EAGER: Exploring the biochemical principle of allostery for algorithm development

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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.