Codes For Life - Artificial Intelligence and Sustainable Software for Biomolecular Interactions

Codes For Life - 生物分子相互作用的人工智能和可持续软件

• 批准号: 2152059
• 负责人: Grace Brannigan
• 金额: $ 200万
• 依托单位: Rutgers University Camden
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152059&HistoricalAwards=false
• 关键词: Codes; Life; Artificial; Intelligence; Sustainable

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The modern study of biology is increasingly digital. Ever-expanding databases of genome sequences, protein structures, electronic health records, and biometric data may hold the key for solving crucial problems in many domains, such as biomedicine, agriculture, ecology, and forensics. Gathering useful information from this data requires advanced computer science skills as well as a deep understanding of biology. This National Science Foundation Research Traineeship (NRT) award to Rutgers University-Camden will meet this national need by training Master’s and doctoral degree students in the fundamental biology and biophysics of DNA and proteins, artificial intelligence (AI) methods, and professional best practices for team software development. The traineeship follows a new model for students and faculty mentors that combines innovative training support with increased expectations. While the program is designed to make science graduate training of any discipline more efficient, effective, and accessible, the NRT is specifically designed to overcome the challenges in training students in new fields between traditional domains of expertise. The project anticipates training 30 M.S. and 25 Ph.D. students, with an additional 300 M.S. and Ph.D. students expected to participate in a subset of the activities. Trainees will graduate with a combination of skills that are highly in-demand across academic, government, and industrial workplaces.The research and training activities of this NRT award are particularly focused on using AI and sustainably developed software to overcome the widening communication gap between genomicists and proteomicists. This communication gap, worsened by diverging scientific dialects, has prevented the synthesis of research advances and the development of new unifying biological principles. AI is a generalizable approach that is being increasingly used by both fields. Software is a powerful method for communication and bridging disciplinary divides. However, it must be accessible to both disciplines and developed to a standard that academic software rarely meets. The team proposes a new model for graduate training that combines evidenced-based methods, redesigned for improved sustainability, and original activities that satisfy an identified need. Training activities will include revised curricula, the introduction of efficient “just-in-time” short format high-quality training, an industry mentorship program integrated with the trainee’s research, and peer mentoring for expanding perspectives and sharing strategies. The traineeship will introduce the new “Codes for Life” track into an interdisciplinary graduate degree program at Rutgers University-Camden. Many activities will be extended to all students enrolled in this broad graduate program. As a result, this will provide a unique opportunity to evaluate activities with a large graduate student body without historical departmental constraints. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。现代生物学研究日益数字化。不断扩大的基因组序列、蛋白质结构、电子健康记录和生物识别数据数据库可能是解决生物医学、农业、生态和法医学等许多领域关键问题的关键。从这些数据中收集有用的信息需要高级计算机科学技能以及对生物学的深入理解。这项授予罗格斯大学卡姆登分校的国家科学基金会研究培训(NRT)奖将通过培训硕士和博士学位学生DNA和蛋白质的基础生物学和生物物理学、人工智能(AI)方法以及团队软件开发的专业最佳实践来满足这一国家需求。培训遵循了一种新的学生和教师导师模式，将创新的培训支持与更高的期望结合在一起。虽然该计划旨在使任何学科的科学研究生培训更有效率、效果和可及性，但NRT是专门为克服在传统专业领域之间的新领域培训学生的挑战而设计的。该项目预计将培训30名硕士和25名博士生，另外300名硕士和博士生预计将参加部分活动。学员毕业后将拥有学术、政府和工业工作场所高度需求的技能组合。这一NRT奖项的研究和培训活动特别侧重于使用人工智能和可持续开发的软件来克服基因组学家和蛋白质学家之间日益扩大的沟通差距。这种交流鸿沟因科学方言的不同而恶化，阻碍了研究进展的综合和新的统一生物学原理的发展。人工智能是一种可推广的方法，正越来越多地被这两个领域使用。软件是沟通和弥合学科分歧的有力手段。然而，它必须对这两个学科都是可访问的，并开发到学术软件很少达到的标准。该团队提出了一种新的研究生培训模式，将以证据为基础的方法与满足特定需求的原始活动相结合，重新设计以提高可持续性。培训活动将包括修订课程，引入高效的“及时”短期高质量培训，与受训人员的研究相结合的行业指导计划，以及扩大视角和分享战略的同行指导。该培训项目将在罗格斯大学卡姆登分校的跨学科研究生学位项目中引入新的“生命密码”课程。许多活动将扩展到所有参加这一广泛的研究生课程的学生。因此，这将提供一个独特的机会来评估有大量研究生参加的活动，而不受历史部门的限制。NSF研究培训(NRT)计划旨在鼓励开发和实施大胆的、具有潜在变革意义的STEM研究生教育培训模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。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。

项目成果

Open-channel structure of a pentameric ligand-gated ion channel reveals a mechanism of leaflet-specific phospholipid modulation.

• DOI: 10.1038/s41467-022-34813-5
• 发表时间: 2022-11-17
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Petroff, John T.;Dietzen, Noah M.;Santiago-McRae, Ezry;Deng, Brett;Washington, Maya S.;Chen, Lawrence J.;Moreland, K. Trent;Deng, Zengqin;Rau, Michael;Fitzpatrick, James A. J.;Yuan, Peng;Joseph, Thomas T.;Henin, Jerome;Brannigan, Grace;Cheng, Wayland W. L.
• 通讯作者: Cheng, Wayland W. L.