SARS病毒S蛋白三维结构预测

蛋白质结构类型识别方法可以在没有序列同源性的蛋白质之间检测有没有结构相似性。利用蛋白质结构类型识别方法预测了SARS病毒S蛋白N端区域的结构。模建的SARS病毒S蛋白N端区域是一个全折叠的结构。     

白色念珠菌羊毛甾醇14α—去甲基化酶三维结构分子？…

基于四种原核细胞色素Ｐ４５０晶体蛋白Ｐ４５０ＢＭ３、Ｐ４５０ｃａｍ、Ｐ４５０ｔｅｒｐ、４５０ｅｒｙＦ模建白色念珠菌羊毛甾醇１４α－去甲基化酶三维结构。序列匹配采用四种晶体结构比较结果基础之上提出的细胞色素Ｐ４５０超家族蛋白基于结构知识的序列匹配方法。以Ｐ４５０ＢＭ３晶体结构坐标模建目标蛋白结构保守区主链结构,结构保守区侧链构象来源于四种晶体蛋白与模建蛋白对应残基同源性得分最高残基构象。建模结果用分     

α-葡萄糖苷酶三维结构的同源模建与分子设计

目的:该文预测了来源于嗜热脂肪芽孢杆菌(Bacillus stearothermophilus)ATCC 12016编码α-葡萄糖苷酶序列的三维结构,设计突变位点,构建突变体模型,并对预测三维结构与突变体进行了评估与分析.方法:分析了α-葡萄糖苷酶的核酸序列与蛋白序列,确定了α-葡萄糖苷酶蛋白序列的同源性与保守区特征;利用来源于蜡状芽胞杆菌(Bacillus cereus)ATCC 7064寡聚-1,6-葡萄糖苷酶三维蛋白结构作为模板,同时基于同源模建方法对α-葡萄糖苷酶序列的三维结构与突变突变体模型进行了结构预测.结果:对预测的三维结构与突变体进行评估与分析,表明预测与设计的结构达到合理化标准.结论:基于以上研究结果,构建α-葡萄糖苷酶的三维结构模型是合理的,为蛋白质工程应用建立了理论研究平台.     

基于小波分析的膜蛋白跨膜区段序列分析和预测

膜蛋白是一类结构独特的蛋白质,在各种细胞中普遍存在,发挥着重要的生理功能。目前仅有少数膜蛋白听结构被实验测出,因此用计算机预测膜蛋白的结构是蛋白质结构预测的主要研究内容之一。膜蛋白一般在膜上形成保守的跨膜螺旋结构,序列特征明显,比较适合用预测的方法确定跨膜螺旋区段的位置。国际上已有一些研究者用人工神经网络方法、多序列比对方法和统计方法进行了预测尝试,取得了一定的成功经验。我们对蛋白质序列数据库中的     

基于模板的蛋白质结构预测

基于模板的蛋白结构预测和不依赖模板的蛋白结构预测是计算预测蛋白质三维结构的两种方法,前者由于具有快速和较高准确性的优点,而得到了广泛的应用.基于模板的结构预测是通过寻找与目标蛋白序列相似并且有实验测定的结构作为模板,进而构建目标序列的结构模型的方法.文章详细综述了基于模板的结构预测方法的步骤、关键环节,并对影响结构预测...     

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.     

白色念珠菌羊毛甾醇14α-去甲基化酶三维结构分子模型研究

基于四种原核细胞色素Ｐ４５０晶体蛋白Ｐ４５０ＢＭ３、Ｐ４５０ｃａｍ、Ｐ４５０ｔｅｒｐ、Ｐ４５０ｅｒｙＦ模建白色念珠菌羊毛甾醇１４α－去甲基化酶三维结构。序列匹配采用四种晶体结构比较结果基础之上提出的细胞色素Ｐ４５０超家族蛋白基于结构知识的序列匹配方法。以Ｐ４５０ＢＭ３晶体结构坐标模建目标蛋白结构保守区主链结构,结构保守区侧链构象来源于四种晶体蛋白与模建蛋白对应残基同源性得分最高残基构象。模建结果用分子力学和分子动力学进行结构优化,模建结果蛋白采用Ｐｒｏｆｉｌｅ－３Ｄ图、Ｒａｍａｃｈａｎｄｒａｎ图和疏水图分析确证结构的合理性。并根据模型推测与血红素辅基相互作用的残基、与氧化还原偶联蛋白作用和参与电子传递的残基、底物进出通道和活性位点的残基。这些研究结果为定点突变研究、抗多肽抗体结合实验等提供理论依据,为高效低毒抗真菌药物合理设计提供靶标。     

赖型钩端螺旋体外膜蛋白基因结构比较性研究

用ＰＣＲ方法扩增不同毒力赖型钩体ＯｍｐＬ１基因片段,进行序列测定,用相关软件比较分析核苷酸序列、蛋白质二级结构以及限制性内切酶谱,不同毒力赖型钩体能扩增出９６０ｂｐ的片段,非致病Ｐａｔｏｃ株未能扩出相应片段,中国赖型参考株ＯｍｐＬ１序列（ＧｅｎｅＢａｎｋ Ｎｏ．ＡＦ２５０３１８）与流感伤寒型相应序列比较有９８个核苷酸差异,同源性为８９．８％,二级结构预测和氨基酸疏水图显示变异主要发生在跨膜蛋白的膜     

一种新的蛋白质结构类预测方法

基于氨基酸的16种分类模型,给出蛋白质序列的派生序列,进而结合加权拟熵和LZ复杂度构造出34维特征向量来表示蛋白质序列。借助于贝叶斯分类器对同源性不超过25%的640数据集进行蛋白质结构类预测,准确度达到71.28%。     

Profile-profile比对方法用于发现远距离同源模板

基于模板的模建方法是蛋白质结构预测领域中最为准确有效的方法,该类方法的成功与否对模板质量的要求较高。为待预测序列找寻合适的模板,本文提出了一种profile-profile比对的方法将查询序列同模板库中的已知结构蛋白进行比对,然后根据比对结果的Z-score得分高低顺序挑选出合适的模板。结果表明:本文的profile-profile比对方法在测试集上的性能明显优于PSI-BLAST,相比PSI-BLAST在测试集上的准确度提高了约14.3%,配对t检验的结果表明准确度的提高具有统计显著性。从而得出如下结论:本文的profile-profile比对方法可以用于为序列相似性较低的待预测序列搜索远距离同源模板,并用于指导后续的三级结构预测。     

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning-based protein inter-residue distance predictor to improve template-free (ab initio) tertiary structure prediction, (b) an enhanced template-based tertiary structure prediction method, and (c) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter-domain structure prediction. The results demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template-free modeling performs better than the template-based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template-free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .     

Toward the solution of the protein structure prediction problem

Since Anfinsen demonstrated that the information encoded in a protein’s amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.     

GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes

Rapid progress in structural modeling of proteins and their interactions is powered by advances in knowledge-based methodologies along with better understanding of physical principles of protein structure and function. The pool of structural data for modeling of proteins and protein–protein complexes is constantly increasing due to the rapid growth of protein interaction databases and Protein Data Bank. The GWYRE (Genome Wide PhYRE) project capitalizes on these developments by advancing and applying new powerful modeling methodologies to structural modeling of protein–protein interactions and genetic variation. The methods integrate knowledge-based tertiary structure prediction using Phyre2 and quaternary structure prediction using template-based docking by a full-structure alignment protocol to generate models for binary complexes. The predictions are incorporated in a comprehensive public resource for structural characterization of the human interactome and the location of human genetic variants. The GWYRE resource facilitates better understanding of principles of protein interaction and structure/function relationships. The resource is available at http://www.gwyre.org.     

基于深度学习的蛋白质建模与设计

随着蛋白质序列及结构数据的大量累积,在获得了大量描述性信息之后如何有效利用海量数据,从已有数据中高效提取信息并且应用到下游任务当中就成为了研究者亟待解决的问题。蛋白质的设计可使新蛋白的研发不再受限于实验条件,这对药物靶点预测、新药研发和材料设计等领域具有重要意义。深度学习作为一种高效的数据特征提取方法,可以通过它对蛋白质数据进行建模,进而加入先验信息对蛋白质进行设计。故此基于深度学习的蛋白质设计就成为一个具有广阔前景的研究领域。文中主要阐述基于深度学习的蛋白质序列与结构数据的建模和设计方法。详述该方法的策略、原理、适用范围、应用实例。讨论了深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Modeling the paramyxovirus hemagglutinin-neuraminidase protein

The paramyxovirus hemagglutinin-neuraminidase (HN) protein exhibits neuraminidase activity and has an active site functionally similar to that in influenza neuraminidases. Earlier work identified conserved amino acids among HN sequences and proposed similarity between HN and influenza neuraminidase sequences. In this work we identify the three-dimensional fold and develop a more detailed model for the HN protein, in the process we examine a variety of protein structure prediction methods. We use the known structures of viral and bacterial neuraminidases as controls in testing the success of protein structure prediction and modeling methods, including knowledge-based threading, discrete three-dimensional environmental profiles, hidden Markov models, neural network secondary structure prediction, pattern matching, and hydropathy plots. The results from threading show that the HN protein sequence has a 6 β-sheet propellor fold and enable us to assign the locations of the individual β-strands. The three-dimensional environmental profile and hidden Markov model methods were not successful in this work. The model developed in this work helps to understand better the biological function of the HN protein and design inhibitors of the enzyme and serves as an assessment of some protein structure prediction methods, especially after the x-ray crystallographic solution of its structure. Proteins 29:264–281, 1997. © 1997 Wiley-Liss, Inc.     

Bioinformatics methods to predict protein structure and function. A practical approach

Protein structure prediction by using bioinformatics can involve sequence similarity searches, multiple sequence alignments, identification and characterization of domains, secondary structure prediction, solvent accessibility prediction, automatic protein fold recognition, constructing three-dimensional models to atomic detail, and model validation. Not all protein structure prediction projects involve the use of all these techniques. A central part of a typical protein structure prediction is the identification of a suitable structural target from which to extrapolate three-dimensional information for a query sequence. The way in which this is done defines three types of projects. The first involves the use of standard and well-understood techniques. If a structural template remains elusive, a second approach using nontrivial methods is required. If a target fold cannot be reliably identified because inconsistent results have been obtained from nontrivial data analyses, the project falls into the third type of project and will be virtually impossible to complete with any degree of reliability. In this article, a set of protocols to predict protein structure from sequence is presented and distinctions among the three types of project are given. These methods, if used appropriately, can provide valuable indicators of protein structure and function.     

The use of soluble protein structures in modeling helical proteins in a layered membrane

Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8?Å to the native structures.     

Refinement of protein termini in template-based modeling using conformational space annealing

The rapid increase in the number of experimentally determined protein structures in recent years enables us to obtain more reliable protein tertiary structure models than ever by template-based modeling. However, refinement of template-based models beyond the limit available from the best templates is still needed for understanding protein function in atomic detail. In this work, we develop a new method for protein terminus modeling that can be applied to refinement of models with unreliable terminus structures. The energy function for terminus modeling consists of both physics-based and knowledge-based potential terms with carefully optimized relative weights. Effective sampling of both the framework and terminus is performed using the conformational space annealing technique. This method has been tested on a set of termini derived from a nonredundant structure database and two sets of termini from the CASP8 targets. The performance of the terminus modeling method is significantly improved over our previous method that does not employ terminus refinement. It is also comparable or superior to the best server methods tested in CASP8. The success of the current approach suggests that similar strategy may be applied to other types of refinement problems such as loop modeling or secondary structure rearrangement.     

Modeling protein loops with knowledge-based prediction of sequence-structure alignment

MOTIVATION: As protein structure database expands, protein loop modeling remains an important and yet challenging problem. Knowledge-based protein loop prediction methods have met with two challenges in methodology development: (1) loop boundaries in protein structures are frequently problematic in constructing length-dependent loop databases for protein loop predictions; (2) knowledge-based modeling of loops of unknown structure requires both aligning a query loop sequence to loop templates and ranking the loop sequence-template matches. RESULTS: We developed a knowledge-based loop prediction method that circumvents the need of constructing hierarchically clustered length-dependent loop libraries. The method first predicts local structural fragments of a query loop sequence and then structurally aligns the predicted structural fragments to a set of non-redundant loop structural templates regardless of the loop length. The sequence-template alignments are then quantitatively evaluated with an artificial neural network model trained on a set of predictions with known outcomes. Prediction accuracy benchmarks indicated that the novel procedure provided an alternative approach overcoming the challenges of knowledge-based loop prediction. AVAILABILITY: http://cmb.genomics.sinica.edu.tw