基于多重进化矩阵的蛋白质特征向量构造方法

特征向量的构造是蛋白质二级结构预测的一个关键问题. 现有的研究方法,通常只使用BLOSUM62进化矩阵生成PSSM矩阵,对蛋白质进化过程中存在的氨基酸残基突变现象缺乏考虑. 本文提出利用多重进化矩阵构造蛋白质特征向量,其融合了不同进化时间的PSSM矩阵,不仅能够很好地反映序列中氨基酸的位置信息,而且能够反映序列进化过程中氨基酸位点发生突变产生的影响. 本文通过组合不同进化程度的矩阵来构造特征向量,选用逻辑回归、随机森林和多分类支持向量机三种分类算法作为预测工具,利用网格搜索法和交叉实验法优化参数,在RS126、CB513和25PDB公用数据集上进行了若干组实验. 对比实验结果表明,本文所提出基于多重进化矩阵的蛋白质特征向量构造方法能够有效提高蛋白质二级结构的预测精度.     

Predicting protein secondary structure using a mixed-modal SVM method in a compound pyramid model

Accurate protein secondary structure prediction plays an important role in direct tertiary structure modeling, and can also significantly improve sequence analysis and sequence-structure threading for structure and function determination. Hence improving the accuracy of secondary structure prediction is essential for future developments throughout the field of protein research.In this article, we propose a mixed-modal support vector machine (SVM) method for predicting protein secondary structure. Using the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-BLAST multiple sequence alignment as input for a mixed-modal SVM, secondary structure can be predicted at significantly increased accuracy. Using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism (KDTICM) method, we have proposed a compound pyramid model, which is composed of three layers of intelligent interface that integrate a mixed-modal SVM (MMS) module, a modified Knowledge Discovery in Databases (KDD1) process, a mixed-modal back propagation neural network (MMBP) module and so on.Testing against data sets of non-redundant protein sequences returned values for the Q₃ accuracy measure that ranged from 84.0% to 85.6%,while values for the SOV99 segment overlap measure ranged from 79.8% to 80.6%. When compared using a blind test dataset from the CASP8 meeting against currently available secondary structure prediction methods, our new approach shows superior accuracy.Availability: http://www.kdd.ustb.edu.cn/protein_Web/.     

Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising

Prediction of protein structural class plays an important role in protein structure and function analysis, drug design and many other biological applications. Prediction of protein structural class for low-similarity sequences is still a challenging task. Based on the theory of wavelet denoising, this paper presents a novel method of prediction of protein structural class for the first time. Firstly, the features of the protein sequence are extracted by using Chou’s pseudo amino acid composition (PseAAC). Then the extracted feature information is denoised by two-dimensional (2D) wavelet. Finally, the optimal feature vectors are input to support vector machine (SVM) classifier to predict protein structural classes. We obtained significant predictive results using jackknife test on three low-similarity protein structural class datasets 25PDB, 1189 and 640, and compared our method with previous methods The results indicate that the method proposed in this paper can effectively improve the prediction accuracy of protein structural class, which will be a reliable tool for prediction of protein structural class, especially for low-similarity sequences.     

基于卷积长短时记忆神经网络的蛋白质二级结构预测

鉴于不同类型氨基酸的相互作用对蛋白质结构预测的影响不同,文中融合卷积神经网络和长短时记忆神经网络模型,提出卷积长短时记忆神经网络,并应用到蛋白质8类二级结构的预测中.首先基于氨基酸序列的类别信息和氨基酸结构的进化信息表示蛋白质序列,并采用卷积提取氨基酸残基之间的局部相关特征,然后利用双向长短时记忆神经网络提取蛋白质序列内部残基之间的远程相互作用,最后将提取的蛋白质的局部相关特征和远程相互作用用于蛋白质8类二级结构的预测.实验表明,相比基准方法,文中模型提高8类二级结构预测的精度,并具有良好的可扩展性.     

基于卷积神经网络的多聚脯氨酸二型二级结构预测

多聚脯氨酸二型螺旋是一种特殊且稀少的蛋白质二级结构。为了节省实验方法测定该结构的时间和成本,本文设计一种基于卷积神经网络的深度学习算法用于预测多聚脯氨酸二型螺旋。首先,对蛋白质序列信息进行特征编码生成特征矩阵,特征编码方式包括氨基酸正交码、氨基酸物理化学性质和位置特异性打分矩阵。其次,将归一化处理后的特征矩阵输入到卷积神经网络中,自动提取蛋白质序列的局部深层特征并输出多聚脯氨酸二型螺旋的预测结果。实验结果表明,该算法的性能相较于支持向量机之类的6种传统机器学习算法有明显的提升。     

Protein Structure from Contact Maps: A Case-Based Reasoning Approach

Determining the three-dimensional structure of a protein is an important step in understanding biological function. Despite advances in experimental methods (crystallography and NMR) and protein structure prediction techniques, the gap between the number of known protein sequences and determined structures continues to grow. Approaches to protein structure prediction vary from those that apply physical principles to those that consider known amino acid sequences and previously determined protein structures. In this paper we consider a two-step approach to structure prediction: (1) predict contacts between amino acids using sequence data; (2) predict protein structure using the predicted contact maps. Our focus is on the second step of this approach. In particular, we apply a case-based reasoning framework to determine the alignment of secondary structures based on previous experiences stored in a case base, along with detailed knowledge of the chemical and physical properties of proteins. Case-based reasoning is founded on the premise that similar problems have similar solutions. Our hypothesis is that we can use previously determined structures and their contact maps to predict the structure for novel proteins from their contact maps. The paper presents an overview of contact maps along with the general principles behind our methodology of case-based reasoning. We discuss details of the implementation of our system and present empirical results using contact maps retrieved from the Protein Data Bank. Funding provided by: The Natural Science and Engineering Research Council (Ottawa); Institute for Robotics and Intelligent Systems (Ottawa); Protein Engineering Network Center of Excellence (Edmonton)     

Using support vector machine with a hybrid feature selection method to the stock trend prediction

In this paper, we developed a prediction model based on support vector machine (SVM) with a hybrid feature selection method to predict the trend of stock markets. This proposed hybrid feature selection method, named F-score and Supported Sequential Forward Search (F_SSFS), combines the advantages of filter methods and wrapper methods to select the optimal feature subset from original feature set. To evaluate the prediction accuracy of this SVM-based model combined with F_SSFS, we compare its performance with back-propagation neural network (BPNN) along with three commonly used feature selection methods including Information gain, Symmetrical uncertainty, and Correlation-based feature selection via paired t-test. The grid-search technique using 5-fold cross-validation is used to find out the best parameter value of kernel function of SVM. In this study, we show that SVM outperforms BPN to the problem of stock trend prediction. In addition, our experimental results show that the proposed SVM-based model combined with F_SSFS has the highest level of accuracies and generalization performance in comparison with the other three feature selection methods. With these results, we claim that SVM combined with F_SSFS can serve as a promising addition to the existing stock trend prediction methods.     

基于不同序列特征值预测氧化还原酶辅酶类型的研究

如何有效提取蛋白质序列特征值,一直是生物信息学研究的重要任务.本文研究8种序列特征值提取方法,并考察它们在不同分类器中的表现,以用于预测氧化还原酶辅酶依赖类型.其中,氨基酸组成法效果最差,平均预测精度仅及64.96%;而将两性伪氨基酸组成和新氨基酸组成分布两种方法合并后,以支持向量机作为分类器时,其识别效果最佳,可达92.93%.此外,不同特征值的提取方法与分类器之间似乎有着一定的匹配关系,只有找到其间的最佳匹配,才能获得最佳的识别效果.     

基于广义回归神经网络的蛋白质二级结构预测

在生化实验中,关于优化蛋白质预测问题,由于采集的信息、参数、选取和设置等优化处理存在随机性,限制了蛋白质二级结构预测精确度。为解决上述问题,针对广义回归神经网络学习速率快、网络稳健的特点,提出基于广义回归神经网络预测蛋白质二级结构的方法。鉴于编码方式对预测精度有重要影响,首先基于5位编码和不同的滑动窗口构建多个广义回归神经网络预测器对蛋白质二级结构进行预测,取得了较好的结果。并采用富含生物进化信息的序列谱(Profile)编码构建输入向量、并针对不同大小的滑动窗口设置多个spread值重新创建广义回归神经网络预测器,大大提高了预测精确度,仿真结果证明了预测模型的有效性和可行性,为预测提供了有效方法。     

基于交叉覆盖算法的蛋白质二级结构预测方法

蛋白质二级结构预测在蛋白质空间结构预测中起着承上启下的重要作用。近年来,大量的方法应用于二级结构预测中,其中,神经网络算法效果较好。但是,由于传统的神经网络存在结构复杂、学习速度慢、运行效率低、处理海量数据困难的缺陷,大大影响了预测的效果,因此,该文将一种基于构造性神经网络算法,也就是交叉覆盖算法应用于蛋白质二级结构预测中,另外,为了引入更多的同源家族结构的信息,采用了基于概率的Profile编码方式。通过实验证明将交叉覆盖算法运用在蛋白质二级结构预测中的可行性．并且比传统的神经网络方法有了更高的准确率。     

基于散列辞典的蛋白质二级结构预测方法

提出一种易于修改的蛋白质二级结构预测算法。以蛋白质数据银行中PDB文本数据作为数据源,提取所有蛋白质氨基酸序列并以此建立样本数据库,然后针对α-螺旋、β-折叠分别利用基于散列辞典的不同改进方法编程实现蛋白质二级结构序列片段预测,在预测过程中,随机抽取68421个蛋白质中部分样本作为测试集,对未知序列根据建立的散列辞典中的片段使用正向最大匹配分词法进行切分对比。从实验结果来看,对未知序列片段预测的准确度达到了83．9％,而且能够较好地体现片段之间的连接顺序。     

Improving protein secondary structure predictions by prediction fusion

Protein secondary structure prediction is still a challenging problem at today. Even if a number of prediction methods have been presented in the literature, the various prediction tools that are available on-line produce results whose quality is not always fully satisfactory. Therefore, a user has to know which predictor to use for a given protein to be analyzed. In this paper, we propose a server implementing a method to improve the accuracy in protein secondary structure prediction. The method is based on integrating the prediction results computed by some available on-line prediction tools to obtain a combined prediction of higher quality. Given an input protein p whose secondary structure has to be predicted, and a group of proteins F, whose secondary structures are known, the server currently works according to a two phase approach: (i) it selects a set of predictors good at predicting the secondary structure of proteins in F (and, therefore, supposedly, that of p as well), and (ii) it integrates the prediction results delivered for p by the selected team of prediction tools. Therefore, by exploiting our system, the user is relieved of the burden of selecting the most appropriate predictor for the given input protein being, at the same time, assumed that a prediction result at least as good as the best available one will be delivered. The correctness of the resulting prediction is measured referring to EVA accuracy parameters used in several editions of CASP.     

Sequence-driven features for prediction of subcellular localization of proteins

Prediction of the cellular location of a protein plays an important role in inferring the function of the protein. Feature extraction is a critical part in prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present a method for extracting useful features from protein sequence data. The method employs local and global pairwise sequence alignment scores as well as composition-based features. Five different features are used for training support vector machines (SVMs) separately and a weighted majority voting makes a final decision. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. Comparing the prediction accuracy of various feature extraction methods, provides a biological insight into the location of targeting information. Our experimental results confirm that our feature extraction methods are very useful for predicting subcellular localization of proteins.     

基于混合SVM方法的蛋白质二级结构预测算法

预测蛋白质二级结构,是当今生物信息学中一个难以解决的问题。由于预测蛋白质二级结构的精度在蛋白 质结构研究中起到非常重要的作用,因此在基于KDTICM理论基础上,提出一种基于混合SVM方法的蛋白质二级 结构预测算法。该算法有效地利用蛋白质的物化属性和PSI-SEARCH生成的位置特异性打分矩阵作为双层SVM的 输入,从而大大地提高了蛋白质二级结构预测的精度。实验比较分析表明,新算法的预测精度和普适性明显优于目前 其他典型的预测方法。     

A simple method for predicting the secondary structure of globular proteins: implications and accuracy

A method is presented for predicting the secondary structure of globular proteins from their amino acid sequence. It is based on a rigorous statistical exploitation of the well-known biological fact that the amino acid compositions of each secondary structure are different. We also propose an evaluation process that allows us to estimate the capacity of a method to predict the secondary structure of a new protein which does not have any homologous proteins whose structure is already known. This evaluation process shows that our method has a prediction accuracy of 58.7% over three states for the 62 proteins of the Kabsch and Sander (1983a) data bank. This result is better than that obtained by the most widely used methods--Lim (1974), Chou and Fasman (1978) and Garnier et al. (1978)--and also than that obtained by a recent method based on local homologies (Levin et al., 1986). Our prediction method is very simple and may be implemented on any microcomputer and even on programmable pocket calculators. A simple Pascal implementation of the method prediction algorithm is given. The interpretation of our results in terms of protein folding and directions for further work are discussed.     

A molecular dynamics and knowledge-based computational strategy to predict native-like structures of polypeptides

One of the main research problems in structural bioinformatics is the prediction of three-dimensional structures (3-D) of polypeptides or proteins. The current rate at which amino acid sequences are identified increases much faster than the 3-D protein structure determination by experimental methods, such as X-ray diffraction and NMR techniques. The determination of protein structures is both experimentally expensive and time consuming. Predicting the correct 3-D structure of a protein molecule is an intricate and arduous task. The protein structure prediction (PSP) problem is, in computational complexity theory, an NP-complete problem. In order to reduce computing time, current efforts have targeted hybridizations between ab initio and knowledge-based methods aiming at efficient prediction of the correct structure of polypeptides. In this article we present a hybrid method for the 3-D protein structure prediction problem. An artificial neural network knowledge-based method that predicts approximated 3-D protein structures is combined with an ab initio strategy. Molecular dynamics (MD) simulation is used to the refinement of the approximated 3-D protein structures. In the refinement step, global interactions between each pair of atoms in the molecule (including non-bond interactions) are evaluated. The developed MD protocol enables us to correct polypeptide torsion angles deviation from the predicted structures and improve their stereo-chemical quality. The obtained results shows that the time to predict native-like 3-D structures is considerably reduced. We test our computational strategy with four mini proteins whose sizes vary from 19 to 34 amino acid residues. The structures obtained at the end of 32.0 nanoseconds (ns) of MD simulation were comparable topologically to their correspondent experimental structures.     

Predicting RNA Secondary Structure Using Profile Stochastic Context-Free Grammars and Phylogenic Analysis

Stochastic context-free grammars （SCFGs） have been applied to predicting RNA secondary structure. The prediction of RNA secondary structure can be facilitated by incorporating with comparative sequence analysis. However, most of existing SCFG-based methods lack explicit phylogenic analysis of homologous RNA sequences, which is probably the reason why these methods are not ideal in practical application. Hence, we present a new SCFG-based method by integrating phylogenic analysis with the newly defined profile SCFG. The method can be summarized as： 1） we define a new profile SCFG, M, to depict consensus secondary structure of multiple RNA sequence alignment; 2） we introduce two distinct hidden Markov models, λ and λ＇, to perform phylogenic analysis of homologous RNA sequences. Here, λ＇ is for non-structural regions of the sequence and λ＇ is for structural regions of the sequence; 3） we merge λ and λ＇ into M to devise a combined model for prediction of RNA secondary structure. We tested our method on data sets constructed from the Rfam database. The sensitivity and specificity of our method are more accurate than those of the predictions by Pfold.     

蛋白质二级结构预测的一种新的编码方式

编码方式是影响蛋白质二级结构预测准确率的重要因素之一。针对单序列蛋白质二级结构预测问题,提出了一种新的综合编码方法。该编码是根据氨基酸出现在每种二级结构中的倾向因子以及氨基酸的疏水性值进行分类,并以二进制形式来表示每类氨基酸的编码方法。在相同的实验条件下,首先用不同的编码方式对数据集CB513进行编码,然后采用支持向量机的方法进行训练建模预测。实验结果显示提出编码的预测准确率比20位正交编码和5位编码分别高出1.48%和10.68%。可见,该编码比较适合非同源或低同源蛋白质结构预测。     

PSIST: A scalable approach to indexing protein structures using suffix trees

Approaches for indexing proteins and for fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this paper, we develop a new method for extracting local structural (or geometric) features from protein structures. These feature vectors are in turn converted into a set of symbols, which are then indexed using a suffix tree. For a given query, the suffix tree index can be used effectively to retrieve the maximal matches, which are then chained to obtain the local alignments. Finally, similar proteins are retrieved by their alignment score against the query. Our results show classification accuracy up to 50% and 92.9% at the topology and class level according to the CATH classification. These results outperform the best previous methods. We also show that PSIST is highly scalable due to the external suffix tree indexing approach it uses; it is able to index about 70,500 domains from SCOP in under an hour.     

HYBP_PSSP: a hybrid back propagation method for predicting protein secondary structure

The prediction of secondary structure is an important topic in the field of bioinformatics, even if the methods have matured, and development of the algorithms is a far less active area than a decade ago. Accurate prediction is very useful to biologists in its own right, but it is worth pointing out that it is also an essential component of tertiary structure prediction, which in contrast is far from solved and continues to be a highly active area of research. In addition, sequence comparison methods have more recently incorporated local structure tracks. The extra information utilized by the new methods has led to considerable improvements in fold recognition and alignment accuracy. In this paper, a novel method for protein secondary structure prediction is presented. Using evolutionary information contained in amino acid’s physicochemical properties, position-specific scoring matrix generated by PSI-BLAST and HMMER3 profiles as input to hybrid back propagation system, secondary structure can be predicted at significantly increased accuracy. Based on knowledge discovery theory based on inner cognitive mechanism (KDTICM) theory, we have constructed a compound pyramid model approach, which is composed of four layers of the intelligent interface and integrated in several ways, such as hybrid back propagation method (HBP), modified knowledge discovery in databases (KDD*), hybrid SVM method (HSVM) and so on. Experiments on three standard datasets (RS126, CB513 and CASP8) show that CPM is capable of producing the higher Q ₃ and SOV scores than that achieved by existing widely used schemes such as PSIPRED, PHD, Predator, as well as previously developed prediction methods. On the RS126 and CB513 datasets, it achieves a Q ₃ and SOV99 score are considerably higher than the best reported scores, respectively. It is also tested on target proteins of critical assessment of protein structure prediction experiment (CASP8) and achieves better results than the traditional methods, including the popular PSIPRED method over overall prediction accuracy. Available: .