III: Small: Integrated prediction of intrinsic disorder and disorder functions with modular multi-label deep learning

蛋白质是了不起的生物机器。在过去的二十年里，数以亿计的蛋白质序列被破译，造成了一个巨大的知识鸿沟，这与我们不知道其中大多数是做什么的事实有关。破译蛋白质功能的一种常见方法依赖于从序列到结构到功能的范例，其中蛋白质功能是从序列产生的蛋白质结构中学习的。然而，最近的研究发现了一大类内在无序的蛋白质，它们在生理条件下缺乏稳定的结构，因此不能用基于结构的方法来表征。这些蛋白质在真核生物中特别丰富，并与许多人类疾病的发病机制有关。内在无序蛋白质的发现推动了新一代计算方法的发展，这些方法可以直接从蛋白质序列中预测内在无序的存在。最近完成的蛋白质内在紊乱预测关键评估(CAID)实验表明，这些方法快速且提供准确的结果。然而，尽管可以在蛋白质序列中很容易和准确地识别内在障碍，但它的功能仍然是一个谜。这项提议将概念化、设计、实施、测试和部署一种创新的机器学习方法，该方法直接从蛋白质序列提供对无序和无序功能的高度准确和集成的预测。该团队将利用这种方法，以前所未有的规模产生数千万种蛋白质的无序功能注释，解决这个蛋白质家族的知识鸿沟问题。从长远来看，该项目将在内在无序蛋白质的背景下促进对基本生物过程和相关人类健康问题的理解。该项目还将通过高中推广以及本科生和研究生以及博士后研究人员的多学科教学和指导来培训STEM学生和研究人员，培养出受到工业界和学术界追捧的高技能研究人员。该团队解决了生物信息学和机器学习领域交叉的内在无序蛋白质结构的结构这一跨学科和具有挑战性的问题。该项目以计算分析内在无序的专业知识为基础，专注于技术创新，将提供一种新型的深度顺序多标签变压器架构，提供对无序和无序功能的准确预测。该解决方案将被设计为适应蛋白质数据的生物学基础，例如蛋白质数据固有的多标签结果、不平衡标签和顺序性质。此外，这一架构将采用模块化设计，以便于转移到蛋白质和核酸生物信息学的其他领域。由此产生的方法将得到广泛的基准和传播，以最大限度地发挥影响。代码将被存入相关的公共储存库，预计算的内在障碍的功能注释将使用现代在线资源，如数据储存库和网络服务器，以满足包括生物学家、生物化学家、生物物理学家和生物信息学家在内的广泛用户的需求。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影响审查标准进行评估，被认为值得支持。

Proteins are remarkable biological machines. Hundreds of millions of protein sequences were decoded over the last two decades creating a significant knowledge gap related to the fact that we do not know what most of them do. A common way to decipher protein functions relies on the sequence-to-structure-to-function paradigm where protein function is learned from the protein structure that is produced from the sequence. However, recent research has identified a large family of the intrinsically disordered proteins that lack a stable structure under physiological conditions and which therefore cannot be characterized using the structure-based approaches. These proteins are particularly abundant in the eukaryotes and are involved in the pathogenesis of numerous human diseases. The discovery of the intrinsically disordered proteins has prompted the development of a new generation of computational methods that predict presence of intrinsic disorder directly from protein sequences. A recently completed Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment has shown that these methods are fast and provide accurate results. However, while intrinsic disorder can be readily and accurately identified in protein sequences, its function remains a mystery. This proposal will conceptualize, design, implement, test and deploy an innovative machine learning method that provides highly accurate and integrated predictions of disorder and disorder functions directly from protein sequences. The team will utilize this method to produce functional annotations of disorder on an unprecedented scale of dozens of millions of proteins, addressing the knowledge gap problem for this protein family. In the long run this project will advance understanding of fundamental biological processes and related human health issues in the context of the intrinsically disordered proteins. This project will also train STEM students and researchers via high-school outreach and multidisciplinary teaching and mentoring of undergraduate and graduate students and postdoctoral researchers, producing highly skilled researchers who are sought after by industry and academia.An interdisciplinary and challenging problem of the structure of intrinsically disorder protein structure at the intersection of bioinformatics and machine learning fields is addressed by the team. Building on expertise in the computational analysis of intrinsic disorder and with focus on technical innovation, this project will deliver a novel deep sequential multi-label transformer architecture that provides accurate predictions of disorder and disorder functions. The solution will be designed to accommodate for the biological underpinnings of protein data, such as the inherently multi-label outcomes, imbalanced labels and sequential nature of protein data. Moreover, this architecture will feature modular design to facilitate transfer to other areas of protein and nucleic acids bioinformatics. The resulting method will be extensively benchmarked and disseminated to maximize impact. The code will be deposited into relevant public repositories and pre-computed functional annotations of intrinsic disorder will be made available using modern online resources, such as data repositories and webservers, in order to meet the needs of a broad spectrum of users including biologists, biochemist, biophysicists and bioinformaticians.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.