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.

蛋白质测序在识别蛋白质功能、分析蛋白质-蛋白质相互作用和表征翻译后修饰方面发挥着重要作用。尽管最近在蛋白质测序和组装方面取得了进展，但目前许多可用的组装蛋白质都是草稿形式。即使将自上而下和自下而上的测序方法结合起来，组装的蛋白质序列仍然存在许多间隙。换句话说，在特定蛋白质的测序步骤结束时，更有可能看到以间隙分隔的重叠群（称为支架）。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。借助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强大的计算工具来获取抗体的序列信息，这对于抗体数据库的构建极具价值。这种跨学科研究还为各个层次的学生，特别是代表性不足的非洲裔美国学生提供各种培训项目，帮助他们从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生/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、教程和研讨会演讲来传播。 在这个项目中，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法，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，它们还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，完成自上而下和自下而上串联质谱获得的蛋白质测序的最后一步第一步分析。该项目将产生一个结合组合和深度学习方法的蛋白质支架填充新框架，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智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。