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.

IMPROVED METHOD FOR RNA SECONDARY STRUCTURE PREDICTION'

A simple stepwise folding process has been developed to simulate RNA secondary structure formation.Modifications for the energy parameters of various loops were included in the program.Five possible types of pseudoknots including the well known H-type pseudoknot were permitted to occur if reasonable.We have applied this approach to e number of RNA sequences.The prediction accuracies we obtained were higher than those in published papers.

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%.

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.

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.

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.

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.

Evaluation of the secondary structures of protein in the extracellular polymeric substances extracted from activated sludge by different methods

The changes of protein secondary structures in the extracellular polymeric substances(EPS) extracted from activated sludge by four different methods were studied by analyzing the amide I region(1700–1600 cm-1) of the Fourier transform infrared spectra and model protein test. The results showed the molecular weight distribution of organic matter extracted by centrifugation, heating and cation exchange resin(CER) was similar, while the EPS extracted by centrifugation(Control) and CER had similar fluorescent organic matter. The protein secondary structures of extracted EPS by the four methods were different. The similarities of protein secondary structures between the EPS extracted by CER with the Control were the highest among the four extracted EPS. Although the EPS yield extracted by formaldehyde + NaOH method were the highest, its protein secondary structures had the lowest similarity with those extracted by the Control. Additionally, the effects of centrifugation and CER extraction on the secondary structures of bovine serum albumin were also lower than that of other extraction processes. CER enables the second maximum extraction of EPS and maximum retention of the original secondary structure of proteins.

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.

基于二维相关红外光谱对pH值影响大豆分离蛋白二级结构含量的快速分析

为满足不同种类食品对大豆分离蛋白(soybean protein isolate,SPI)不同功能性的需求,本研究利用红外光谱快速采集70组不同pH值处理后SPI的数据,探讨pH值变化对SPI结构含量的影响。使用均值中心化、多元散射校正、标准正态变量变换和归一化算法对红外光谱数据进行预处理,基于二维相关红外光谱提取特征波段,再利用偏最小二乘(partial least square,PLS)法和算术优化算法-随机森林(arithmetic optimization algorithm-random forests,AOA-RF)建立不同pH值条件下SPI结构及含量的预测模型。结果表明,经均值中心化和多元散射校正结合处理后,α-螺旋、β-折叠、β-转角和无规卷曲模型的相对标准偏差分别为1.29%、1.60%、1.37%、7.28%,两者结合对光谱数据的预处理效果最佳。预测α-螺旋和β-折叠含量最优模型为AOA-RF(特征波段),校正集决定系数为0.9350和0.9266,预测集决定系数为0.8568和0.8701;预测β-转角和无规卷曲含量最优模型为PLS(特征波段),校正集决定系数为0.9154和0.8817,预测集决定系数为0.8913和0.7843。本研究结果可为工业生产过程中产品质量快速检测和工艺条件控制提供理论支撑。

不同干燥方式对毛竹笋全粉中氨基酸含量和蛋白质结构的影响

为探究不同干燥方式对竹笋全粉氨基酸含量及蛋白质结构的影响,该实验采用喷雾干燥(spray drying,SD)、热风干燥(hot air drying,HAD)、微波干燥(microwave drying,MD)、低温真空干燥(low-temperature vacuum drying,LVD)4种方式制备毛竹笋全粉(Phyllostachys pubescens shoot powder,PPSP),分析了4种干燥方式的PPSP氨基酸组成及含量,并基于拉曼光谱酰胺Ⅰ带比较了4组样品蛋白质二级结构变化。结果表明,LVD制备的PPSP总氨基酸含量最高,为29.19 g/100 g,其次为SD,HAD制备的PPSP总氨基酸含量最低。LVD组的PPSP中必需氨基酸含量最高,为10.43 g/100 g,占总氨基酸含量的35.73%。LVD和SD制备的PPSP中呈味氨基酸含量分别为17.58 g/100 g、15.81 g/100 g,显著高于HAD和MD组的呈味氨基酸含量。LVD样品中氨基酸组成最接近于标准模式谱。通过主成分分析提取出2个主成分,累计方差贡献率达89.45%,综合评分结果为:LVD>SD>MD>HAD,聚类热图将4种干燥方式分为3类。干燥后的PPSP蛋白质中β-折叠结构最高,占总二级结构的74.66%~83.35%,HAD样中β-折叠、α-螺旋结构含量明显高于其他样品。该研究可为毛竹笋干燥及竹笋全粉的开发利用提供理论参考。

400 km/h高速铁路轮轨噪声与二次结构噪声预测及分析

为评估400 km/h高速铁路列车通过桥梁时产生的轮轨噪声与二次结构噪声,基于统计能量分析法、有限元-边界元法,建立轮轨噪声和二次结构噪声数值预测模型。在此基础上,从轮轨粗糙度、车速和扣件刚度3个方面,分析轮轨粗糙度变化、车速变化以及扣件刚度变化对高速铁路高架段轮轨噪声和二次结构噪声的影响。研究结果表明:(1)在仅改变轮轨粗糙度情况下,综合噪声的变化趋势一致,随着轮轨粗糙度的增大,综合噪声也呈现增大趋势,并且轮轨粗糙度每增加1 dB,声压级增加1 dB(A);(2)在仅改变车速情况下,综合噪声的变化趋势一致,随着车速的增大,综合噪声也呈现增大趋势,但是噪声增大的速率变缓;(3)在仅改变扣件刚度情况下,扣件刚度变化对综合噪声的影响,主要集中在50~800 Hz频段,在其他频段范围内,综合噪声几乎不受扣件刚度变化的影响。在50~200 Hz频段,随着扣件刚度的增大,综合噪声也相应增大;而在200~800 Hz频段,随着扣件刚度的增大综合噪声数值减小。

羊口疮病毒F1L蛋白二级结构分析与表位预测

目的利用软件预测羊口疮病毒(ORFV)F1L蛋白的二级结构和细胞表位。方法利用SOPMA服务器预测ORFV F1L蛋白的二级结构,以DNAStar软件单参数(二级结构、亲水性、可及性、柔韧性及抗原性)预测结果为基础,通过二级结构预测初步筛选,并以ABCpred方案作为最终验证,预测羊口疮病毒(ORFV)F1L蛋白的B细胞表位;运用神经网络+量化矩阵法(ANNs+QM法)预测ORFV F1L蛋白的CTL细胞表位;使用MHC-II类分子结合肽在线程序预测ORFV F1L蛋白的Th细胞表位。结果 ORFV F1L蛋白含有α-螺旋34.71%、β-片层18.82%、β-转角5.88%、无规则卷曲40.59%;没有信号肽,可能存在7个B细胞表位,2个CTL表位,3个Th细胞表位。结论该研究结果将为建立ORFV诊断方法、制备单克隆抗体及合成肽疫苗提供理论依据。

不同折叠类型蛋白编码基因的密码子使用

对 1 95个编码不同折叠类型蛋白 (5 0种全α结构蛋白 ,6 6种全 β结构蛋白 ,3 7种α +β结构蛋白 ,42种α/β结构蛋白 )基因的密码子使用偏性的方差分析研究表明 ,不同折叠类型蛋白的密码子使用偏性有着显著的区别 .特定折叠类型的蛋白有着特定的编码基因的密码子使用模式 .这一结果表明 ,在蛋白质折叠类型和蛋白质二级结构的预测过程中 ,编码基因的密码子使用偏性可以作为蛋白质一级结构以外的一项重要的预测标准 .

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 。

猪内源性反转录病毒囊膜蛋白基因的克隆及其蛋白二级结构和B细胞表位预测

猪内源性反转录病毒(PERV)是与猪-人异种移植病原安全性密切相关的一类病毒。env基因编码病毒的囊膜蛋白,它与病毒的亚型分类、宿主感染范围、细胞的嗜性以及对宿主细胞的感染机制、诱导宿主产生中和抗体等密切相关。本研究利用RT-PCR的方法,从五指山小型猪外周血淋巴细胞中扩增PERV的囊膜蛋白基因并进行测序,随后用生物信息学相关软件和方法,对PERV-Env蛋白二级结构及B细胞表位进行预测。经综合分析评价,结果发现PERV-Env蛋白有18个可能的B细胞优势抗原表位区域,7个可能的糖基化位点。该分析预测结果不但有利于PERV疫苗的设计、单抗及诊断试剂研制,而且将有助于分析Env蛋白的功能及PERV对人源细胞的感染机制。

基于级联神经网络的蛋白质二级结构预测

为提高蛋白质二级结构预测的精度,提出一种由两层网络构成的级联神经网络模型。第1层网络采用具有差异度的5个子网构成的网络模型,对第2层网络的输入编码进行改进。对PDBSelect25中的36条蛋白质共6122个残基进行测试,结果表明,该模型能有效预测蛋白质二级结构,其预测精度分别比SNN,DSC,PREDSATOR方法提高5.31%,1.21%和0.92%,平均预测精度提高到69.61%。

RNA二级结构预测方法综述

RNA二级结构预测是计算分子生物学中的一个重要领域.本文介绍了RNA二级结构的预测方法,包括该问题的数学模型、主要算法思想以及每种算法对应的软件.在tRNA和RNase P RNA数据库中随机选取了几组样例对目前主要的7种软件进行测试,同时对每种软件的优缺点进行了详细比较.实验证明,当存在同源序列时,Pfold的效果优于其它软件.最后,在总结分析现有算法的基础上探讨了该领域进一步的研究方向.

模拟退火算法的一种改进及其应用研究

针对连续函数全局优化问题提出了改进的模拟退火算法:采用新的解扰动策略,并将局部极小化过程引入模拟退火算法.数值试验证实了该算法的可行性及有效性.作为该方法的应用,计算了著名的L ennard-Jones簇问题,通过比较说明新方法可以提高精度及成功率;此外对脑啡肽的空间结构进行了预测,也得到了较好的结果.