Deep transformers for integrating protein sequence, structure and interaction data to predict function

蛋白质是生命系统中最基本的大分子。了解蛋白质的功能对生物学研究和技术发展具有重要意义。然而，大多数蛋白质的功能仍然是未知的。为了填补这一空白，该项目旨在开发最强大的人工智能（AI）技术之一——深度学习方法，整合蛋白质序列、结构和相互作用等多种蛋白质数据来源，以准确预测蛋白质功能。该方法将推动蛋白质功能预测的发展，在生命科学研究、生物技术开发、农业和医疗保健等领域具有广泛的应用前景。该项目将提供独特的跨学科研究机会，以培养不同层次的学生，包括具有不同背景的少数民族学生，将人工智能应用于解决基本的科学和技术问题。本项目将开发基于自关注的深层变形模型，整合蛋白质序列、结构和相互作用数据，显著推进蛋白质水平功能和氨基酸水平功能的预测。具体而言，它旨在实现三个目标：(1)开发1D和3D转换器，从多个序列比对和结构中预测蛋白质功能；(2)开发2D图形转换器，从蛋白质相互作用中预测蛋白质功能，并将其与序列和结构相结合；(3)实现变压器作为社区用户友好、准确、鲁棒的开源蛋白质功能预测工具。将开发基于自关注机制的前沿深层变压器模型，首次整合蛋白质序列、结构和相互作用数据来预测蛋白质功能。与现有基于传统卷积和循环机制的深度学习方法相比，基于一维序列转换器、二维图转换器和三维等变图转换器可以更好地提取多序列比对中的氨基酸保守性和远程协同进化信号、蛋白质-蛋白质网络中的远程相互作用以及蛋白质结构的旋转和翻译不变/等变特性。通过多任务学习和新颖的深度学习架构来预测整体蛋白质水平的功能项和残基水平的功能位点，可以利用这两种预测任务的互补来提供更准确、更完整、更可解释的功能预测。该项目将为社区提供用户友好的开源工具，从序列、结构和交互数据中准确预测功能，这将有助于减少蛋白质序列和功能之间巨大的知识差距。开源的深度学习工具可以用于预测和研究蛋白质在许多领域的功能。这些方法和工具将用于培养多层次的学生，并增加科学研究和教育的多样性。该项目的结果可以在https://calla.rnet.missouri.edu/cheng/nsf_protein_function.htmlThis上找到，该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

Proteins are fundamental macromolecules in the living systems. The knowledge about the function of proteins is important for biological research and technology development. However, the function of most proteins is still unknown. To fill the gap, this project aims to develop deep learning methods, one of the most powerful artificial intelligence (AI) technologies, to integrate multiple sources of protein data such as protein sequences, structures, and interaction to accurately predict protein function. The methods will advance the state of the art of protein function prediction and can be broadly applied in many domains such as life science research, biotechnology development, agriculture, and healthcare. The project will provide unique interdisciplinary research opportunities to train students at multiple levels including under-represented minority students with diverse backgrounds to apply AI to address fundamental scientific and technological problems. The project will develop deep transformer models based on self-attention to integrate protein sequence, structure, and interaction data to significantly advance the prediction of both protein-level function and amino acid-level function. Specifically, it aims to achieve three objectives: (1) develop 1D and 3D transformers to predict protein function from multiple sequence alignments and structures; (2) develop 2D graph transformers to predict protein function from protein-protein interactions and integrate them with sequences and structures; and (3) implement transformers as user-friendly, accurate, robust open-source protein function prediction tools for the community. Cutting-edge deep transformer models based on the self-attention mechanism will be developed to integrate protein sequence, structure, and interaction data to predict protein function for the first time. 1D sequence-based transformer, 2D graph transformer, and 3D-equivariant graph transformer can extract amino acid conservation and long-range co-evolutionary signals in multiple sequence alignments, long-range interactions in protein-protein networks, and rotation- and translation-invariant/equivariant properties of protein structures better than the existing deep learning methods based on traditional convolutional and recurrent mechanisms. Predicting both overall protein-level function terms and residue-level function sites via multi-task learning and novel deep learning architectures can leverage the compliment of the two prediction tasks to provide more accurate, more complete, and more interpretable function prediction. The project will deliver user-friendly open-source tools for the community to accurately predict function from sequence, structure, and interaction data, which will help reduce the vast knowledge gap between protein sequence and function. The open-source deep learning tools can be used to predict and study protein function in many domains. The methods and tools will be leveraged to train students at multiple levels and increase the diversity in scientific research and education. The results of the project can be found at https://calla.rnet.missouri.edu/cheng/nsf_protein_function.htmlThis 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.