameters of these naturally evolved protein networks can effectively represent the structural and functional properties required for fast information transfer among the residues facilitating biochemical/kinetic functions, such as, allostery, stability and the rate of folding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Towards an integrated understanding of the structural characteristics of protein residue networks

A protein residue network or PRN is a network induced by spatial contacts between amino acid residues of a protein. Studies of the structure of PRNs have revealed a list of network characteristics common to a diverse class of proteins. Explanations for the observed network characteristics for protein folding have been suggested but not tested in an integrated way. In this article, in silico experiments are performed to understand how structural characteristics of PRNs influence protein folding as modeled by a search problem. We find that the blend of structural characteristics PRNs possess help to place them in a sweet spot within the space of all network configurations tested. PRNs are plausible 3D structures and yield competitive search performances. Hence, it appears that PRNs are in a form suited to the function they evolved into. However, this conclusion is partially contingent upon the fitness function preferentially satisfying short-range links but also allowing short- and long-range interactions to cooperate towards the satisfaction of all links. We close with a discussion on the rather intricate interplay among the three main structural characteristics of PRNs, i.e., clustering, average path length, and assortativity, and their impact on search performance and 3D structure plausibility.     

The influence of protein coding sequences on protein folding rates of all-β proteins

It is currently believed that the protein folding rate is related to the protein structures and its amino acid sequence. However, few studies have been done on the problem that whether the protein folding rate is influenced by its corresponding mRNA sequence. In this paper, we analyzed the possible relationship between the protein folding rates and the corresponding mRNA sequences. The content of guanine and cytosine (GC content) of palindromes in protein coding sequence was introduced as a new parameter and added in the Gromiha's model of predicting protein folding rates to inspect its effect in protein folding process. The multiple linear regression analysis and jack-knife test show that the new parameter is significant. The linear correlation coefficient between the experimental and the predicted values of the protein folding rates increased significantly from 0.96 to 0.99, and the population variance decreased from 0.50 to 0.24 compared with Gromiha's results. The results show that the GC content of palindromes in the corresponding protein coding sequence really influences the protein folding rate. Further analysis indicates that this kind of effect mostly comes from the synonymous codon usage and from the information of palindrome structure itself, but not from the translation information from codons to amino acids.     

Mechanism of protein folding: I. General considerations and refolding of myoglobin

To explain the rapidity of the process of protein folding, we cite two aspects of hydrophobic interaction: its long-range nature and the specificity of pairing after the formation of secondary structures. These two factors, when incorporated with the growth-type mechanism, can determine the folding pathway of proteins. This mechanism is applied to myoglobin. Appropriate introduction of side chains of amino acid residues and the heme group attached to His 93 yield a refolded tertiary structure that is in good agreement with the native structure.     

Analysis on folding of misgurin using two-dimensional HP model

Misgurin is an antimicrobial peptide from the loach, while the hydrophobic-polar (HP) model is a way to study the folding conformations and native states in peptide and protein although several amino acids cannot be classified either hydrophobic or polar. Practically, the HP model requires extremely intensive computations, thus it has yet to be used widely. In this study, we use the two-dimensional HP model to analyze all possible folding conformations and native states of misgurin with conversion of natural amino acids according to the normalized amino acid hydrophobicity index as well as the shortest benchmark HP sequence. The results show that the conversion of misgurin into HP sequence with glycine as hydrophobic amino acid at pH 2 has 1212 folding conformations with the same native state of minimal energy -6; the conversion of glycine as polar amino acid at pH 2 has 13,386 folding conformations with three native states of minimal energy -5; the conversion of glycine as hydrophobic amino acid at pH 7 has 2538 folding conformations with three native states of minimal energy -5; and the conversion of glycine as polar amino acid at pH 7 has 12,852 folding conformations with three native states of minimal energy -4. Those native states can be ranked according to the normalized amino acid hydrophobicity index. The detailed discussions suggest two ways to modify misgurin.     

Native geometry and the dynamics of protein folding

In this paper, we investigate the role of native geometry on the kinetics of protein folding based on simple lattice models and Monte Carlo simulations. Results obtained within the scope of the Miyazawa-Jernigan indicate the existence of two dynamical folding regimes depending on the protein chain length. For chains larger than 80 amino acids, the folding performance is sensitive to the native state's conformation. Smaller chains, with less than 80 amino acids, fold via two-state kinetics and exhibit a significant correlation between the contact order parameter and the logarithmic folding times. In particular, chains with N=48 amino acids were found to belong to two broad classes of folding, characterized by different cooperativity, depending on the contact order parameter. Preliminary results based on the Go model show that the effect of long-range contact interaction strength in the folding kinetics is largely dependent on the native state's geometry.     

An analysis of non-bonded energy of proteins

Non-bonded energy of 16 proteins was calculated using the atomic co-ordinates obtained by X-ray crystallography. The curve of total energy against the number of atoms in proteins is approximately linear with a slight concaved shape. According to a linear equation to fit the curve, the extrapolated length of a polypeptide chain of a globular shape is expected to be 18 residues, which corresponds conceivably to an approximate size of nucleus for a folding of the polypeptide chain. Contributions from short-range and medium-range energies are always much greater than those from long-range energy for all the proteins and there seems to exist a change of each contribution in a range from 1200 to 1700 atoms. The energies with a lag less than four residues are a major part of the total energy and the contribution of energy from main-chain atoms is considerably higher than that from side-chain atoms. Side-chain atoms of a residue have a tendency to interact more strongly with main-chain atoms of N-terminal, than with those of C-terminal side of the residue, indicating asymmetry of the interaction in a protein. Amino acid residues in proteins may be divided into three groups by the order of strength of average energy. The first group exhibiting strong interaction consists mainly of hydrophobic amino acids and the third group consists of hydrophilic ones corresponding to the location in a protein molecule. Cys, val, leu and met are important for medium-range and long-range energies; gly and ala for medium-range energy; ile, trp, phe, tyr and arg for long-range energy. One simple application of the average energy of amino acid residues is illustrated to estimate local energy of a segment of nine residues given by a protein sequence. There is a good correlation between the curve computed by the average energy and the experimental curve for myoglobin.