A new greedy heuristic for 3DHP protein struture prediction with side chain

L. C. Galvao, L. Nunes, H. S. Lopes, P. Moscato
{"title":"A new greedy heuristic for 3DHP protein struture prediction with side chain","authors":"L. C. Galvao, L. Nunes, H. S. Lopes, P. Moscato","doi":"10.1109/BIBMW.2012.6470229","DOIUrl":null,"url":null,"abstract":"In spite of the fact that many models of protein structure prediction have been proposed and have also been widely studied in the last years, little attention has been given to the discrete models with side chains. Few papers present algorithms that try to predict the 3 dimensional structures of protein from their amino acid sequences represented by a backbone and the side chains (hydrophobic or hydrophilic). In this paper, we propose a new greedy heuristic with a pull-move set for finding these structures to the 3DHP-SC model, i.e. for a three-dimensional model on a cubic lattice, with side chains. To demonstrate the performance of our method, we have used 25 benchmark instances from the literature. For the instances tested, the proposed technique matched the best known results for 12 instances and obtained better results for the other 13. The computational resources that we have used have been relatively limited in comparison with other studies in the literature, and the quality of our results shows the potential of the approach both in terms of quality and total computation time.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBMW.2012.6470229","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

In spite of the fact that many models of protein structure prediction have been proposed and have also been widely studied in the last years, little attention has been given to the discrete models with side chains. Few papers present algorithms that try to predict the 3 dimensional structures of protein from their amino acid sequences represented by a backbone and the side chains (hydrophobic or hydrophilic). In this paper, we propose a new greedy heuristic with a pull-move set for finding these structures to the 3DHP-SC model, i.e. for a three-dimensional model on a cubic lattice, with side chains. To demonstrate the performance of our method, we have used 25 benchmark instances from the literature. For the instances tested, the proposed technique matched the best known results for 12 instances and obtained better results for the other 13. The computational resources that we have used have been relatively limited in comparison with other studies in the literature, and the quality of our results shows the potential of the approach both in terms of quality and total computation time.
带侧链的3DHP蛋白结构预测贪心启发式算法
尽管近年来人们提出了许多蛋白质结构预测模型并对其进行了广泛的研究,但对带有侧链的离散模型的关注却很少。很少有论文提出算法,试图预测蛋白质的三维结构,从它们的氨基酸序列表示的主链和侧链(疏水或亲水)。在本文中,我们提出了一种新的贪心启发式算法,用于寻找3DHP-SC模型的这些结构,即在立方晶格上的三维模型,具有侧链。为了演示我们方法的性能,我们使用了文献中的25个基准实例。对于测试的实例,所建议的技术与12个实例的最佳结果相匹配,并在另外13个实例中获得更好的结果。与文献中的其他研究相比,我们使用的计算资源相对有限,我们结果的质量显示了该方法在质量和总计算时间方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信