AF: Small: RUI: A new and improved algorithm for fitting RNA backbone in crystallographic data

RNA分子三维结构的精确细节对于理解RNA的功能非常重要，这反过来可以帮助我们理解生物系统，开发新药，改善人类健康。RNA三维结构分析的一个问题是，从生物实验中获得的结构经常包含错误，需要纠正。产生这些错误的主要原因是RNA的3D结构非常复杂。虽然已有从实验数据中获取蛋白质3D结构的自动工具，但这种工具尚未可用于rna。此前，PI开发了一个名为RNA主干校正（RNABC）的程序，该程序使用几何算法和机器人技术来搜索无错误的RNA结构。虽然RNABC已被用于纠正现有RNA结构中的结构错误，并已集成到MolProbity web服务中用于RNA结构验证，但还需要进一步的发展。研究表明，RNABC可以纠正40%到80%的RNA结构错误。该项目开发了一种新的改进的计算机程序，科学家可以使用它来纠正结构错误并获得RNA 3D结构的准确细节。目标是纠正超过80%的RNA结构中的错误，并保持相同的运行时间。新项目将结合机器学习、数据挖掘、机器人技术和数值分析等方法，寻找无差错的RNA结构。该项目将以多种方式推进算法研究，并帮助我们更好地了解RNA结构的细节。该项目为对计算机科学、数学和生物学感兴趣的学生提供了研究机会，并有助于培养下一代科学家。

Accurate details in 3D structures of RNA molecules are important for understanding RNA function, which can in turn help us understand biological systems, develop new medicines, and improve human health. One issue in RNA 3D structure analysis is that the structures obtained from biological experiments often contain errors and need to be corrected. The main reason for the errors is because RNA 3D structures are highly complex. While there are existing automatic tools for obtaining protein 3D structures from experimental data, such tools are not yet available for RNAs.Previously the PI developed a program called RNA Backbone Correction (RNABC) that uses geometric algorithms and robotics to search for error-free RNA structures. While RNABC has been used to correct structural errors in existing RNA structures and has been integrated into the MolProbity web service for RNA structure validation, further advancement is needed. Research shows that RNABC corrects errors in 40% to 80% of RNA structures tested.This project develops a new and improved computer program that scientists can use to correct structural errors and obtain accurate details of RNA 3D structures. The goal is to correct errors in over 80% of RNA structures and maintain the same running time. The new program will combine methods in machine learning, data mining, robotics and numerical analysis to search for error-free RNA structures. The project will advance algorithmic research in multiple ways and help us better understand the details of RNA structures. This project provides research opportunities to students interested in computer science, mathematics, and biology, and helps educate the next generation of scientists.