检索结果-内蒙古大学图书馆

19th IEEE Canadian Conference on Electrical and Computer Engineering

作者： Green, James R. Korenberg, Michael J. Carleton Univ Syst & Comp Engn Ottawa ON K1S 5B6 Canada Queens Univ Elect & Dept Comp Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9781424400379

Much like the shape of a tool suggests its intended purpose, knowledge of a protein's structure can provide substantial insight into its function. Therefore, computational prediction of protein structure based solely on protein sequence data is a challenge of fundamental importance to biomedical research. An effective solution promises significant advances in computational drug discovery and an increased understanding of complex disease processes such as cancer. We have recently developed a novel approach to determining the secondary structure of proteins from protein sequence data which makes use of Parallel Cascade Identification (M), a powerful method of nonlinear system identification. PCI is used to create two layers of dynamic nonlinear systems that map divergent evolutionary profile input data into secondary structure assignment output data. PCI prediction accuracy compares well with eleven top contemporary methods over a dataset of new protein structures. Furthermore, PCI is a highly effective means to combine multiple experts achieving the highest observed accuracy over two test datasets and also the lowest rate of occurrence of a particularly detrimental class of errors. One limitation of the PCI classifiers is that approximately 13% of all amino acids cannot readily be assigned predictions due to settling times introduced by the dynamic linear component in each cascade model. In this paper we describe a number of methods designed to overcome this limitation. While zero-padding of the input sequence data proved to be the most effective solution in terms of prediction accuracy, an analysis of causal, anti-causal, and mixed cascades provides interesting insights into the biological mechanism of protein folding.

关键词： protein secondary structure prediction nonlinear system identification parallel cascade identification bioinformatics

来源：评论

学校读者我要写书评

暂无评论

Functional structures and folding dynamics of two peptides

引用

Chinese Physics B 2005年第11期14卷 2365-2369页

作者：盛乐标李菁马保亮王炜 National Laboratory of Solid State Microstructure Institute of Biophysics and Department of Physlcs Nanjing University 210093 China Interdisciplinary Center of Theoretical Studies Chinese Academy of Sciences Beijing 100080 China

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.

关键词： peptide folding molecular dynamics protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

protein encoder: An autoencoder-based ensemble feature selection scheme to predict protein secondary structure

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年 213卷

作者： Uzma Manzoor, Usama Halim, Zahid Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Machine Intelligence Res Grp MInG Topi 23460 Pakistan Namal Univ Dept Comp Sci Mianwali Pakistan

proteins play a vital role in the human body as they perform important metabolic tasks. Experimental identi-fication of protein structure is expensive and time consuming. The prediction of protein secondary structure is significant to identify the protein tertiary structure and its folds. The feature subset selection from high dimensional protein primary sequence is a key to improve the accuracy of protein secondary structure prediction (PSSP). Therefore, it is essential to select the relevant features from high dimensional data to predict the protein secondary structure. This work presents a novel method for the PSSP problem based on a two-phase feature selection technique. The first stage utilizes an unsupervised autoencoder for feature extractions. Whereas, the second stage is an ensemble of three feature selection methods, namely, generic univariate select, recursive feature elimination, and Pearson's correlation. This phase combines multiple feature subsets using mutual in-formation to select the optimum feature subset. For classification, different resultant subset features are used. These include random forest, decision tree, and multilayer perceptron. Two sets of experiments are performed on five datasets for the assessment of proposed work. The proposed solution is compared with three state-of-the-art methods based on Q3 accuracy, Q8 accuracy, and segment overlap score. Obtained results show that the pro-posed framework performs better in the majority of the cases than the past contributions. The proposed framework achieves Q8 accuracies of 82%, 80%, 79%, 73%, and 74% and Q3 accuracies of 90%, 90%, 92%, 79%, and 74% on CB6133, CB6133-filtered, CB513, CASP10, and CASP11 datasets, respectively.

关键词： protein secondary structure prediction Ensemble methods Autoencoder Feature extraction Amino acids

来源：评论

学校读者我要写书评

暂无评论

Predicting the protein structure using random forest approach

引用

Procedia Computer Science 2018年 132卷 1654-1662页

作者： Charu Kathuria Deepti Mehrotra Navnit Kumar Misra Amity University Noida Uttar Pradesh 201313 India Brahmanand College The Mall Kanpur 208004 India

Predicting the secondary structure of proteins is a challenging task. A large variety approaches exist that include observation using equipment’s and theoretical evaluation, in which the optimal structure is determined. The secondary structure determines 3D tertiary structure of protein, on which features and functionalities of protein depend. This paper use classification technique, Random Forest to build a model which is able to determine structure of unknown proteins. The dataset included the amide frequencies of proteins whose structure is known. Machine learning model is developed that can predict the structure of protein that still need to be exploited. The accuracy of the model is determined using ROC curve. The results confirm the performance of the model constructed using amides dataset.

关键词： protein secondary structure prediction amide frequencies random forest ROC area under the curve

来源：评论

学校读者我要写书评

暂无评论

A high-throughput predictive method for sequence-similar fold switchers

引用

BIOPOLYMERS 2021年第10期112卷 e23416页

作者： Kim, Allen K. Looger, Loren L. Porter, Lauren L. NIH Natl Lib Med Bethesda MD 20894 USA NHLBI NIH Bldg 10 Bethesda MD 20892 USA Howard Hughes Med Inst Janelia Res Campus Ashburn VA USA

Although most experimentally characterized proteins with similar sequences assume the same folds and perform similar functions, an increasing number of exceptions is emerging. One class of exceptions comprises sequence-similar fold switchers, whose secondary structures shift from alpha-helix beta-sheet through a small number of mutations, a sequence insertion, or a deletion. Predictive methods for identifying sequence-similar fold switchers are desirable because some are associated with disease and/or can perform different functions in cells. Here, we use homology-based secondary structure predictions to identify sequence-similar fold switchers from their amino acid sequences alone. To do this, we predicted the secondary structures of sequence-similar fold switchers using three different homology-based secondary structure predictors: PSIPRED, JPred4, and SPIDER3. We found that alpha-helix beta-strand prediction discrepancies from JPred4 discriminated between the different conformations of sequence-similar fold switchers with high statistical significance (P < 1.8*10(-19)). Thus, we used these discrepancies as a classifier and found that they can often robustly discriminate between sequence-similar fold switchers and sequence-similar proteins that maintain the same folds (Matthews Correlation Coefficient of 0.82). We found that JPred4 is a more robust predictor of sequence-similar fold switchers because of (a) the curated sequence database it uses to produce multiple sequence alignments and (b) its use of sequence profiles based on Hidden Markov Models. Our results indicate that inconsistencies between JPred4 secondary structure predictions can be used to identify some sequence-similar fold switchers from their sequences alone. Thus, the negative information from inconsistent secondary structure predictions can potentially be leveraged to identify sequence-similar fold switchers from the broad base of genomic sequences.

关键词： bioinformatics metamorphic proteins protein fold switching protein secondary structure prediction protein structure prediction

来源：评论

学校读者我要写书评

暂无评论

Yeast POL5 Is an Evolutionarily Conserved Regulator of rDNA Transcription Unrelated to Any Known DNA Polymerases

引用

CELL CYCLE 2003年第2期2卷 120-122页

作者： Yang, Wei Rogozin, Igor B. Koonin, Eugene V. Natl Lib Med Natl Ctr Biotechnol Informat NIH 8600 Rockville PikeBldg 38A Bethesda MD 20894 USA NIDDK Mol Biol Lab NIH Bethesda MD 20892 USA

We show that yeast protein Yel055cp, which has been identified as the fifth essential DNA polymerase in Saccharomyces cerevisiae (POL5), is a member of a family of predicted rDNA transcription regulators (typified by human MYB-binding protein MYBBP1A), which are represented by a single ortholog in all animals, fungi and plants with sequenced genomes. These proteins are confidently predicted to have an entirely a-helical structure and are unrelated to the B class DNA polymerases, as claimed for yeast POL5, or any other known polymerases.

关键词： DNA polymerase Transcription regulation MYB-binding proteins Conserved sequence motifs Multiple alignment protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

COMPUTERIZED VERSION OF THE CHOU AND FASMAN protein secondary structure PREDICTIVE METHOD

引用

INTERNATIONAL JOURNAL OF PEPTIDE AND protein RESEARCH 1983年第5期22卷 515-524页

作者： RAWLINGS, N ASHMAN, K WITTMANNLIEBOLD, B MAX PLANCK INST MOLEC GENET WITTMANN ABT D-1000 BERLIN 33 FED REP GER

A DEC-SYSTEM 10 FORTRAN computer program to carry out secondary structure prediction of proteins, according to the algorithm of Chou and Fasman is described. Program results are compared with predictions made by Chou ... 详细信息

关键词： Chou and Fasman computerised FORTRAN protein secondary structure prediction

来源：评论

学校读者我要写书评

暂无评论

RaptorX server: A Resource for Template-Based protein structure Modeling

RaptorX server: A Resource for Template-Based Protein Struct...

引用

作者： Morten Källberg Gohar Margaryan Sheng Wang Jianzhu Ma Jinbo Xu

Assigning functional properties to a newly discovered protein is a key challenge in modern biology. To this end, computational modeling of the three-dimensional atomic arrangement of the amino acid chain is often crucial in determining the role of the protein in biological processes. We present a community-wide web-based protocol, RaptorX server (http://*** ), for automated protein secondary structure prediction, template-based tertiary structure modeling, and probabilistic alignment sampling. Given a target sequence, RaptorX server is able to detect even remotely related template sequences by means of a novel nonlinear context-specific alignment potential and probabilistic consistency algorithm. Using the protocol presented here it is thus possible to obtain high-quality structural models for many target protein sequences when only distantly related protein domains have experimentally solved structures. At present, RaptorX server can perform secondary and tertiary structure prediction of a 200 amino acid target sequence in approximately 30 min. less

关键词： protein structure prediction Homology Modeling protein structure Analysis protein secondary structure prediction structure prediction Jmol Model quality assessment protein threading

来源：评论

学校读者我要写书评

暂无评论

prediction of protein–protein Interaction Based on structure

Prediction of Protein–Protein Interaction Based on Structur...

引用

作者： Gregorio Fernandez-Ballester Luis Serrano

A great challenge in the proteomics and structural genomics era is to predict protein structure and function from sequence, including the identification of biological partners. The development of a procedure to construct position-specific scoring matrices for the prediction and identification of sequences with putative significant affinity faces this challenge. The local and web applications used for sequence and structure search, sequence alignment, protein modeling, molecule edition and modification, and scoring matrices construction are described in detail. The methodology is based on the information contained in structural databases and takes into account the subtle conformational and sequence details that characterize different structures within a family. Using the matrices, the protein sequence databases can be easily scanned to locate putative partners of biological significance. The success of this methodology opens the way for the prediction of protein-protein interaction at genome scale. less

关键词： Homology Modeling Database Search Proteomics Genomics protein structure Analysis Position-Specific Scoring Matrices Multiple Sequence Alignment ClustalW Molecular Dynamics Simulation Statistical Calculation Molecular Modeling protein Engineering Molecular Docking Simulated Annealing AutoDock Docking Method Jpred Ab Initio structure prediction PROSITE protein secondary structure prediction GlobPlot Python Swiss-Prot Expasy TrEMBL protein Bioinformatics protein–protein interaction prediction positional scoring matrix pattern search

来源：评论

学校读者我要写书评

暂无评论

Investigation of diabetes-induced effect on apex of rat heart myocardium by using cluster analysis and neural network approach: An FTIR study

引用

SPECTROSCOPY-AN INTERNATIONAL JOURNAL 2007年第5-6期21卷 269-278页

作者： Toyran, Neslihan Severcan, Feride Severcan, Mete Turan, Belma Baskent Univ Fac Med Dept Physiol TR-06530 Ankara Turkey Middle E Tech Univ Dept Biol Sci TR-06531 Ankara Turkey Middle E Tech Univ Dept Elect & Elect Engn TR-06531 Ankara Turkey Ankara Univ Sch Med Dept Biophys TR-06100 Ankara Turkey

Diabetes mellitus (DM) is a progressive chronic disorder, which affects people belonging to all age groups of the population. This disease is accompanied by a greatly increased risk of cardiovascular death. In the present study, the effects of streptozotocin (STZ)-induced type 1 diabetes on apex myocardium of the rat heart have been investigated using Fourier Transform Infrared (FTIR) spectra. The cluster analysis has been applied to FTIR spectra to differentiate the diabetic samples from the normal controls. In addition, the protein secondary structures of diabetic and normal tissues were predicted by neural networks based on the amide I band of the FTIR spectra. The findings mainly suggest that 5 weeks of diabetes alters the lipid and protein profile of normal rat heart apex myocardium, which might have an important role in understanding the molecular mechanism of diabetes-related heart diseases.

关键词： diabetes FTIR cluster analysis neural networks protein secondary structure prediction apex myocardium

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：