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一种用于蛋白质接触图重叠分析教育推广的交互式可视化工具。

An interactive visualization tool for educational outreach in protein contact map overlap analysis.

作者信息

Baker Kevan, Hughes Nathaniel, Bhattacharya Sutanu

机构信息

Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States.

Department of Computer Science and Computer Information Systems, Auburn University at Montgomery, Montgomery, AL, United States.

出版信息

Front Bioinform. 2024 Mar 15;4:1358550. doi: 10.3389/fbinf.2024.1358550. eCollection 2024.

Abstract

Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.

摘要

基于接触图的蛋白质三维(3D)结构预测的最新进展是由深度学习算法的发展推动的。然而,该领域新手可用软件工具的差距仍然是一个重大挑战。本研究介绍了GoFold,这是一种新颖的独立图形用户界面(GUI),专为初学者设计,用于执行接触图重叠(CMO)问题,以更好地选择模板。与现有工具更迎合研究需求或假定基础知识不同,GoFold提供了一个直观、用户友好的平台,并配有全面的教程。它在直观呈现CMO问题方面表现突出,允许用户以各种格式输入蛋白质并探索CMO问题。通过使用PSICOV和CAMEO这两个数据集,与最先进的接触图重叠方法map_align进行基准测试,证明了GoFold的教育价值。在各种接触图质量和目标难度方面,GoFold在TM分数和Z分数指标上表现出卓越的性能。值得注意的是,GoFold在个人电脑上运行高效,无需任何第三方依赖,从而使公众能够使用它来推动公民科学。该工具可免费下载,适用于macOS、Linux和Windows。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a661/10982686/dca0952d46de/fbinf-04-1358550-g001.jpg

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