Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Protein Organization

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

Combining Transcatheter Edge-to-Edge Repair and Commissural Plug Occlusion for Complex Degenerative Mitral Regurgitation.

JACC. Case reports·2026

Same author

Construction of <i>ortho</i>-C-N Bond and <i>ipso</i>-C(sp<sup>2</sup>)-C(sp<sup>3</sup>) Bond by Amination/Decarboxylation Catellani Reaction.

Organic letters·2026

Same author

Systematic Modeling of Covalent Inhibitors of SARS-CoV-2 Main Protease and Its Mutants.

ACS catalysis·2026

Same author

Retinal and Optic-Disc Changes Associated With ¹³¹I Therapy in Papillary Thyroid Carcinoma and Their Determinants.

Investigative ophthalmology & visual science·2026

Same author

Computational Insights into the Activation Mechanism of CXCR4: Implications for the Design of Small Molecule Agonists.

Journal of the American Chemical Society·2026

Same author

Mesenchymal stem cell therapy in autoimmune eye diseases: Focus on dry eye diseases and autoimmune uveitis.

Experimental eye research·2026

Search research articles

相关实验视频

Updated: Jan 16, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

SARST2高吞吐量和资源效率的蛋白质结构与大规模数据库对齐.

Wei-Cheng Lo^1,2,3,4, Arieh Warshel⁵, Chia-Hua Lo^6,7

¹Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. WadeLo@nycu.edu.tw.

Nature communications

|September 30, 2025

概括

此摘要是机器生成的。

一个新的蛋白质结构对齐算法,SARST2,为大型生物数据库提供更快,更准确的搜索. 这种计算工具可以帮助研究人员高效地分析蛋白质结构,即使在个人电脑上也是如此.

更多相关视频

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

相关实验视频

Last Updated: Jan 16, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
结构生物学结构生物学

背景情况:

蛋白质结构数据的快速增长给研究人员带来了重大的计算挑战.
现有的蛋白质结构对齐工具可能会与生物大数据的日益扩大规模和复杂性作斗争.

研究的目的:

开发一种高效准确的蛋白质结构对齐搜索算法,以满足日益增长的计算需求.
介绍SARST2,一种新的算法,旨在帮助研究人员分析大型蛋白质结构数据集.

主要方法:

SARST2集成了初级,二级和三级蛋白质的结构特征与进化统计数据.
它使用了一种机器学习增强的过和精炼策略,对角切换字符匹配的捷径,以及基于联系人号码的加权评分方案.
基于替换的可变差距处罚被纳入,以提高对齐准确度.

主要成果:

在大规模基准中,SARST2表现出与最先进的方法相比更高的准确性.
它实现了显著更快的搜索速度,并且比BLAST和Foldseek对AlphaFold数据库搜索所需的内存要少得多.
该算法可以在普通个人电脑上进行大规模的数据库搜索.

结论:

SARST2为蛋白质结构大数据提供了一个高效和强大的分析引擎.
它在速度和内存使用方面的性能提升为生物研究提供了更广泛的可访问性.
该算法支持研究人员通过先进的计算分析推动生物科学和技术的前沿.