Suppr超能文献

FELLS:潜在局部结构的快速估计器。

FELLS: fast estimator of latent local structure.

机构信息

Department of Biomedical Sciences, University of Padua, Padova, Italy.

Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.

出版信息

Bioinformatics. 2017 Jun 15;33(12):1889-1891. doi: 10.1093/bioinformatics/btx085.

Abstract

MOTIVATION

The behavior of a protein is encoded in its sequence, which can be used to predict distinct features such as secondary structure, intrinsic disorder or amphipathicity. Integrating these and other features can help explain the context-dependent behavior of proteins. However, most tools focus on a single aspect, hampering a holistic understanding of protein structure. Here, we present Fast Estimator of Latent Local Structure (FELLS) to visualize structural features from the protein sequence. FELLS provides disorder, aggregation and low complexity predictions as well as estimated local propensities including amphipathicity. A novel fast estimator of secondary structure (FESS) is also trained to provide a fast response. The calculations required for FELLS are extremely fast and suited for large-scale analysis while providing a detailed analysis of difficult cases.

AVAILABILITY AND IMPLEMENTATION

The FELLS web server is available from URL: http://protein.bio.unipd.it/fells/ . The server also exposes RESTful functionality allowing programmatic prediction requests. An executable version of FESS for Linux can be downloaded from URL: protein.bio.unipd.it/download/.

CONTACT

silvio.tosatto@unipd.it.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

蛋白质的行为由其序列编码,可以用于预测二级结构、内在无序或两亲性等不同特征。整合这些特征和其他特征可以帮助解释蛋白质的上下文相关行为。然而,大多数工具都侧重于单一方面,阻碍了对蛋白质结构的整体理解。在这里,我们提出了快速估计潜在局部结构(FELLS),以从蛋白质序列中可视化结构特征。FELLS 提供无序、聚集和低复杂度预测以及估计的局部倾向,包括两亲性。还训练了一种新的快速二级结构估计器(FESS),以提供快速响应。FELLS 的计算速度极快,适合大规模分析,同时对困难情况进行详细分析。

可用性和实现

FELLS 网络服务器可从 URL:http://protein.bio.unipd.it/fells/ 获得。服务器还公开了允许编程预测请求的 RESTful 功能。适用于 Linux 的 FESS 可执行版本可从 URL:protein.bio.unipd.it/download/ 下载。

联系人

silvio.tosatto@unipd.it

补充信息

补充数据可在“Bioinformatics”在线获取。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验