低同源性蛋白质结构预测

基因组计划在实施产生了大量的DNA序列信息,如何有效地利用这些信息来研究基因的产物－蛋白质的结构与功能成为引入注目的研究领域,同源蛋白质结构预测及蛋白质折工识别是在基因组水平上进行蛋白质结构预测的有效方法,酵母基因组中约有50％的基因可以通过这类方法来确定其表面产物蛋白质的结构[1],但是目前所采用的方法在低同源性蛋白质的结构预测方面尚存在较大困难。     

深度学习在蛋白质结构预测中的应用及启示

蛋白质结构预测通常指借助计算机计算模拟方法从氨基酸序列推断其三维空间结构.而空间结构决定其生理功能,故结构预测问题尤为重要.基于单纯物理学的预测仅能应对较短蛋白质且精度不高.而基于数据驱动和生物信息学的方法近十多年备受重视.本文主要回顾近十多年来深度学习在蛋白质预测领域的应用,重点介绍Deepmind团队的AlphaFold方法,此方法预测在单域蛋白质达到了中低分辨率实验精度,一定程度上解决了困扰人们五十多年的蛋白质结构预测难题.     

一种3D打印蛋白质模型的方法及其在教学中的应用

本文报道了一种3D打印蛋白质模型的方法,得到了一系列不同的蛋白质模型。这些模型可作为可视化教具,帮助学生理解蛋白质多级结构、蛋白质折叠、蛋白质-蛋白质相互作用等相关知识点,同时也可作为艺术展示教具应用于科学交流与科普宣传。     

基于序列特征筛选与支持向量回归预测蛋白质折叠速率

折叠速率预测对阐明蛋白质折叠机理意义重大.本文收集了115条目前已知折叠速率的蛋白质样本(包括二态、多态和混态蛋白),为了较全面地表征蛋白质分子的一级结构信息,提取序列长度、氨基酸残基多尺度组分、成对残基k-space特征与基于残基物理化学性质的地统计学关联总共9357维特征.经改进的二元矩阵重排过滤器和多轮末尾淘汰非线性筛选,获得23个物理化学意义明确的保留特征,建立的非线性支持向量回归模型Jackknife交叉验证的相关系数R=0.95,优于文献报道及其他参比特征选择方法.支持向量回归解释体系表明折叠速率与保留描述符的非线性回归极显著,分析了各保留描述符对折叠速率的影响,结果表明蛋白质折叠速率与序列长度、中短程关联特征、三联体残基组份特征等密切相关.     

天然无序蛋白质:序列-结构-功能的新关系

天然无序蛋白质是一类新发现的蛋白质,它们在天然条件下没有确定的三维结构,却具有正常的生物学功能,广泛参与信号传递、DNA转录、细胞分裂和蛋白质聚集等重要的生理与病理过程.无序蛋白质的发现是对传统的蛋白质"序列-结构-功能"范式的挑战.在这篇综述里,我们首先回顾了蛋白质的传统范式以及无序蛋白质的发现过程,然后介绍无序蛋白质在结构、序列、功能等方面的特征与相互作用,并以分子识别过程为例,进一步阐述目前国际上对无序蛋白质所具有优势的一些认识与观点.我们还分析了无序蛋白质研究在生命科学和医学等领域的应用前景,并介绍了国内在无序蛋白质领域的研究现状.     

依据氨基酸残基的相关性预测蛋白质的结构类型

作为蛋白质的建筑构件,各种类型的蛋白质的20种氨基酸残基之间存在着特定的相互关联,反映了氨基酸残基之间的制约性,并有深刻的物理和化学的内在因素.某些氨基酸残基对之间的相关系数可以作为一种类型的蛋白质区别于其它类型蛋白质的特征,用于蛋白质结构类型的预测.研究了4种类型的蛋白质204个样品的氨基酸残基对的相关性系数,找出了可作为蛋白质结构类型特征的氨基酸残基的相关对,并用于蛋白质结构类型的预测,对于α型、β型、α/β型和α＋β型蛋白质的204个蛋白质样品的交叉测试,正确率分别为94％、89％、79％和89％,平均为88％,高于简单距离法和欧几里德距离法.     

蛋白质二级结构预测的人工神经网络方法研究

本文比较了五种神经网络方法预测蛋白质二级结构的准确率,并做出初步评价。五种神经网络分别是:误差反传前向网络(BP),径向基函数网络(RBF),广义回归神经网络(GRNN),串并联叠层网络(CF),Elman网络(ELM)。结果显示:GRNN的预测准确率达85.7%,优于其它网络。本文还讨论了训练集样本数及参数的优化对GRNN预测准确率的影响。     

基于改进遗传算法的蛋白质三维折叠模拟

根据氨基酸的序列预测蛋白质的空间结构在基因治疗药物分子设计等方面有巨大的潜在应用价值.本研究基于HP格子模型利用改进的遗传算法预测了蛋白质的三维空间结构.改进的遗传算法引入了克隆体数量限制策略、巢穴竞争选择策略及局部优化策略等.实验结果表明,改进的遗传算法显著地提高了蛋白质结构的预测效率,模拟的蛋白质结构紧凑,更接近真实蛋白质的构型.     

蛋白质结构的“限域下最低能量结构片段”假说与蛋白质进化的“石器时代”

蛋白质折叠问题被称为第二遗传密码,至今未破译;蛋白质序列的天书仍然是"句读之不知,惑之不解"。在最近工作的基础上,我们提出了蛋白质结构的"限域下最低能量结构片段"假说。这一假说指出,蛋白质中存在一些关键的长程强相互作用位点,这些位点相当于标点符号,将蛋白质序列的天书变成可读的句子(多肽片段)。这些片段的天然结构是在这些强长程相互作用位点限域下的能量最低状态。完整的蛋白质结构由这些"限域下最低能量结构片段"拼合而成,而蛋白质整体结构并不一定是全局性的能量最低状态。在蛋白质折叠过程中,局部片段的天然结构倾向性为强长程相互作用的形成提供主要基于焓效应的驱动力,而天然强长程相互作用的形成为局部片段的天然结构提供主要基于熵效应的稳定性。在蛋白质进化早期,可能存在一个"石器时代",即依附不同界面(比如岩石)的限域作用而稳定的多肽片段先进化出来,后由这些片段逐步进化(包括拼合)而成蛋白质。     

几种过渡态结构的有效优化方法

通过双桥反应机理、电环合反应和催化反应三个不同类型的过渡态优化，说明当标准方法难以给出结果时，对物理问题本身的分析有助于给出过渡态优化的线索．第一个例子根据化学问题给出限制条件，通过平衡几何构型优化方法优化得到过渡态；第二个例子是在使用标准过渡态优化方法失败后，根据物理问题从反应途径上用平衡几何构型优化方法选择过渡态优化的初始结构；第三个例子通过Gaussian 98程序中的标准方法QST2直接得到过渡态．     

Protein structure optimization using improved simulated annealing algorithm on a three-dimensional AB off-lattice model

This paper proposed an improved simulated annealing (ISA) algorithm for protein structure optimization based on a three-dimensional AB off-lattice model. In the algorithm, we provided a general formula used for producing initial solution, and designed a multivariable disturbance term, relating to the parameters of simulated annealing and a tuned constant, to generate neighborhood solution. To avoid missing optimal solution, storage operation was performed in searching process. We applied the algorithm to test artificial protein sequences from literature and constructed a benchmark dataset consisting of 10 real protein sequences from the Protein Data Bank (PDB). Otherwise, we generated C_α space-filling model to represent protein folding conformation. The results indicate our algorithm outperforms the five methods before in searching lower energies of artificial protein sequences. In the testing on real proteins, our method can achieve the energy conformations with C_α-RMSD less than 3.0 Å from the PDB structures. Moreover, C_α space-filling model may simulate dynamic change of protein folding conformation at atomic level.     

Protein Folding: A Perspective from Theory and Experiment

The mechanism of protein folding (represented schematically below) is one of the most fascinating problems in the field of chemical reactions. This review presents the progess made recently in understanding key elements of this reaction and describes a solution to the often quoted Levinthal Paradox.     

The Wako-Saitô-Muñoz-Eaton Model for Predicting Protein Folding and Dynamics

Despite the recent advances in the prediction of protein structures by deep neutral networks, the elucidation of protein-folding mechanisms remains challenging. A promising theory for describing protein folding is a coarse-grained statistical mechanical model called the Wako-Saitô-Muñoz-Eaton (WSME) model. The model can calculate the free-energy landscapes of proteins based on a three-dimensional structure with low computational complexity, thereby providing a comprehensive understanding of the folding pathways and the structure and stability of the intermediates and transition states involved in the folding reaction. In this review, we summarize previous and recent studies on protein folding and dynamics performed using the WSME model and discuss future challenges and prospects. The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results. Furthermore, extended versions of the WSME model were applied to predict the folding mechanisms of multi-domain proteins and the conformational changes associated with protein function. Thus, the WSME model may contribute significantly to solving the protein-folding problem and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications.     

Protein fold recognition

Summary An important, yet seemingly unattainable, goal in structural molecular biology is to be able to predict the native three-dimensional structure of a protein entirely from its amino acid sequence. Prediction methods based on rigorous energy calculations have not yet been successful, and best results have been obtained from homology modelling and statistical secondary structure prediction. Homology modelling is limited to cases where significant sequence similarity is shared between a protein of known structure and the unknown. Secondary structure prediction methods are not only unreliable, but also do not offer any obvious route to the full tertiary structure. Recently, methods have been developed whereby entire protein folds are recognized from sequence, even where little or no sequence similarity is shared between the proteins under consideration. In this paper we review the current methods, including our own, and in particular offer a historical background to their development. In addition, we also discuss the future of these methods and outline the developments under investigation in our laboratory.     

Refolding of Cold-Denatured Barstar Induced by Radio-Frequency Heating: A New Method to Study Protein Folding by Real-Time NMR Spectroscopy

The C40A/C82A double mutant of barstar has been shown to undergo cold denaturation above the water freezing point. By rapidly applying radio-frequency power to lossy aqueous samples, refolding of barstar from its cold-denatured state can be followed by real-time NMR spectroscopy. Since temperature-induced unfolding and refolding is reversible for this double mutant, multiple cycling can be utilized to obtain 2D real-time NMR data. Barstar contains two proline residues that adopt a mix of cis and trans conformations in the low-temperature-unfolded state, which can potentially induce multiple folding pathways. The high time resolution real-time 2D-NMR measurements reported here show evidence for multiple folding pathways related to proline isomerization, and stable intermediates are populated. By application of advanced heating cycles and state-correlated spectroscopy, an alternative folding pathway circumventing the rate-limiting cis-trans isomerization could be observed. The kinetic data revealed intermediates on both, the slow and the fast folding pathway.     

Neural Networks Predict Protein Folding and Structure: Artificial Intelligence Faces Biomolecular Complexity

Abstract

In the genomic era DNA sequencing is increasing our knowledge of the molecular structure of genetic codes from bacteria to man at a hyperbolic rate. Billions of nucleotides and millions of aminoacids are already filling the electronic files of the data bases presently available, which contain a tremendous amount of information on the most biologically relevant macromolecules, such as DNA. RNA and proteins. The most urgent problem originates from the need to single out the relevant information amidst a wealth of general features. Intelligent tools are therefore needed to optimise the search. Data mining for sequence analysis in biotechnology has been substantially aided by the development of new powerful methods borrowed from the machine learning approach. In this paper we discuss the application of artificial feedforward neural networks to deal with some fundamental problems tied with the folding process and the structure-function relationship in proteins.