Collaborative Research: FET: Small: De Novo Protein Scaffold Filling by Combinatorial Algorithms and Deep Learning Models

合作研究：FET：小型：通过组合算法和深度学习模型从头填充蛋白质支架

• 批准号: 2307573
• 负责人: Xiaowen Liu
• 金额: $ 9.92万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307573&HistoricalAwards=false
• 关键词: Collaborative; Research; FET; Small; De

Protein sequencing plays an important role in identifying protein functions, analyzing protein-protein interactions, and characterizing post-translational modifications. Despite the recent progress in protein sequencing and assembly, many of the currently available assembled proteins come in a draft form. There are still many gaps in the assembled protein sequences even if one combines top-down and bottom-up sequencing methods. In other words, at the end of the sequencing step for a specific protein, it is more likely to see contigs separated with gaps (which is called a scaffold). Hence, an important but also natural combinatorial problem is to fill the missing amino acids into a scaffold to obtain a complete protein sequence. With the new framework produced by this project, de novo protein sequencing will greatly advance the research and clinical practice of identifying the function and structure of proteins. The project will provide researchers with powerful computational tools for obtaining the sequence information of antibodies, which is extremely valuable for the construction of antibody databases. This interdisciplinary research also provides various training projects to students at all levels, particularly to underrepresented African American students, and helps them to pursue high quality research from an open-minded and cross-disciplinary perspective. New advances achieved will be integrated into undergraduate/graduate curricula. The results will be disseminated through journal publications, conferences, open-source software release, tutorials, and seminar talks.In this project, the investigators will study the mass spectrometry-based de novo protein scaffold filling problem by two related phases. Firstly, the investigators will analyze the top-down and bottom-up tandem mass spectrometry to construct the protein scaffold without a proper reference. The methods include general global optimization, dynamic programming, and graph algorithms, which can also handle small protein mutations (where the mass of some amino acid changes). Secondly, the investigators will use deep learning methods, such as combined convolutional neural network and long short-term memory, convolutional denoising autoencoder, and transformer models to finish the last step of protein sequencing obtained by top-down and bottom-up tandem mass spectrometry analysis at first step. The project will result in a new framework of combined combinatorial and deep learning methods for protein scaffold filling, and a corresponding open-source software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蛋白质测序在鉴定蛋白质功能、分析蛋白质-蛋白质相互作用和表征翻译后修饰方面起着重要作用。尽管最近在蛋白质测序和组装方面取得了进展，但许多目前可用的组装蛋白质都是以草稿形式出现的。即使结合自上而下和自下而上的测序方法，组装的蛋白质序列中仍然存在许多缺口。换句话说，在特定蛋白质的测序步骤结束时，更有可能看到重叠群被间隙（称为支架）隔开。因此，一个重要但也是自然的组合问题是将缺失的氨基酸填充到支架中以获得完整的蛋白质序列。通过该项目产生的新框架，从头蛋白质测序将极大地推进鉴定蛋白质功能和结构的研究和临床实践。该项目将为研究人员提供获取抗体序列信息的强大计算工具，这对于抗体数据库的构建极具价值。这种跨学科的研究还提供了各种培训项目，以学生在各级，特别是代表性不足的非洲裔美国学生，并帮助他们从一个开放的思想和跨学科的角度追求高质量的研究。取得的新进展将纳入本科/研究生课程。研究结果将通过期刊出版物、会议、开源软件发布、教程和研讨会讲座进行传播。在本项目中，研究人员将通过两个相关阶段研究基于质谱的从头蛋白质支架填充问题。首先，研究人员将分析自上而下和自下而上的串联质谱，以在没有适当参考的情况下构建蛋白质支架。这些方法包括一般全局优化，动态规划和图形算法，也可以处理小的蛋白质突变（其中一些氨基酸的质量发生变化）。其次，研究人员将使用深度学习方法，例如组合卷积神经网络和长短期记忆，卷积去噪自动编码器和Transformer模型，以完成第一步由自上而下和自下而上串联质谱分析获得的蛋白质测序的最后一步。该项目将为蛋白质支架填充提供一个新的组合和深度学习方法框架，以及相应的开源软件。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。