The main aim of this proposal is to develop a comprehensive quantitative evolutionary theory of structure-function relationship in proteins. To this end, a novel approach to study proteins is proposed based on graph theory, whereby the whole universe of all protein domains is organized into a graph (PDUG), based on structural functional or metaboilic participation similarities. This provides a multidimensional description of proteins at the level of all existing domains or whole proteoms in specific organisms. Using graph theory to analyze structural, functional and metabolic protein domain universes makes it possible to get unique insights into the evolutionary origin of proteins and the cause of their diversity. Comparing protein domain universe graphs from different organisms helps to create a new paradigm in phylogeny, whereby the tree of life is built based, not on specific genes or RNA molecules, but on whole proteoms taken in multidimensional space of structural, functional and metabolic relationships. Furthermore, the analysis of robust properties protein domain universe graphs makes it possible to develop testable dynamic models of protein evolution that encompass a range of evolutionary time scales from single mutations to the evolution of organisms. The research plan encompasses several crucial steps to achieve these specific aims. First, a new quantitative graph theoretical description of functional and metabolic relationships between proteins will be developed. It will be based on hierarchical description of functional and metabolic annotation of proteins, and will use markov models to quantify the distances in functional and metabolic spaces, as well as to quantify functional distances between enzymes via graph based similarity comparisons between their metabolites. Using these new quantitative descriptions, multidimentional protein domain universe graphs will be constructed and each will be clustered into disjoint clusters of structurally, functionally and metabolically similar proteins. Overlap between these clusters provides the extent of structure-function relationship and will also relate functions of proteins with their participation in particular metabolic pathways. By creating multidimensional protein domain universe graphs for various organisms, we first will evaluate the degree of participation of various structural and functional templates in different organisms, and by comparing those, we will create a comprehensive tree of life that will shed light on major evolutionary events. These findings will be applied to enhance our ability to predict structure and function of novel proteins leading to possible therapeutical applications. Our findings will be available to the scientific community via the ELISA database.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM068670-02
Application #
6874497
Study Section
Molecular and Cellular Biophysics Study Section (BBCA)
Program Officer
Edmonds, Charles G
Project Start
2004-04-01
Project End
2008-03-31
Budget Start
2005-04-01
Budget End
2006-03-31
Support Year
2
Fiscal Year
2005
Total Cost
$263,389
Indirect Cost
Name
Harvard University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
082359691
City
Cambridge
State
MA
Country
United States
Zip Code
02138
Manhart, Michael; Adkar, Bharat V; Shakhnovich, Eugene I (2018) Trade-offs between microbial growth phases lead to frequency-dependent and non-transitive selection. Proc Biol Sci 285:
Rotem, Assaf; Serohijos, Adrian W R; Chang, Connie B et al. (2018) Evolution on the Biophysical Fitness Landscape of an RNA Virus. Mol Biol Evol 35:2390-2400
Manhart, Michael; Shakhnovich, Eugene I (2018) Growth tradeoffs produce complex microbial communities on a single limiting resource. Nat Commun 9:3214
Jacobs, William M; Shakhnovich, Eugene I (2018) Accurate Protein-Folding Transition-Path Statistics from a Simple Free-Energy Landscape. J Phys Chem B :
Razban, Rostam M; Gilson, Amy I; Durfee, Niamh et al. (2018) ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms' proteomes. Bioinformatics 34:3557-3565
Choi, Jeong-Mo; Gilson, Amy I; Shakhnovich, Eugene I (2017) Graph's Topology and Free Energy of a Spin Model on the Graph. Phys Rev Lett 118:088302
Adkar, Bharat V; Manhart, Michael; Bhattacharyya, Sanchari et al. (2017) Optimization of lag phase shapes the evolution of a bacterial enzyme. Nat Ecol Evol 1:149
Bershtein, Shimon; Serohijos, Adrian Wr; Shakhnovich, Eugene I (2017) Bridging the physical scales in evolutionary biology: from protein sequence space to fitness of organisms and populations. Curr Opin Struct Biol 42:31-40
Gilson, Amy I; Marshall-Christensen, Ahmee; Choi, Jeong-Mo et al. (2017) The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution. Biophys J 112:1350-1365
Jacquin, Hugo; Gilson, Amy; Shakhnovich, Eugene et al. (2016) Benchmarking Inverse Statistical Approaches for Protein Structure and Design with Exactly Solvable Models. PLoS Comput Biol 12:e1004889

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