Excellence in Research: Developing a Knowledge Graph Driven Integrative Framework for Explainable Protein Function Prediction via Generative Deep Learning

蛋白质是生命的基石，执行多种功能，包括但不限于催化酶反应，作为抗体参与身体的防御机制，形成结构和运输重要的化学物质。蛋白质之间的相互作用描述了疾病的分子机制，并为疾病的预防、诊断和治疗提供了潜在的重要见解。因此，蛋白质的功能表征对于帮助了解生命、疾病和开发威胁生命的疾病的新疗法至关重要。 尽管最近的进展，预测蛋白质功能仍然是一个悬而未决的问题，由于低性能，缺乏可解释的结果，和不可复制的研究传播强调需要改进的方法，利用最近激增的生物医学数据的蛋白质。本研究的目标是设计、实现和评估一个蛋白质功能预测管道，该管道使用一种由异构知识图提供支持的新型生成式深度学习方法，以解决多组学数据集成、可解释功能预测和再现性的挑战。该研究将通过三个相互关联的任务进行：1）在知识图谱上研究一种新的生成式深度学习模型; 2）通过大型语言模型整合多组学特征; 3）开发可复制的软件。该项目的成功完成将导致一个强大的，更准确的，可重复的和可解释的蛋白质功能预测管道。该项目将创造新的教育和推广机会，以大大加强在计算生物学的培训和研究活动，利用现代人工智能技术在Meharry医学院，领先的HBCU。 梅哈里主要招收非裔美国学生。超过90%的数据科学学生在Meharry是非洲裔美国人，大多数是女性。该项目将提高STEM教育的意识，影响力和机会，让Meharry的女性和少数民族学生在AI/ML，定量基因组学和数据科学研究方面脱颖而出。可复制的开源软件将极大地促进更广泛的科学界致力于改善蛋白质功能预测。该奖项反映了NSF的法定使命，并已被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Proteins are the building blocks of life performing multitudes of functions that include but not limited to catalyzing reactions as enzymes, participating in the body’s defense mechanism as antibodies, forming structures and transporting important chemicals. The interactions among proteins describe the molecular mechanism of diseases, and convey potentially important insights about the disease prevention, diagnosis, and treatments. Therefore, functional characterization of proteins is crucial to helping understand life, diseases, and developing novel treatments for life threatening illness. Despite recent advancements, predicting protein function remains an open problem due to low performance, lack of explainable outcomes, and irreproducible research dissemination highlighting the need for improved methodologies leveraging the recent proliferation of biomedical data about proteins. The objective of this research is to design, implement, and evaluate a protein function prediction pipeline using a novel generative deep learning approach powered by heterogeneous knowledge graph to address the challenge of multi-omics data integration, explainable function prediction, and reproducibility. The research will be carried out through three interrelated tasks: 1) investigation of a novel generative deep learning model on knowledge graph; 2) integration of multi-omics features through large language model; and, 3) development of reproducible software. Successful completion of this project will lead to a robust, more accurate, reproducible and explainable protein function prediction pipeline. The project will create new education and outreach opportunities to greatly strengthen the training and research activities in computational biology leveraging modern AI technologies at Meharry Medical College, a leading HBCU. Meharry dominantly enrolls African American students. More than 90% data science students at Meharry are African Americans and majority are women. This project will increase STEM education awareness, impact, and opportunity to the women and minority students at Meharry to excel in AI/ML, quantitative genomics and data science research. The reproducible open-source software will greatly facilitate broader scientific community working to improve protein function prediction.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.