RaMP: Post-Baccalaureate Training Program in Biomolecular Structure Prediction and Design

RaMP：生物分子结构预测和设计学士后培训项目

• 批准号: 2216011
• 负责人: Jeffrey Gray
• 金额: $ 299.17万
• 依托单位: Johns Hopkins University
• 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: 2022-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216011&HistoricalAwards=false
• 关键词: RaMP; Post; Baccalaureate; Training; Program

Recent advances in computational methods that predict the structures of biological molecules enable the design of equally intricately structured polymers for their use in research, biotechnology, materials, and nanotechnology. Emerging artificial intelligence approaches amplify the power of the computational work and have increased demand for people with training in biomolecular structure computations. This project will provide to the trainees rigorous, interdisciplinary, collaborative research training in biomolecular structure prediction and design. Activities such as a code school, laboratory research and regular mentoring will afford training in the technical and communication skills necessary for success in STEM. Outreach to communities historically excluded from STEM will ensure that this growing field is accessible to a broad demographic. The program will build a diverse STEM workforce, which is critically needed as artificial intelligence methods continue to dramatically impact the fields of molecular biology and bioengineering. Biomolecular computational structure research allows prediction of three-dimensional structures of proteins, nucleic acids, and their complexes. The program will provide rigorous, interdisciplinary, collaborative research training in biomolecular structure prediction and design to eight post-baccalaureate trainees (“participants”) in each of three years. It will (1) develop post-baccalaureate participants' technical research skills and professional power skills, including communication and perseverance, with experiences that enhance participants’ scientific identity, self-efficacy, and sense of belonging; (2) increase post-baccalaureate scholars’ ease of transition into graduate level academic programs, in that they will be more prepared and supported when applying for and entering those programs, (3) create a tight knit network of mentors and mentees within the greater Rosetta Commons community through structured leadership training in improved mentoring; and (4) expand the pool of biomolecular graduate program applicants to include more individuals from backgrounds and communities historically excluded from STEM. 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.

预测生物分子结构的计算方法的最新进展，使设计同样复杂结构的聚合物成为可能，用于研究、生物技术、材料和纳米技术。新兴的人工智能方法扩大了计算工作的力量，并增加了对受过生物分子结构计算训练的人员的需求。本项目将为学员在生物分子结构预测与设计方面提供严格的跨学科合作研究训练。代码学校、实验室研究和定期指导等活动将提供在STEM领域取得成功所需的技术和沟通技能培训。向历史上被排除在STEM之外的社区伸出援手，将确保广泛的人口能够接触到这一不断发展的领域。该计划将建立一支多样化的STEM劳动力队伍，随着人工智能方法继续对分子生物学和生物工程领域产生巨大影响，这是急需的。生物分子计算结构研究可以预测蛋白质、核酸及其复合物的三维结构。该计划将提供严格的，跨学科的合作研究培训，生物分子结构预测和设计，每三年为八名学士学位后的学员（“参与者”）。它将(1)培养学士后参与者的技术研究技能和专业权力技能，包括沟通和毅力，通过经验增强参与者的科学认同，自我效能感和归属感；(2)增加学士学位后学者过渡到研究生水平学术项目的难度，因为他们在申请和进入这些项目时将得到更多的准备和支持；(3)在更大的Rosetta Commons社区内，通过改进指导的结构化领导力培训，建立一个紧密的导师和学员网络；(4)扩大生物分子研究生项目申请者的范围，包括更多来自历史上被排除在STEM之外的背景和社区的个人。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。