Computers & chemistry最新文献_第8页

Predicting function from structure: examples of the serine protease inhibitor canonical loop conformation found in extracellular proteins 从结构预测功能:在细胞外蛋白中发现的丝氨酸蛋白酶抑制剂典型环构象的例子

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00097-3

Richard M Jackson , Robert B Russell

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引用次数: 10

Modelling protein side-chain conformations using constraint logic programming 用约束逻辑编程建模蛋白质侧链构象

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00103-6

Martin T Swain , Graham J.L Kemp

引用次数: 8

AI-based algorithms for protein surface comparisons 基于人工智能的蛋白质表面比较算法

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00102-4

Steven J Pickering , Andrew J Bulpitt , Nick Efford , Nicola D Gold , David R Westhead

引用次数: 22

Artificial Intelligence in Bioinformatics 生物信息学中的人工智能

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00104-8

Dave W Corne , Andrew C.R Martin

引用次数: 3

Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome 延时神经网络在果蝇基因组启动子注释中的应用

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00099-7

Martin G Reese

{"title":"Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome","authors":"Martin G Reese","doi":"10.1016/S0097-8485(01)00099-7","DOIUrl":"10.1016/S0097-8485(01)00099-7","url":null,"abstract":"<div><p>Computational methods for automated genome annotation are critical to understanding and interpreting the bewildering mass of genomic sequence data presently being generated and released. A neural network model of the structural and compositional properties of a eukaryotic core promoter region has been developed and its application for analysis of the <em>Drosophila melanogaster</em> genome is presented. The model uses a time-delay architecture, a special case of a feed-forward neural network. The structure of this model allows for variable spacing between functional binding sites, which is known to play a key role in the transcription initiation process. Application of this model to a test set of core promoters not only gave better discrimination of potential promoter sites than previous statistical or neural network models, but also revealed indirectly subtle properties of the transcription initiation signal. When tested in the <em>Adh</em> region of 2.9 Mbases of the <em>Drosophila</em> genome, the neural network for promoter prediction (<span>nnpp</span>) program that incorporates the time-delay neural network model gives a recognition rate of 75% (69/92) with a false positive rate of 1/547 bases. The present work can be regarded as one of the first intensive studies that applies novel gene regulation technologies to the identification of the complex gene regulation sites in the genome of <em>Drosophila melanogaster</em>.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 1","pages":"Pages 51-56"},"PeriodicalIF":0.0,"publicationDate":"2001-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00099-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81750853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 820

Applications of neural network prediction of conformational states for small peptides from spectra and of fold classes 从光谱和折叠类预测小肽构象状态的神经网络应用

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00101-2

H.G Bohr , P Røgen , K.J Jalkanen

{"title":"Applications of neural network prediction of conformational states for small peptides from spectra and of fold classes","authors":"H.G Bohr , P Røgen , K.J Jalkanen","doi":"10.1016/S0097-8485(01)00101-2","DOIUrl":"10.1016/S0097-8485(01)00101-2","url":null,"abstract":"<div><p>Electronic structures of small peptides were calculated ‘ab initio’ with the help of Density Functional Theory (DFT) and molecular dynamics that rendered a set of conformational states of the peptides. For the structures of these states it was possible to derive atomic polar tensors that allowed us to construct vibrational spectra for each of the conformational states with low energy. From the spectra, neural networks could be trained to distinguish between the various states and thus be able to generate a larger set of relevant structures and their relation to secondary structures of the peptides. The calculations were done both with solvent atoms (up to ten water molecules) and without, and hence the neural networks could be used to monitor the influence of the solvent on hydrogen bond formation. The calculations at this stage only involved very short peptide fragments of a few alanine amino acids but already at this stage they could be compared with reasonable agreements to experiments. The neural networks are shown to be good in distinguishing the different conformers of the small alanine peptides, especially when in the gas phase. Also the task of predicting protein fold-classes, defined from line-geometry, seems promising.</p></div>","PeriodicalId":79331,"journal":{"name":"Computers & chemistry","volume":"26 1","pages":"Pages 65-77"},"PeriodicalIF":0.0,"publicationDate":"2001-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0097-8485(01)00101-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73285226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A computer system to perform structure comparison using TOPS representations of protein structure 一种利用蛋白质结构的TOPS表示进行结构比较的计算机系统

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00096-1

David Gilbert , David Westhead , Juris Viksna , Janet Thornton

引用次数: 32

Generating protein three-dimensional fold signatures using inductive logic programming 利用归纳逻辑编程生成蛋白质三维折叠特征

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00100-0

M Turcotte , S.H Muggleton , M.J.E Sternberg

引用次数: 7

Medical target prediction from genome sequence: combining different sequence analysis algorithms with expert knowledge and input from artificial intelligence approaches 从基因组序列预测医学靶标:将不同的序列分析算法与专家知识和人工智能方法的输入相结合

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00095-X

Thomas Dandekar , Fuli Du , R.Heiner Schirmer , Steffen Schmidt

引用次数: 9

Drug design by machine learning: support vector machines for pharmaceutical data analysis 基于机器学习的药物设计:支持向量机用于药物数据分析

Computers & chemistry Pub Date : 2001-12-01 DOI: 10.1016/S0097-8485(01)00094-8

R. Burbidge, M. Trotter, B. Buxton, S. Holden

引用次数: 620