蛋白质二级结构预测方法的评价

蛋白质结构预测是后基因组时代的一项重要任务,蛋白质二级结构预测是蛋白质结构预测的关键步骤。现在一般认为,如果蛋白质二级结构的预测准确率达到80％的话,就可以基本准确地预测一个蛋白质分子的三维空间结构。目前蛋白质二级结构预测的方法不断涌现,提供二级结构预测的网站也逐渐增多。为给广大研究工作者在选择使用这些预测方法时提供一种参考,文章采用统一的标准对10种比较重要而且有效的方法进行测试,并在此基础上做出评价和分析,这10种方法是：GORI、PROF、GORⅣ、NNPREDICT、PHDsec、SSpro v 2．0、PSIPRED、PREDATOR、SOPMA和APSSP2。比较结果显示：APSSP2、SSpro v 2．0和PSIPRED方法的预测效果较好,可以作为使用时的首选方案,其中尤其以APSSP2方法的预测效果最佳。     

A multimodal neural network with single-state predictions for protein secondary structure

Prediction of protein secondary structure is considered to be an important step toward elucidating the three-dimensional structure and function of proteins. We have developed a multimodal neural network (MNN) to predict protein secondary structure. The MNN is composed of several subclassifiers for single-state predictions using neural networks and a decision neural network (DNN). Each subclassifier employs a number of subnetworks to predict the single-state of the secondary structure individually and produces the final results by majority decision. The DNN uses a three-layer neural network to produce the final overall prediction from the outputs of the single-state predictions. The MNN gives an overall accuracy of 71.1% with corresponding Matthews correlation coefficients of C_H = 0.62 and C_E = 0.53. The prediction test is based on a database of 126 nonhomologous protein sequences. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

Bidirectional segmented-memory recurrent neural network for protein secondary structure prediction

The formation of protein secondary structure especially the regions of β-sheets involves long-range interactions between amino acids. We propose a novel recurrent neural network architecture called segmented-memory recurrent neural network (SMRNN) and present experimental results showing that SMRNN outperforms conventional recurrent neural networks on long-term dependency problems. In order to capture long-term dependencies in protein sequences for secondary structure prediction, we develop a predictor based on bidirectional segmented-memory recurrent neural network (BSMRNN), which is a noncausal generalization of SMRNN. In comparison with the existing predictor based on bidirectional recurrent neural network (BRNN), the BSMRNN predictor can improve prediction performance especially the recognition accuracy of β-sheets.     

A protein secondary structure prediction scheme for the IBM PC and compatibles

A prediction scheme has been developed for the IBM PC and compatibles containing computer programs which make use of the protein secondary structure prediction algorithms of Nagano (1977a,b), Garnier et al. (1978), Burgess et al. (1974), Chou and Fasman (1974a,b), Lim (1974) and Dufton and Hider (1977). The results of the individual prediction methods are combined as described by Hamodrakas et al. (1982) by the program PLOTPROG to produce joint prediction histograms for a protein, for three types of secondary structure: alpha-helix, beta-sheet and beta-turns. The scheme requires uniform input for the prediction programs, produced by any word processor, spreadsheet, editor or database program and produces uniform output on a printer, a graphics screen or a file. The scheme is independent of any additional software and runs under DOS 2.0 or later releases.     

A Pascal microcomputer program for prediction of protein secondary structure and hydropathic segments

This paper describes a simple Pascal microcomputer program for prediction of protein secondary structure according to the Chou and Fasman algorithm. In addition, it performs an analysis of the hydropathic character of the residues for prediction of external/internal regions of the polypeptide chain. Also it searches for probable glycosylation and phosphorylation sites.     

Parallel protein secondary structure prediction schemes using Pthread and OpenMP over hyper-threading technology

Protein secondary structure prediction has a fundamental influence on today’s bioinformatics research. In this work, tertiary classifiers for the protein secondary structure prediction are implemented on Denoeux Belief Neural Network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 matrix and PSSM matrix are experimented separately as the encoding schemes for DBNN. Hydrophobicity matrix, BLOSUM62 matrix and PSSM matrix are applied to DBNN architecture for the first time. The experimental results contribute to the design of new encoding schemes. Our accuracy of the tertiary classifier with PSSM encoding scheme reaches 72.01%, which is almost 10% better than the previous results obtained in 2003. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the Hyper-Threading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that Hyper-Threading technology for Intel architecture is efficient for parallel biological algorithms.

蛋白质二级结构预测中的HMM及I／O HMM方法研究

文章主要介绍了在蛋白质结构预测中两种被有效使用的模型--隐马尔可夫模型和输入隐马尔可夫模型，分别阐述了其原理、算法及应用实例。数值实验表明，这两种方法对小样本的预测实用具有较强的适应性。     

基于动态规划的DNA二级结构预测算法

基于DNA分子二级结构的结构稳定性和热力学稳定性,提出一种预测DNA分子二级结构的算法.利用基于矩阵的动态规划算法求解DNA分子最大碱基对匹配的所有二级结构;利用Nearest-Neighbor热力学模型计算所有结构的自由能,自由能在阈值范围内的即为DNA分子可能的二级结构.将实验结果与RNAstructure软件结果进行对比,对比结果表明,该方法具有较高的准确率和覆盖范围.     

KAAPRO: An approach of protein secondary structure prediction based on KDD1 in the compound pyramid prediction model

The problem of protein secondary structure prediction is one of the most important problems in Bioinformatics. After the study of this problem for 30 years and more, there have been some breakthroughs. Especially, the introduction of ensemble prediction model and hybrid prediction model makes the accuracy of prediction better, but there is a long distance to induce the tertiary structures from the secondary ones. As one of the extension researches of KDTICM [Bingru, Yang (2004). Knowledge discovery based on theory of inner cognition mechanism and application. Beijing: Electronic Industry Press] theory, this paper proposed a method KAAPRO, which is based on Maradbcm algorithm which is induced by KDD1 model and combined with CBA, for protein secondary structure prediction. And a gradually enhanced, multi-layer systematic prediction model, compound pyramid model, is proposed. The kernel of this model is KAAPRO. Domain knowledge is used through the whole model, and the physical–chemical attributes are chosen by causal cellular automata. In the experiment, the test proteins used in reference Muggleton et al. (Muggleton, S. H., King, R., Sternberg, M. (1992). Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7), 647–657) are predicted. The structures of amino acids, whose structural traits are obscure, are predicted well by KAAPRO. Hence, the result of this model is satisfying too.     

Computer program Jamsek combining statistical and stereochemical rules for the prediction of protein secondary structure

This article briefly describes our program Jamsek written in FORTRAN for an ICL 2950/10 computer. Jamsek combines statistical and stereochemical rules most frequently encountered in literature to predict protein secondary structure from its sequence, into a single algorithm. The composite algorithm does not work better than the best existing single algorithms of Garnier et al. (J. Mol. Biol., 120, 97-120, 1978) or Lim (J. Mol. Biol., 88, 873-894, 1974) if percentage of residues with a correctly predicted secondary structure is taken as a criterion. However, it is fairly reliable in predicting the total amount of alpha-helices and beta-sheets in proteins, the secondary structure of highly ordered proteins or their parts and identification of long alpha-helices. It surpasses the previous algorithms by providing a possibility to make a notion about confidence of the prediction of the particular secondary structure elements thanks to the simultaneous availability of four independent predictions of the secondary structure and other relevant data (hydrophobic profile and helical wheel representation). The main body of this article is devoted to a demonstration that output data of Jamsek can simply be used for the prediction of protein topological class, identification of globular proteins containing hydrophobic alpha-helices and, as an auxiliary means, to distinguish between protein coding and non-coding nucleotide sequences.     

The prediction of protein secondary structure with a Cascade Correlation Learning Architecture of neural networks

A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards the global minimum of the error function more efficiently than BPNN. However, only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (C_t8,C ,C and C_coil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system, different NN architectures can perform similarly.     

A massively parallel genetic algorithm for RNA secondary structure prediction

We present a new method for predicting RNA secondary structure based on a genetic algorithm. The algorithm is designed to run on a massively parallel SIMD computer. Statistical analysis shows that the program performs well when compared to a dynamic programming algorithm used to solve the same problem. The program has also pointed out a long-standing simplification in the implementation of the original dynamic programming algorithm that sometimes causes it not to find the optimal secondary structure.     

A bayesian statistical algorithm for RNA secondary structure prediction.

A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that Bayesian inference can be employed to relax or eliminate the need to specify the parameters of bioinformatics recursive algorithms and to give a statistical representation of the full ensemble of probable solutions with the incorporation of uncertainty in parameter values. In this paper, we make an initial exploration of these potential advantages of the Bayesian approach. We present a Bayesian algorithm that is based on stacking energy rules but relaxes the need to specify the parameters. The algorithm returns the exact posterior distribution of the number of destabilizing loops, stacking energy matrices, and secondary structures. The algorithm generates statistically representative structures from the full ensemble of probable secondary structures in exact proportion to the posterior probabilities. Once the forward recursions for the algorithm are completed, the backward recursive sampling executes in O(n) time, providing a very efficient approach for generating representative structures. We demonstrate the utility of the Bayesian approach with several tRNA sequences. The potential of the approach for predicting RNA secondary structures and presenting alternative structures is illustrated with applications to the Escherichia coli tRNA(Ala) sequence and the Xenopus laevis oocyte 5S rRNA sequence.     

Improving acoustic vehicle classification by information fusion

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modified Bayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found to be improved than a feature level fusion approach.     

A coprocessor architecture for fast protein structure prediction

Predicting the protein structure from an amino acid sequence is computationally very intensive. In order to speed up protein sequence matching and processing, we present a novel coprocessor architecture for fast protein structure prediction. The architecture consists of systolic arrays to speed up the data intensive sequence alignment and structure prediction steps, and finite state machines for the control dominated steps. The architecture has been synthesized using Synopsys DC Compiler in 0.18 micron CMOS technology and details of its area and timing performance have been provided. A procedure to develop architectures with area-time trade-offs has also been presented.