Neural Network Based on GA-BP Algorithm and its Application in the Protein Secondary Structure Prediction

The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication---the highest prediction rate 75.65%,the average prediction rate 65.04%.

Protein Secondary Structure Prediction with Dynamic Self-Adaptation Combination Strategy Based on Entropy

The algorithm based on combination learning usually is superior to a singleclassification algorithm on the task of protein secondary structure prediction. However,the assignment of the weight of the base classifier usually lacks decision-makingevidence. In this paper, we propose a protein secondary structure prediction method withdynamic self-adaptation combination strategy based on entropy, where the weights areassigned according to the entropy of posterior probabilities outputted by base classifiers.The higher entropy value means a lower weight for the base classifier. The final structureprediction is decided by the weighted combination of posterior probabilities. Extensiveexperiments on CB513 dataset demonstrates that the proposed method outperforms theexisting methods, which can effectively improve the prediction performance.

A Deep Learning Approach for Prediction of Protein Secondary Structure

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures.For this reason,it is important to design methods for accurate protein secondary structure prediction.Most of the existing computational techniques for protein structural and functional prediction are based onmachine learning with shallowframeworks.Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem.In this study,deep learning based models,i.e.,convolutional neural network and long short-term memory for protein secondary structure prediction were proposed.The input to proposed models is amino acid sequences which were derived from CulledPDB dataset.Hyperparameter tuning with cross validation was employed to attain best parameters for the proposed models.The proposed models enables effective processing of amino acids and attain approximately 87.05%and 87.47%Q3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models,respectively.

Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and more.Protein secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure prediction.Protein secondary structures are classified into two categories:3-state category and 8-state category.Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,respectively.The 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,Q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on Q3 prediction.In this paper,we develop an ensemble Machine Learning(ML)approach for Q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in Q8 PSSP.The ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),RF(Random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in Q8 PSSP.The novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP problem.The experimental results confirmed that ensemble ML models are more accurate than individual ML models.They also indicated that features extracted by LDA are more effective than those extracted by PCA.

Using Neural Networks to Predict Secondary Structure for Protein Folding

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

Functional structures and folding dynamics of two peptides

The folding dynamics and structural characteristics of peptides RTKAWNRQLYPEW （P1） and RTKQLYPEW （P2） are investigated by using all-atomic simulation procedure CHARMM in this work. The results show that P1, a segment of an antigen, has a folding motif of α-helix, whereas P2, which is derived by deleting four residues AWNR from peptide P1, prevents the formation of helix and presents a β-strand. And peptlde P1 experiences a more rugged energy landscape than peptide P2. From our results, it is inferred that the antibody CD8 cytolytic T lymphocyte prefers an antigen with a β-folding structure to that with an α-helical one.

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

A novel method for predicting the secondary structures of proteins from amino acid sequence has been presented. The protein secondary structure seqlets that are analogous to the words in natural language have been extracted. These seqlets will capture the relationship be-tween amino acid sequence and the secondary structures of proteins and further form the protein secondary structure dictionary. To be elaborate, the dictionary is organism-specific. Protein sec-ondary structure prediction is formulated as an integrated word segmentation and part of speech tagging problem. The word-lattice is used to represent the results of the word segmentation and the maximum entropy model is used to calculate the probability of a seqlet tagged as a certain secondary structure type. The method is markovian in the seqlets, permitting efficient exact cal-culation of the posterior probability distribution over all possible word segmentations and their tags by viterbi algorithm. The optimal segmentations and their tags are computed as the results of protein secondary structure prediction. The method is applied to predict the secondary struc-tures of proteins of four organisms respectively and compared with the PHD method. The results show that the performance of this method is higher than that of PHD by about 3.9% Q3 accuracy and 4.6% SOV accuracy. Combining with the local similarity protein sequences that are obtained by BLAST can give better prediction. The method is also tested on the 50 CASP5 target proteins with Q3 accuracy 78.9% and SOV accuracy 77.1%. A web server for protein secondary structure prediction has been constructed which is available at http://www.insun.hit.edu.cn:81/demos/bi-ology/index.html.

Population-based incremental learning for the prediction of Homo sapiens’ protein secondary structure

prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method’s mechanisms are too complex to be able to extract clear and straightforward physical meanings from it. This paper explores population-based incremental learning (PBIL), which is a method that combines the mechanisms of a generational genetic algorithm with simple competitive learning. The result shows that its accuracies are particularly associated with the Homo species. This new perspective reveals a number of different possibilities for the purposes of performance improvements.

Learning to Predict in Complex Biological Domains

Protein secondary structure prediction and high-throughput drug screen data mining are two important applications in bioinformatics. The data is represented in sparse feature spaces and can be unrepresentative of future data. There is certainly some noise in the data and there may be significant noise. Supervised learners in this context will display their inherent bias toward certain solutions, generally solutions that fit the training set well. In this paper, we first describe an ensemble approach using subsampling that scales well with dataset size. A sufficient number of ensemble members using subsamples of the data can yield a more accurate classifier than a single classifier using the entire dataset. Experiments on several datasets demonstrate the effectiveness of the approach. We report results from the KDD Cup 2001 drug discovery dataset in which our approach yields a higher weighted accuracy than the winning entry. We then ex-tend our ensemble approach to create an over-generalized classifier for prediction by reducing the individual subsample size. The ensemble strategy using small subsamples has the effect of averaging over a wider range of hypotheses. We show that both protein secondary structure prediction and drug discovery prediction can be improved by the use of over-generalization, specifically through the use of ensembles of small subsamples.

SARS病毒基因组所编码的E蛋白的二级结构和B细胞表位预测

目的 预测SARS病毒E蛋白的B细胞表位和二级结构。方法 以SARS病毒基因组序列为基础 ,采用Gar nier Robson方法、Chou Fasman方法和Karplus Schultz方法预测E蛋白质的二级结构 ;用Kyte Doolittle方案预测蛋白质的亲水性 ;用Emini方案预测蛋白质的表面可能性 ;用Jameson Wolf方案预测氨基酸的抗原性指数。综合评判 ,预测SARS病毒E蛋白的B细胞表位。结果 在SARS病毒E蛋白N 端的第 1～ 6、13～ 19、39～ 4 3、4 7～ 6 4区段和第 73～ 76区段有 β 折叠中心 ;第 6～ 12区段和第 6 7～ 6 9区段可能形成转角或无规则卷曲 ,是柔性区域。E蛋白N端第 2～ 13区段和第 6 1～ 74区段为B细胞优势表位区域。结论 用多参数预测SARS病毒E蛋白的二级结构和B细胞表位 。

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。

基于SQL Server的蛋白质二级结构预测样本集数据库的构建

基于SQL Server数据库管理系统，将蛋白质二级结构预测的样本集CB513、CB396和RS126组织起来，建立了数据库DataSet，并配置了一个IIS服务器以方便网络查询。该数据库将蛋白质二级结构预测样本集有效地组织起来，实现了规范化、结构化统一管理，便于存储、检索和分析数据，减少错误的发生。通过该数据库可以提取供蛋白质二级结构预测研究的样本、序列转换、变换编码以及分析评价预测结果等，取代许多传统编程处理文本文件的繁琐工作，大大提高效率，促进工作的开展。

优化多核SVM的蛋白质二级结构预测

蛋白质序列的不同特征提取方式对蛋白质结构分类有很大的影响。为更好地表达蛋白质结构信息,基于特征融合思想构建特征向量,并使用一种基于多核支持向量机的方法,以多个核函数的线性加权代替传统的单一核函数,在对多类特征进行整合后构造SimpleMKL分类模型;利用梯度下降法迭代求解核函数的权值系数,并校准核函数参数和不同特征表达的融合效果。实验结果表明,该方法提高了蛋白质二级结构分类精度,在分类精度方面有明显优势,有助于准确预测蛋白质的二级结构。

基于Spiking神经网络的蛋白质二级结构学习预测模型

从氨基酸序列来预测蛋白质二级结构，是我们理解蛋白质结构和功能的重要一步。本文探讨了基于Spiking神经网络的蛋白质二级结构学习预测模型，利用单个神经网络进行学习取得的效果不明显，而利用级联神经网络，通过结构到结构的学习，能很好地提高学习准确率。

基于CNN与LSTM模型的蛋白质二级结构预测

蛋白质结构的预测在理解蛋白质结构组成和蛋白质的生物学功能有重要意义,而蛋白质二级结构预测是蛋白质结构预测的重要环节。当PSSM位置特异性进化矩阵被广泛应用于将蛋白质初级结构序列编码作为输入样本后,每个残基可以被表示成二维空间的数据平面,由此文中尝试利用卷积神经网络对其进行训练。文中还设计了另一种卷积神经网络,利用长短记忆网络感知了CNN最后卷积特征面的横向特征和纵向特征后连同卷积神经网络的全连接共同完成分类,最后用ensemble方法对两类卷积神经网络模型进行了整合,最终ensemble方法中包含两类卷积神经网络的六个模型,在CB513蛋白质数据集测得的Q3结果为77.2。

蛋白质二级结构预测服务器PSRSM

蛋白质二级结构预测是蛋白质结构研究的一个重要环节,大量的新预测方法被提出的同时,也不断有新的蛋白质二级结构预测服务器出现。试验选取7种目前常用的蛋白质二级结构预测服务器:PSRSM、SPOT-1D、MUFOLD、Spider3、RaptorX,Psipred和Jpred4,对它们进行了使用方法的介绍和预测效果的评估。随机选取了PDB在2018年8月至11月份发布的180条蛋白质作为测试集,评估角度为:Q3、Sov、边界识别率、内部识别率、转角C识别率,折叠E识别率和螺旋H识别率七种角度。上述服务器180条测试数据的Q3结果分别为:89.96%、88.18%、86.74%、85.77%、83.61%,79.72%和78.29%。结果表明PSRSM的预测结果最好。180条测试集中,以同源性30%,40%,70%分类的实验结果中,PSRSM的Q3结果分别为:89.49%、90.53%、89.87%,均优于其他服务器。实验结果表明,蛋白质二级结构预测可从结合多种深度学习方法以及使用大数据训练模型方向做进一步的研究。

基于SVM的蛋白质二级结构预测

蛋白质结构预测是现代计算生物领域最重要的问题之一,而蛋白质二级结构预测是蛋白质高级结构预测的基础。目前蛋白质二级结构的预测方法较多,其中SVM方法取得了较高的预测精度。重在阐述使用SVM用于蛋白质二级结构预测的步骤,以及与其他方法进行比较时应该注意的事项,为下一步的研究提供参考及启发。

百脉根Hsp70s基因家族的生物信息学分析

热激蛋白(HSPs)是细胞受逆境诱导产生的一类具有稳定蛋白质结构、修复变性蛋白和维护生物膜稳定等功能的分子伴侣,同时,在缓解植物高温伤害和提高热耐受性等方面有重要的调控作用。根据百脉根基因组数据,利用生物信息学方法对百脉根Hsp70基因(LjHsp70s)家族的基因结构、染色体分布、选择压力、顺式作用元件、表达特性和系统进化等进行分析。结果表明,LjHsp70s家族有18个成员,分为4类,它们的内含子和外显子数量为1~8,含有多个保守基序,分布在6条染色体上,编码602~703个氨基酸,均编码酸性亲水蛋白;含有激素、光照及胁迫等响应顺式作用元件,LjHsp70s在百脉根的根、茎、叶等部位均有表达,但其表达量不同;该家族基因在进化过程中主要受负选择作用,进化上较为保守,家族成员与拟南芥和烟草的Hsp70基因可分为6组,但是百脉根LjHsp70s基因仅存在于4个亚族中,表明其与拟南芥、烟草的Hsp70s基因家族成员之间进化关系密切,同时也存在一定差异。LjHsp70s基因在百脉根的各个部位均有表达,对百脉根生长发育过程起着重要的作用,研究结果为解析百脉根LjHsp70s基因功能提供了依据。

嗜酸性乳杆菌MG株S层蛋白的高级结构预测分析及原核表达

以嗜酸性乳杆菌MG株slp基因(表达S层蛋白的基因)为材料,应用Genetyx软件推导出其正确的开放阅读框(ORF),并用CPHmodels,Swiss-Model和Swiss-PdbViewer生物软件预测SLP蛋白质的高级结构;此外,构建slp基因的原核表达载体pGEX-slp,经IPTG诱导后,成功表达出重组S层蛋白(S-layer protein,SLP),并进行SDS-PAGE和Western blot鉴定。结果表明,使用3种不同的软件预测SLP蛋白质的高级结构,预测结果具有很高的相似性,其中α螺旋占3.33%,线性结构占45%,无规则卷曲占51.67%,平均亲水值为-0.262,亲水氨基酸比例为59.1%;表达载体pGEX-slp在E.coli BL21中经IPTG诱导成功表达出重组SLP蛋白,其GST融合蛋白的大小约为70ku。本研究结果为进一步以SLP为材料的生物制品的制备和应用开发奠定了基础。

How Many 3D Structures Do We Need to Train a Predictor?

It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both α-helical and β-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of α-helical membrane proteins perform slightly better than that of β-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a metaoanalysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors.