OMB No. 0925-0001/0002 (Rev. 08/12 Approved Through 8/31/2015)

BIOGRAPHICAL SKETCH Provide the following information for the Senior/key personnel and other significant contributors.

Follow this format for each person. DO NOT EXCEED FIVE PAGES.

NAME: Diana Murray, Ph.D.

eRA COMMONS USER NAME (credential, e.g., agency login): dim2007

POSITION TITLE: Program Director of Research, Research Scientist

EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.)

INSTITUTION AND LOCATION

DEGREE (if

applicable)

Completion Date

MM/YYYY

FIELD OF STUDY

State University of New York, Stony Brook, NY B.S. 05/1989 Physics

State University of New York, Stony Brook, NY Ph.D. 08/1994 Physics

State University of New York, Stony Brook, NY 1996-1999 Biophysics

Columbia University, New York, NY 1999-2001 Biophysics

Stony Brook University, Stony Brook, NY M.A.T. 12/2014 Science Teaching

A. Personal Statement My scientific passion is understanding the formation and structure of macromolecular complexes that function

in cellular processes using approaches at the intersection of physics, computation, and biology. In 2010, I took a medical disability leave and eventually retired from my position as Associate Professor at Columbia University. I have now recovered to the extent that I can return to an active research career, and I am grateful for the opportunity the Department of Systems Biology has offered me. The following summarizes my research activities prior to leaving Columbia in 2010.

The work in my lab (2001-2010) provided fundamentally new insights into the physiological control of subcellular targeting. A fundamental question in cell biology is how the recruitment of proteins to different cellular membranes is achieved and regulated. My lab used computational modeling to characterize the structural and energetic bases for the binding of proteins to membranes and, in turn, to better understand the underlying forces that govern membrane-mediated processes in cells. Our close collaborations with experimental groups attest to our success in impacting cell biology and to the uniqueness of our research program.

In 2001, I started my first faculty position and for the following nine years enjoyed a fascinating and productive career as a scientist and mentor. Four students received their PhDs under my direction, and I mentored 22 postdoctoral fellows, graduate students, and high school students. Through my computational biology research in phosphoinositide signaling, retroviral assembly, and structural genomics, I established collaborations with over 15 labs, published 60 papers, accepted invitations to speak at over 20 national and international conferences, and was awarded grants from the NIH and NSF totaling $2.6 M (as Principal Investigator) and $0.6 M (as co-Principal Investigator). In addition, I created the Computational Genomics Core Facility at Weill Medical School, served as associate editor for PLoS Computational Biology, participated at NIH study sections, and organized symposia for FASEB and ACS meetings. I taught 134 hours in 25 courses at the graduate level, and, more recently, I taught science, physics, and chemistry full-time at the middle school and high school levels as a student teacher for four months.

I am thrilled to be back at Columbia University Medical Center as of September 2015. Through my accomplishments in science, teaching/mentoring, and administration, I have the experience and expertise to successfully integrate components of each to support the innovative research and outreach activities in the rich, multi-disciplinary environment of the Department of Systems Biology.

B. Positions and Honors 1994-1996 Research Analyst, Institute for Pattern Recognition, Robert Nathans Ltd., Setauket, NY. 1996-1999 Postdoctoral Fellow, Physiology and Biophysics, SUNY Stony Brook. 1996 NIH Institutional Training Grant postdoctoral fellowship, SUNY Stony Brook. 1997-2000 Helen Hay Whitney Foundation Postdoctoral Fellowship. 1999-2001 Postdoctoral Fellow, Biochemistry and Molecular Biophysics, Columbia University, NY. 2000-2001 Alfred P. Sloan Foundation/Department of Energy Fellowship in Computational Biology. 2001-2003 Director, Computational Genomics Core Facility, Weill Medical College of Cornell University 2001-2005 Assistant Professor, Department of Microbiology and Immunology and The Institute for Computational Biomedicine (ICB), Weill Medical College of Cornell University. 2003-2005 Research fellowship from the Alfred P. Sloan Foundation. 2005-2007 Associate Professor, Department of Microbiology and Immunology and ICB, Weill Medical College 2007-2010 Associate Professor, Department of Pharmacology and the Center for Computational Biology and Bioinformatics, Columbia University 2015-present Program Director for Research, Research Scientist, Department of Systems Biology, Columbia University

C. Contributions to Science 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]. The reversible binding of proteins to membrane surfaces is critical to many biological processes and is often accomplished through lipid-interacting protein domains [4]. Our computational studies, based on the application of the finite-difference Poisson-Boltzmann equation to atomic models of proteins and membranes, helped establish that the membrane association of many peripheral membrane proteins is mediated, at least in part, by nonspecific electrostatic interactions [4]. We collaborated with experimental labs to apply a powerful combined theory/experiment approach to elucidate the role of nonspecific electrostatic interactions in peripheral protein function [1,3-5,7-13]: 1) computational studies with molecular models, 2) in vitro biophysical and/or genomic characterization of protein/membrane interactions, and 3) in vivo studies of wild-type and mutant protein to follow subcellular localization or cellular transformation. We characterized the membrane binding properties and subcellular targeting of many proteins [4] (Src [1], MARCKS, caveoilin, k-Ras, Gbg, type IIb PIPkin and PTEN) and protein families [4] (C2 domains [2], PLA2s [3], retroviral matrix domains [5-8], PH domains [6,13]). In particular, we extended the calcium/electrostatic switch observed by others with C2 domains‒where calcium binding was shown to increase attractive interactions with acidic phospholipids‒to include C2 domains that have a large negative electrostatic potential surrounding a cluster of hydrophobic residues [2,4]: Calcium ions neutralize the coordinating aspartates, and, thus, decrease the desolvation penalty associated with partitioning this portion of the domain from the high dielectric aqueous phase to the lower dielectric membrane interface, allowing for the membrane insertion of the hydrophobic clusters. 1. Murray, D., Hermida-Matsumoto, L., Buser, C.A., Tsang, J., Sigal, C., Ben-Tal, N., Honig, B., Resh, M.D.,

and McLaughlin, S. (1998). Electrostatics and the membrane association of Src: Theory and experiment. Biochemistry 37:2145-2159. PMID: 9485361.

2. Murray, D., and Honig, B. (2002). Electrostatic control of the membrane targeting of C2 domains. Molecular Cell 9:145-154. PMID: 11804593.

3. Diraviyam, K., Murray, D. (2006). Computational analysis of the interfacial membrane association of Group IIA Secreted phospholipase A2: A differential role for electrostatics. Biochemistry. 45:2584-2598. PMID: 16489752.

4. Mulgrew-Nesbitt, A., Diraviyam, K., Wang, J., Singh, S., Murray, P., Li, Z., Rogers, L., Mirkovic, N., Murray, D. (2006). The role of electrostatics in protein-membrane interactions. Lipid Binding Domains. Biochim. Biophys. Acta. 1761:812-826. PMID: 16928468,

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 [4]. 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]. Phosphoinositides are signals in their own right, but in contrast to calcium, they are membrane-embedded rather than soluble signals. In analogy to the calcium-mediated electrostatic switch we characterized for C2 domains [2], we uncovered a phosphoinositide-mediated switch [4,5,8]: Hydrophobic groups on the protein domains that bind these lipids do not penetrate the membrane in the absence of the specific protein/phosphoinositide interaction. However, the binding of a domain to its phosphoinositide ligand, which is highly negatively charged, neutralizes basic residues in the binding pocket on the domain and dramatically decreases the positive electrostatic potential in this region. Consequently, the switch induces membrane penetration of hydrophobic residues that surround the phosphoinositide-binding pocket, and this membrane insertion is critical to the function of these proteins [4,6,8,13]. Some phosphoinositides, like PI(3,4,5)P3, are transiently produced, and others, like PI(4,5)P2, are more permanently resident and may be sequestered in localized regions of the cytosolic surface of the plasma membrane and are, thus, unavailable for signaling. 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, R.V., Cho, W., Murray, D. (2003). Computer modeling of the membrane interaction

of FYVE domains. J. Molecular Biology 328:721-736. PMID: 12706728. 6. Singh, S. and Murray, D. (2003). Molecular modeling of the membrane targeting of phospholipase pleckstrin

homology domains. Protein Science 12:1934-1953. PMID: 12930993. PMCID: PMC2323991. 7. Wang, J., Gambhir, A., McLaughlin, S., Murray, D. (2004). A computational model for the electrostatic

sequestration of PI(4,5)P2 by membrane-adsorbed basic peptides. Biophys. J. 86:1969-1986. PMID: 15041641. PMCID: PMC1304052.

8. Silkov, A., Yoon, Y., Lee, H., Gokhale, N., Robert V. Adu-Gyamfi, E., Stahelin, R.V., and Cho, W., and Murray, D. (2011). Genome-wide identification of lipid binding domains and prediction of their membrane binding properties: An ANTH/ENTH domain study. J. Biol. Chem. 286:34155-34163. PMID: 21828048. PMCID: PMC3190782.

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 [4], 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]. We predicted electrostatic control of membrane binding is a central characteristic of all retroviruses. Remarkably, in spite of non-detectable sequence similarity, all retroviral matrix domains contain a positive region of electrostatic potential on their surfaces which we predict is sufficient to drive plasma membrane targeting. More generally, we established how electrostatic interactions control different stages of the viral life cycle: membrane targeting, Gag protein oligomerization, budding and maturation, and host entry and infection [9]. Our calculations provided numerical estimates for the strengths of the interactions involved in each of these steps. The level of agreement between theory and experiment that is obtained [9-12], suggests our approach can be generalized to other enveloped viruses, such as Influenza and Ebola. 9. Murray, P.S., Li, Z., Wang, J., Tang, C., Honig, B., Murray, D. (2005). Retroviral matrix domains share

electrostatic homology: A model for membrane targeting function. Structure. 13:1521-31. PMID: 16216583. 10. Dalton, A., Murray, P., Murray, D., Vogt, V. (2005). Biochemical characterization of Rous sarcoma virus MA

protein interaction with membranes. J. Virology. 79:6227-6238. PMID: 15858007. PMCID: PMC1091718. 11. Dalton, A.K., Ako-Adjei, D., Murray, P.S., Murray, D., Vogt, V.M. (2007). Electrostatic interactions drive

membrane association of the human immunodeficiency virus type 1 Gag MA domain. J. Virology. 81:6434-6445. PMID: 17392361. PMCID: PMC1900125.

12. Phillips JM, Murray PS, Murray D, Vogt VM. (2008). A molecular switch required for retrovirus assembly participates in the hexagonal immature lattice. EMBO J. 27(9):1411-20. PMID: 18401344. PMCID: PMC2374847.

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 [14]. More notably, we developed a high-throughput computational pipeline to detect remote sequence homologs for the NESG structures and to construct high quality structural models for these homologs [15]. In this way, we leveraged the NESG structures to 1) structurally and functionally annotate whole protein families [8,13,16], 2) provide extended structural coverage of protein sequence space [15], and 3) develop a dynamic target selection strategy for experimental structural determination, thus, further increasing the structural coverage of protein sequence space [15]. We applied our computational pipeline and annotation approaches to characterize families of membrane-binding domains as well [8,13,16]. 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. Yu, J.W., Mendrola, J.M., Audhya, A., Singh, S., Keleti, D., DeWald, D.B., Murray, D., Emr, S.D., Lemmon,

M.A. (2004). Genome-wide analysis of membrane targeting by S. cerevisiae pleckstrin homology (PH) domains. Molecular Cell. 13:677-688. PMID: 15023338.

14. Liu, G, Li, Z., Chiang, Y., Acton, T., Montelione, G.T., Murray, D., Szyperski, T. (2005). 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 Science. 14:1597-1608.

15. Mirkovic, N., Li, Z., Parnassa, A., Murray, D. (2007). Strategies for high-throughput comparative modeling: Applications to leverage analysis in structural genomics and protein family organization. Proteins. 66:766-777. PMID: 15930006. PMCID: PMC2253389.

16. Lee H, Li Z, Silkov A, Fischer M, Petrey D, Honig B, Murray D. (2010). High-throughput computational structure-based characterization of protein families: START domains. J Struct Funct Genomics 11(1):51-9. PMID: 20383749. PMCID: PMC2881152.