The goal of my current research is to integrate protein structure, lipidomics, and proteomics- and genomics-based networks to study cellular signaling pathways dysregulated in cancer. The work builds in part, on research in my lab (2001-2010) which provided fundamentally new insights into the physiological control of subcellular targeting. We computationally modeled large atomic-level protein/membrane systems and characterized the structural and energetic bases for the binding of proteins to membranes and for membrane-mediated processes in cells. Validation of our models through close collaborations with experimental groups attest to the success of our research and it's impact on cell biology. This approach is now being expanded to Cancer Systems Biology.
Relevant areas of previous research and representative publications:
I. An experimentally-verified computational model for dissecting how peripheral proteins exhibit a range of membrane-binding behaviors.
Our computational work clarified and established the importance of non-specific electrostatics as a driving force for the membrane association and function of peripheral proteins [1,2,3,4].
1. Murray D, Hermida-Matsumoto L, Buser CA, Tsang J, Sigal CT, Ben-Tal N, Honig B, Resh MD, McLaughlin S. Electrostatics and the membrane association of Src: Theory and experiment. Biochemistry. 1998 Feb 24;37(8):2145-59.
2. Murray D, Honig B. Electrostatic control of the membrane targeting of C2 domains. Mol Cell. 2002 Jan;9(1):145-54.
3. Diraviyam K, Murray D. Computational analysis of the interfacial membrane association of Group IIA Secreted phospholipase A2: A differential role for electrostatics. Biochemistry. 2006 Feb 28;45(8):2584-98.
4. Mulgrew-Nesbitt A, Diraviyam K, Wang J, Singh S, Murray P, Li Z, Rogers L, Mirkovic N, Murray D. The role of electrostatics in protein-membrane interactions. Biochim Biophys Acta. 2006 Aug;1761(8):812-26.
II. A computational framework for examining phosphoinositide signaling at the molecular level.
In contrast to peripheral proteins that interact non-specifically with membrane surfaces (described above), phosphoinositide-binding domains typically bind phosphoinositide head groups in a highly stereospecific fashion . Our computational/experimental approach established that electrostatics plays a central role in phosphoinositide-mediated signaling and is manifested through phosphoinositide-mediated membrane association [4-6,8] and the lateral organization of proteins and lipids [4,6,7]. Our calculations with molecular-level models indicate that much of the PI(4,5)P2 in the plasma membrane of a cell is kinetically trapped by membrane-adsorbed basic clusters on peripheral proteins [4,7,9]. The sequestered PI(4,5)P2 is released in response to cellular signaling events, such as an increase in cellular calcium, that lead to the membrane desorption of the basic clusters.
5. Diraviyam K, Stahelin RV, Cho W, Murray D. Computer modeling of the membrane interaction of FYVE domains. J Mol Biol. 2003 May 2;328(3):721-36.
6. Singh SM, Murray D. Molecular modeling of the membrane targeting of phospholipase pleckstrin homology domains. Protein Sci. 2003 Sep;12(9):1934-53.
7. Wang J, Gambhir A, McLaughlin S, Murray D. A computational model for the electrostatic sequestration of PI(4,5)P2 by membrane-adsorbed basic peptides. Biophys J. 2004 Apr;86(4):1969-86.
8. Silkov A, Yoon Y, Lee H, Gokhale N, Adu-Gyamfi E, Stahelin RV, Cho W, Murray D. Genome-wide identification of lipid binding domains and prediction of their membrane binding properties: An ANTH/ENTH domain study. J Biol Chem. 2011 Sep 30;286(39):34155-63.
III. Retroviral assembly: The central role of electrostatics in membrane targeting and lateral organization at the cytoplasmic surface of the plasma membrane.
A fundamental question in retrovirology is how the recruitment of Gag polyproteins to the cytoplasmic surface of the plasma membrane of an infected cell is achieved. Building on our work with peripheral proteins and phosphoinositide signaling , we probed the physical basis of the interaction of retroviral matrix domains with membrane surfaces and provided a detailed theoretical picture for the role of these domains in viral assembly and disassembly [9-12].
9. Murray PS, Li Z, Wang J, Tang CL, Honig B, Murray D. Retroviral matrix domains share electrostatic homology: models for membrane binding function throughout the viral life cycle. Structure. 2005 Oct;13(10):1521-31.
10. Dalton AK, Murray PS, Murray D, Vogt VM. Biochemical characterization of Rous sarcoma virus MA protein interaction with membranes. J Virol. 2005 May;79(10):6227-38.
11. Dalton AK, Ako-Adjei D, Murray PS, Murray D, Vogt VM. Electrostatic interactions drive membrane association of the human immunodeficiency virus type 1 Gag MA domain. J Virol. 2007 Jun;81(12):6434-45.
12. Phillips JM, Murray PS, Murray D, Vogt VM. A molecular switch required for retrovirus assembly participates in the hexagonal immature lattice. EMBO J. 2008 May 7;27(9):1411-20.
IV. Structural genomics and the computational modeling and function annotation of protein families.
We were members of the Northeast Structural Genomics Consortium, which used high-throughput methodologies to experimentally determine the structures of protein domains from eukaryotic genomes. My lab applied detailed computational approaches to functionally annotate the protein structures solved . The ability to structurally model whole protein families and to discriminate the biophysical characteristics of individual members is a powerful, novel approach to the discovery of new members as well as the function annotation of proteomes [13-14].
13. Yu JW, Mendrola JM, Audhya A, Singh S, Keleti D, DeWald DB, Murray D, Emr SD, Lemmon MA. Genome-wide analysis of membrane targeting by S. cerevisiae pleckstrin homology (PH) domains. Mol Cell. 2004 Mar 12;13(5):677-88.
14. Liu G, Li Z, Chiang Y, Acton T, Montelione GT, Murray D, Szyperski T. High quality homology models derived from NMR and X-ray structure of E.coli proteins YdgK and SufE suggest that all members of the YdgK/SufE protein family are enhancers of cysteine desulferases. Protein Sci. 2005 Jun;14(6):1597-608.
15. Mirkovic N, Li Z, Parnassa A, Murray D. Strategies for high-throughput comparative modeling: Applications to leverage analysis in structural genomics and protein family organization. Proteins. 2007 Mar 1;66(4):766-77.
16. Lee H, Li Z, Silkov A, Fischer M, Petrey D, Honig B, Murray D. High-throughput computational structure-based characterization of protein families: START domains and implications for structural genomics. J Struct Funct Genomics. 2010 Mar;11(1):51-9.