CAREER - Flexibility vs. Preorganization: Atomistic Simulations of Partner Recognition by Natively Unfolded Peptides

职业 - 灵活性与预组织：本机展开的肽对伙伴识别的原子模拟

• 批准号: 0845216
• 负责人: Lillian Chong
• 金额: $ 69.85万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-01 至 2015-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845216&HistoricalAwards=false
• 关键词: CAREER; Flexibility; vs.; Preorganization; Atomistic

A substantial fraction of proteins are natively unfolded, or lack well-defined structure. Many of these proteins fold only upon binding to their partner proteins, suggesting a new paradigm for protein-protein recognition. This new paradigm challenges the conventional assumption that proteins bind their partners more quickly if they are preorganized, adopting structures in their unbound states that are identical to structures in their bound states. This CAREER project will reassess this assumption by comparing the rates of partner-binding by natively unfolded peptides with their exact, preorganized analogues using atomically detailed computer simulations. Such direct comparison is not achievable by laboratory experiments. Novel aspects of this research are (a) the use of the weighted ensemble approach to focus atomistic computer simulations on the sampling of rare binding events that involve conformational changes, or changes in the shapes of the binding partners, (b) its implementation within distributed computing resources to dramatically increase the methodology's computational reach, and (c) the intention to study realistic binding rates without forcing the events to occur. Results from this project will advance the understanding of fundamental principles governing molecular recognition and could therefore improve strategies for designing highly specific inhibitors of chosen protein-protein interactions, molecular sensors, and molecular switches.Broader Impacts. With the increasingly interdisciplinary nature of modern research, it is essential for students to learn how to give effective scientific presentations to broad audiences. Dr. Chong has designed and taught an undergraduate course that helps students develop effective scientific presentations through intensive practice and feedback throughout the semester. Building on this experience, she will develop additional approaches for helping students improve their speaking skills. These approaches include training students to communicate specific concepts in science within five minutes and packaging these presentations into podcasts and digital videos. To broaden the outreach of this training, Dr. Chong will encourage undergraduates from institutions all over the country to create such videos in a contest that she will organize at a national conference. Goals of this contest include: a) to help undergraduates develop effective communication skills and b) to promote interest in the sciences among undergraduates, including minorities. Finally, Dr. Chong will extend the training to graduate students by designing a graduate course in giving effective scientific presentations.

相当一部分蛋白质是天然未折叠的，或者缺乏明确的结构。这些蛋白质中的许多只在与伴侣蛋白结合时才折叠，这提示了蛋白质-蛋白质识别的新范式。这个新范式挑战了传统的假设，即如果蛋白质是预先组织的，它们会更快地结合它们的伙伴，在它们的非结合状态下采用与它们的结合状态相同的结构。这个CAREER项目将重新评估这一假设，通过使用原子详细的计算机模拟，比较天然未折叠肽与它们精确的、预先组织的类似物的伴侣结合率。这种直接的比较是无法通过实验室实验实现的。本研究的新颖方面是：(a)使用加权集成方法将原子计算机模拟集中在涉及构象变化或结合伙伴形状变化的罕见结合事件的采样上，(b)在分布式计算资源中实现该方法以显着增加方法的计算范围，以及(c)在不强迫事件发生的情况下研究现实结合率的意图。该项目的结果将促进对控制分子识别的基本原理的理解，从而可以改进设计特定蛋白质-蛋白质相互作用、分子传感器和分子开关的高度特异性抑制剂的策略。更广泛的影响。随着现代研究越来越多的跨学科性质，学生学习如何向广泛的受众进行有效的科学报告是至关重要的。钟博士设计并教授了一门本科课程，帮助学生通过整个学期的密集练习和反馈来培养有效的科学报告。在这些经验的基础上，她将开发更多的方法来帮助学生提高他们的口语技能。这些方法包括训练学生在五分钟内传达特定的科学概念，并将这些演示打包成播客和数字视频。为了扩大这种培训的范围，钟博士将鼓励全国各院校的本科生在她将在一次全国会议上组织的比赛中制作这样的视频。本次竞赛的目标包括：a)帮助本科生培养有效的沟通技巧；b)促进本科生（包括少数民族）对科学的兴趣。最后，钟博士将把培训扩展到研究生，设计一门有效的科学报告研究生课程。