检索结果-内蒙古大学图书馆

Improved Chou-Fasman method for protein secondary structure prediction

BMC BIOINFORMATICS 2006年第s4期7卷 S14-S14页

作者： Chen, Hang Gu, Fei Huang, Zhengge Zhejiang Univ Dept Biomed Engn Coll Biomed Engn & Instrument Sci Hangzhou 310027 Peoples R China Zhejiang Univ Coll Life Sci Dept Biotechnol Hangzhou 310027 Peoples R China Zhejiang Univ Ctr Engn & Sci Computat Dept Comp Sci Hangzhou 310027 Peoples R China

Background: protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. Results: We improved Chou-Fasman method in three aspects. (a) Replace the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform. (b) Substitute the original secondary structure conformational parameters with folding type-specific secondary structure propensities. (c) Modify Chou-Fasman rules. The CB396 data set was tested by using improved Chou-Fasman method and three indices: Q3, Qpre, SOV were used to measure this method. We compared the indices with those obtained from the original Chou-Fasman method and other four popular methods. The results showed that our improved Chou-Fasman method performs better than the original one in all indices, about 10-18% improvement. It is also comparable to other currently popular methods considering all the indices. Conclusion: Our method has greatly improved Chou-Fasman method. It is able to predict protein secondary structure as good as current popular methods. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result.

关键词： secondary structure Continuous Wavelet Transform protein secondary structure secondary structure Information protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

Deep metric learning for accurate protein secondary structure prediction

引用

KNOWLEDGE-BASED SYSTEMS 2022年 242卷 108356-108356页

作者： Yang, Wei Liu, Yang Xiao, Chunjing Henan Univ Sch Comp & Informat Engn Henan Key Lab Big Data Anal & Proc Henan Engn Lab Spatial Informat Proc Kaifeng 475004 Peoples R China

Predicting the secondary structure of a protein from its amino acid sequence alone is a challenging prediction task for each residue in bioinformatics. Recent work has mainly used deep models based on the profile feature derived from multiple sequence alignments to make predictions. However, the existing state-of-the-art predictors usually have higher computational costs due to their large model sizes and complex network architectures. Here, we propose a simple yet effective deep centroid model for sequence-to-sequence secondary structure prediction based on deep metric learning. The proposed model adopts a lightweight embedding network with multibranch topology to map each residue in a protein chain into an embedding space. The goal of embedding learning is to maximize the similarity of each residue to its target centroid while minimizing its similarity to nontarget centroids. By assigning secondary structure types based on the learned centroids, we bypass the need for a time-consuming k-nearest neighbor search. Experimental results on six test sets demonstrate that our method achieves state-of-the-art performance with a simple architecture and smaller model size than existing models. Moreover, we also experimentally show that the embedding feature from the pretrained protein language model ProtT5-XL-U50 is superior to the profile feature in terms of prediction accuracy and feature generation speed. Code and datasets are available at https://***/fengtuan/DML_SS.(c) 2022 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Deep metric learning protein language model Profile feature Embedding feature

来源：评论

学校读者我要写书评

暂无评论

Influence of Encoding Scheme on protein secondary structure prediction

Influence of Encoding Scheme on Protein Secondary Structure ...

引用

7th World Congress on Intelligent Control and Automation

作者： Zou, Dongsheng He, Zhongshi He, Jingyuan Huang, Xiaofeng Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781424421138

secondary structure prediction of proteins has increasingly been a central research area in bioinformatics. In order to know which data encoding approach is more effective whiling predicting secondary structure using SVM, five approaches: ENCOrth, ENCFive, ENCCodBas, ENCCodExt and ENCProf are discussed in this paper. The results of data encoding are used as input of SVM. By performing ENCProf approach, the accuracy of Q3 can be improved 19.4%similar to 23.9% more than the other four approaches.

关键词： SVM data encoding protein secondary structure prediction Profile encoding

来源：评论

学校读者我要写书评

暂无评论

A New Deep Neighbor Residual Network For protein secondary structure prediction 29

A New Deep Neighbor Residual Network For Protein Secondary S...

引用

29th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Fang, Chao Shang, Yi Xu, Dong Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Missouri Bond Life Sci Ctr Columbia MO 65211 USA

ISBN: (纸本)9781538638767

A protein secondary structure defines the local conformation of the protein's polypeptide backbone, which provides important information for protein 3D structure prediction and protein functions. In this study, a new deep neural network, the deep neighbor residual network (DeepNRN), is proposed for protein secondary structure predictions. The network takes three types of inputs, namely protein sequence features, profile features generated by PSI-BLAST, and profile features generated by HHBlits, and predicts the protein secondary structure in either one of eight states (Q8) or one of three states (Q3). The basic building block of the network, the neighbor residual unit, is designed with two types of short-cut connections that are more general and expressive than residual units in existing residual deep neural networks, yet can still be computed efficiently. In addition, the prediction result of DeepNRN can be refined by a Struct2Struct network to make the result more protein-like. Extensive experimental results on multiple widely used benchmark data sets show that the new DeepNRN-based method outperformed existing methods and obtained the best results across multiple data sets.

关键词： protein secondary structure prediction deep learning deep neighbor residual network

来源：评论

学校读者我要写书评

暂无评论

Empirical knowledge and genetic algorithms for selection of amide I frequencies in protein secondary structure prediction 04

Empirical knowledge and genetic algorithms for selection of ...

引用

Asia-Pacific Bioinformatics Conference

作者： Joachim A. Hering Peter R. Innocent Parvez I. Haris De Montfort University Leicester UK

Here we investigate an extension of a previously suggested "automatic amide I frequency selection procedure" where we introduce an additional criterion utilizing empirical knowledge on regions within the amide I band (1600--1700 cm^-1) found to be particularly sensitive to protein secondary structure. We show that the genetic algorithm provides a solution with good protein secondary structure prediction *** on an evaluation set of 13 protein infrared spectra from proteins not contained in the reference set, it is demonstrated that our method is capable of making good predictions for proteins it has never seen before during training. In the present study, where the genetic algorithm is guided towards a solution with a higher number of empirically determined, structure sensitive amide I frequencies selected, minor improvement in prediction accuracy for α-helix and ?-sheet structure could be achieved compared to our previous study, where no such knowledge has been provided. Despite the very limited number of protein spectra in the reference set (18), the neural networks were able to generalize with an overall average of standard errors of prediction of 4.36 % based on the evaluation set of protein spectra, which is even better than that achieved during the analysis based on the reference set of protein spectra (4.8 %). This clearly indicates the potential of our approach once more protein infrared spectra are available to base the analysis on.

关键词： protein secondary structure prediction secondary protein structure Genetic Algorithm (GA) amides knowledge solidification Set (Psychology) I-band infrared spectra Alpha Helix Lamellar structures

来源：评论

学校读者我要写书评

暂无评论

Influence of Encoding Scheme on protein secondary structure prediction

Influence of Encoding Scheme on Protein Secondary Structure ...

引用

7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.4

作者： Dongsheng Zou Zhongshi He Jingyuan He Xiaofeng Huang College of Computer Science Chongqing University Chongqing China

关键词： SVM Data encoding protein secondary structure prediction Profile encoding

来源：评论

学校读者我要写书评

暂无评论

SAAS:Short Amino Acid Sequence - A Promising protein secondary structure prediction Method of Single sequence

SAAS:Short Amino Acid Sequence - A Promising Protein Seconda...

引用

The 3rd Asia Pacific protein Association Conference in Conjunction with the 3rd Symposium of the Chinese protein Society

作者： Yuanpai Zhou Ray P.S.Han School of Mathematics and Computational Science Xiangtan University Xiangtan P.R.China Civil Construction Engineering Department Guangxi University of Technology Department of Advanced Materials and Nanotechnology Peking University

In statistical methods of prediction protein secondary structure,many people focus on single amino acid frequencies in - helices,-sheets,and so on,or the impact near amino acids on a amino acid forming a secondary *** the paper considers short sequence of amino acids(3,4,5 or 6 amino acids) as integer,and statistics short sequence's probability forming secondary ***,many researchers select low homologous sequences as statistical *** this paper select whole PDB

关键词： component short amino acid sequence protein secondary structure prediction statistical method

来源：评论

学校读者我要写书评

暂无评论

The GOR Method of protein secondary structure prediction and Its Application as a protein Aggregation prediction Tool

The GOR Method of Protein Secondary Structure Prediction and...

引用

作者： Maksim Kouza Eshel Faraggi Andrzej Kolinski Andrzej Kloczkowski

The GOR method of protein secondary structure prediction is described. The original method was published by Garnier, Osguthorpe, and Robson in 1978 and was one of the first successful methods to predict protein secondary structure from amino acid sequence. The method is based on information theory, and an assumption that information function of a protein chain can be approximated by a sum of information from single residues and pairs of residues. The analysis of frequencies of occurrence of secondary structure for singlets and doublets of residues in a protein database enables prediction of secondary structure for new amino acid sequences. Because of these simple physical assumptions the GOR method has a conceptual advantage over other later developed methods such as PHD, PSIPRED, and others that are based on Machine Learning methods (like Neural Networks), give slightly better predictions, but have a “black box” nature. The GOR method has been continuously improved and modified for 30 years with the last GOR V version published in 2002, and the GOR V server developed in 2005. We discuss here the original GOR method and the GOR V program and the web server. Additionally we discuss new highly interesting and important applications of the GOR method to chameleon sequences in protein folding simulations, and for prediction of protein aggregation propensities. Our preliminary studies show that the GOR method is a promising and efficient alternative to other protein aggregation predicting tools. This shows that the GOR method despite being almost 40 years old is still important and has significant potential in application to new scientific problems. less

关键词： structure prediction protein secondary structure prediction Machine Learning protein structure prediction NMR GOR method protein aggregation Information theory GOR

来源：评论

学校读者我要写书评

暂无评论

A fast and efficient nearest neighbor method for protein secondary structure prediction

A fast and efficient nearest neighbor method for protein sec...

引用

2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

作者： Wei Yang Kuanquan Wang Wangmeng Zuo Biocomputing Research Centre School of Computer Science and Technology Harbin institute of Technology Harbin 150001 China

ISBN: (纸本)9781424488094

Using PSSM profiles, various machine learning methods have been successfully developed for protein secondary structure prediction. With the steady increase of protein structure data, the probability of having available homologous structural information of the protein in real prediction is now fairly high and will continue to increase. Therefore, how to effectively utilize the ever-growing protein structure data has become a huge challenge and opportunity. In this paper, we propose a novel nearest neighbor method, DPred, to use both homologous and non-homologous information for protein secondary structure prediction. On the dataset composed of new solved proteins, the method achieves the overall Q3 and SOV scores of 87.51％ and 86.50％, which is comparable with Porter_H and better than PROTEUS and CDM.

关键词： Nearest neighbor protein secondary structure prediction data mining bioinformatics

来源：评论

学校读者我要写书评

暂无评论

New Trend of protein secondary structure prediction

New Trend of Protein Secondary Structure Prediction

引用

2009 International Symposium on Intelligent Ubiquitous Computing and Education(2009国际智能普适计算与教育研讨会 IUCE 2009)

作者： Bingru Yang Lijun Wang Yun Zhai Bing An School of Information Engineering University of Science and Technology Beijing Beijing China School of Computer Liaocheng University Liaocheng China

At first, this paper reviews the development history of the protein secondary structure prediction. Some concerned secondary structure prediction methods are introduced. Then a novel method is proposed, which substantially improves the prediction accuracy of CB513 with 80.49％ and RSI26 with 82.79％ respectively. In the end, this paper points out several possible trends in the protein secondary structure prediction.

关键词： protein secondary structure prediction KDD combination method trend

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：