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: 2307571
• 负责人: Letu Qingge
• 金额: $ 10.14万
• 依托单位: North Carolina Agricultural & Technical State 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=2307571&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 - 蛋白质相互作用并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，但许多当前可用的组装蛋白都以草稿形式出现。即使将自上而下和自下而上的测序方法结合在一起，组装蛋白序列中仍然存在许多差距。换句话说，在特定蛋白质的测序步骤结束时，更有可能看到与间隙分开的重叠群（这称为脚手架）。因此，一个重要但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。通过该项目生产的新框架，从头蛋白质测序将大大提高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，以获取抗体的序列信息，这对于构建抗体数据库非常有价值。这项跨学科研究还为各个级别的学生，尤其是代表性不足的非裔美国学生提供了各种培训项目，并帮助他们从开放的思想和跨学科的角度从事高质量的研究。实现的新进步将集成到本科/研究生课程中。结果将通过期刊出版物，会议，开源软件发布，教程和研讨会谈话来传播。在该项目中，研究人员将通过两个相关阶段研究基于质谱的DE NOVO蛋白脚手架填充问题。首先，研究人员将分析自上而下和自下而上的串联质谱法，以构建蛋白质支架，而无需适当的参考。这些方法包括一般的全局优化，动态编程和图算法，这些算法也可以处理小蛋白质突变（某些氨基酸的质量变化）。其次，研究人员将使用深度学习方法，例如卷积神经网络和长期短期记忆，卷积降级自动编码器和变压器模型，以完成第一步，通过自上而下和自下而上的串联质谱分析获得蛋白质测序的最后一步。该项目将为蛋白质脚手架填充的组合组合和深度学习方法提供新的框架，并提供相应的开源软件。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响审查标准通过评估来进行评估的。