EAGER: Exploring the biochemical principle of allostery for algorithm development

EAGER：探索算法开发的变构生化原理

• 批准号: 1144213
• 负责人: Christopher Langmead
• 金额: $ 1.73万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144213&HistoricalAwards=false
• 关键词: EAGER; Exploring; biochemical; principle; allostery

EAGER: Exploring the biochemical principle of allostery for algorithm developmentPI: Judith Klein-Seetharaman, Department of Structural Biology, University of PittsburghCo-PI: Christopher J. Langmead, Department of Computer Science, Carnegie Mellon UniversityProject AbstractMotivation: Computer science and biology have inspired each other by drawing analogies leading to new classes of algorithms such as neural networks and genetic algorithms and new fields such as computational biology and biocomputing (computing using biomolecules). The ever increasing data streams in everyday life as well as biology are most often characterized by networks, such as the internet, telephone network, disease transmission networks, social networks to name just a few. Networks consist of nodes connected by edges and only the nodes vary with the application areas. The network structures are conserved: the edges allow communication and information flow. Due to the size, complexity and dynamic nature of such networks, their control is challenging. As environments change, the structures of these networks change and are subject to numerous perturbations and failures. Nature faces these same types of challenges and has evolved robust strategies for ensuring that information is transmitted and that the system appropriately responds to changes in the environment: for example, the oxygen transport protein in the blood, hemoglobin, changes its affinity to oxygen in response to small molecule ligands favoring oxygen release where needed. The strategy that Nature employs in hemoglobin is called allostery.Opportunity: Allostery is a biochemical term that refers to the ability of biomolecules, in particular proteins, to achieve action at a distance in the atomic network through a small, localized perturbation. Proteins can be viewed as networks of atoms interacting in three-dimensional space. Allostery is a classical text-book example of an experimentally well studied and firmly established mechanism of control of this atomic network. Here, PIs propose the hypothesis that one can share the biochemical principle of allostery with other domains such as disease transmission, social networks, economics, surveillance applications and cloud computing. Understanding how Nature performs acquisition, transmission and processing of information at the molecular level may lead to future enabling technologies in other domains. Intellectual Merit: While the proposed hypothesis is potentially transformative, in-depth pursuit requires obtaining a proof-of-concept outlining (1) what kinds of mechanisms might exist in proteins that could be transferred to other domains and (2) develop an understanding of what are the requirements for such a transfer. Although allostery in proteins is an established biochemical principle, little is known how it works and how to predict it. Some proteins are regulated through allostery and others are not. Unfortunately, there is no simple property that determines whether a given protein is (or can be) allosterically regulated. While experimental methods provide direct evidence for allostery, they generally do not reveal the detailed physical and biological mechanisms for it. PIs propose to reveal these mechanisms through the combination of computation and experiments: a predicted path of communication between two distant sites can be validated or refuted by disrupting this path. The deliverables of this work will be a list of fundamental principles of allostery in proteins, and an analysis of their suitability for future extension to non-protein related networks. Broader Impact: This grant will support investigators and train graduate students from the areas of computer science, chemistry, biology & biomedicine. Convergence of technologies, here between protein allostery modeling and network control, is expected to speed up scientific progress in potentially many disciplines. Thus, one may in the future be able to push the field of biocomputing forward, predict disease outbreaks or identify action at a distance in economic networks.

项目负责人：Judith Klein-Seetharaman，美国匹兹堡大学结构生物系，合作负责人：Christopher J. Langmead，美国卡内基梅隆大学计算机科学系计算机科学和生物学通过类比相互启发，导致了新的算法类别，如神经网络和遗传算法，以及新的领域，如计算生物学和生物计算（利用生物分子进行计算）。日常生活和生物学中不断增长的数据流最常以网络为特征，例如互联网、电话网络、疾病传播网络、社会网络等等。网络由由边连接的节点组成，只有节点随着应用领域的不同而变化。网络结构是保守的：边缘允许通信和信息流。由于此类网络的规模、复杂性和动态性，它们的控制具有挑战性。随着环境的变化，这些网络的结构也会发生变化，并受到许多扰动和故障的影响。大自然面临着同样类型的挑战，并进化出了强大的策略来确保信息的传递，以及系统对环境变化的适当反应：例如，血液中的氧气运输蛋白血红蛋白，在需要时改变其对氧气的亲和力，以响应小分子配体，从而有利于氧气释放。大自然在血红蛋白中采用的策略被称为变构。机会：变构是一个生化术语，指的是生物分子，特别是蛋白质，通过小的局部扰动在原子网络中实现远距离作用的能力。蛋白质可以看作是在三维空间中相互作用的原子网络。变构是一个经典的教科书上的例子，实验研究得很好，并牢固地建立了这种原子网络的控制机制。在这里，pi提出了一个假设，即人们可以将变质的生化原理与其他领域共享，如疾病传播、社会网络、经济学、监测应用和云计算。了解大自然如何在分子水平上进行信息的获取、传输和处理，可能会导致未来在其他领域的支持技术。智力优势：虽然提出的假设具有潜在的变革性，但深入研究需要获得一个概念证明，概述(1)蛋白质中可能存在的可转移到其他结构域的机制；(2)了解这种转移的要求是什么。虽然蛋白质的变构是一个既定的生化原理，但人们对它是如何工作的以及如何预测它却知之甚少。有些蛋白质是通过变构调节的，有些则不是。不幸的是，没有一种简单的性质可以决定一个给定的蛋白质是否被（或可以）变构调节。虽然实验方法为变构提供了直接证据，但它们通常不能揭示其详细的物理和生物机制。pi建议通过计算和实验的结合来揭示这些机制：两个遥远地点之间的预测通信路径可以通过破坏该路径来验证或反驳。这项工作的成果将是列出蛋白质变构的基本原理，并分析它们未来扩展到非蛋白质相关网络的适用性。更广泛的影响：这项资助将支持研究人员和培养来自计算机科学、化学、生物和生物医学领域的研究生。蛋白质变构建模和网络控制之间的技术融合有望加速许多学科的科学进步。因此，人们将来可能能够推动生物计算领域向前发展，预测疾病爆发或确定经济网络中的远距离行动。