Research Focus
Lipid Membrane Biophysics
In recent years we have made strides in the modeling of liquid phase coexistence in lipid bilayers, first characterizing the finite size effects that prevent stabilization of phase coexistence [1]. This work paved the way for modeling lipid rafts in membrane environments, for which we have developed qualitative theories to explain why proteins partition to or act as linactants between lipid domains [2]. We have identified novel dimer conformations of cholesterol [3] and a putative new gel lipid phase induced by high local concentrations of cholesterol that may be explained by Superlattice theories of the past [4], and which may play critical roles in protein kinetics.
[1] G. A. Pantelopulos, T. Nagai, A. Bandara, A. Panahi, and J. E. Straub, “Critical size dependence of domain formation observed in coarse-grained simulations of bilayers composed of ternary lipid mixtures,” J. Chem. Phys. 147, 095101 (2017). [PDF]
[2] A. Bandara, A. Panahi, G. A. Pantelopulos, T. Nagai and J. E. Straub, “Exploring the impact of proteins on the line tension of a phase-separating ternary lipid mixture,” J. Chem. Phys. 150, 204702 (2019). [PDF]
[3] A. Bandara, A. Panahi, G.A. Pantelopulos, and J.E. Straub, “Exploring the structure and stability of cholesterol dimer formation in multicomponent lipid bilayers,” J. Comp. Chem. 38, 1479-1488 (2017); ibid. 40, 2348-2348 (2019). [PDF]
[4] G. A. Pantelopulos and J. E. Straub “Regimes of Complex Lipid Bilayer Phases Induced by Cholesterol Concentration in MD Simulation,” Biophys. J. 115, 2167–2178 (2018). [PDF]
Protein Aggregation and Amyloid Formation
Fifteen years ago, we carried out the first studies of the structure and function of amyloid protein using atomistic molecular dynamics [1]. At a time when the field of amyloid research was focused on the importance of amyloid fibrils, we performed studies of monomeric protein and proposed that changes in the structural ensemble of the monomer could provide essential insight into the nature of protein aggregation [2]. That point of view now receives increased attention in both computational and experimental studies of protein aggregation [3]. More recently, we have focused on the genesis of the amyloidβ-protein of Alzheimer’s Disease [4], carrying out simulations of key molecular structures that are now being confirmed experimentally [5].
[1] F. Massi, J. W. Peng, J. P. Lee and J. E. Straub, “Simulation study of the structure and dynamics of the Alzheimer’s amyloid peptide congener in solution,” Biophys. J. 80, 31-44 (2001). [PDF]
[2] B. Tarus, J. E. Straub and D. Thirumalai, “Dynamics of Asp23-Lys28 salt-bridge formation in Aβ(10-35) monomers,” J. Am. Chem. Soc. 128, 16159-16168 (2006). [PDF]
[3] J.E. Straub and D. Thirumalai, “Toward a molecular theory of early and late events in monomer to amyloid fibril formation,” Ann. Rev. Phys. Chem. 62, 437-463 (2011). [PDF]
[4] N. Miyashita, J.E. Straub, D. Thirumalai and Y. Sugita, “Transmembrane structures of amyloid precursor protein dimer predicted by Replica-Exchange Molecular Dynamics simulations [Communication],” J. Am. Chem. Soc. 131, 3438-3439 (2009). [PDF]
[5] L. Dominguez, S.C. Meredith, J.E. Straub, and D. Thirumalai, “Transmembrane fragment structures of Amyloid Precursor Protein depend on membrane surface curvature [Communication],” J. Am. Chem. Soc. 136, 854-857 (2014). [PDF]
Protein Dynamics and Energy Transfer
Our work in the area of vibrational energy transfer represented the first extensions of theories developed to treat energy transfer and dynamics in the solid and liquid state to a biomolecular context [1]. Our work on energy transfer in heme proteins not only advanced theoretical models for the prediction of time scale and mechanism in vibrational energy transfer, but also led to some of the few quantitative predictions of how the mechanism of ligand dissociation [2] and vibrational energy transfer [3] can depend on protein structure and sequence that were later experimentally verified [4]. Aspects of that work are summarized in his fine book “Proteins: Energy, Heat, and Signal Flow” coedited with David Leitner [5].
[1] H. Fujisaki, L. Bu and J. E. Straub, “Vibrational Energy Relaxation (VER) of a CD stretching mode in cytochrome c,” Adv. Chem. Phys. 130, 179-203 (2005).[PDF]
[2] D. E. Sagnella, J. E. Straub, T. A. Jackson, M. Lim, and P. A. Anfinrud, “Vibrational population relaxation of carbon monoxide in the heme pocket of photolyzed carbonmonoxy myoglobin: Comparison of time-resolved mid-IR absorbance experiments and molecular dynamics simulations,” Proc. Natl. Acad. Sci. USA 96, 14324-14329 (1999). [PDF]
[3] D. E. Sagnella and J. E. Straub, “A study of vibrational relaxation of B-state carbon monoxide in the heme pocket of photolyzed carboxymyoglobin,” Biophys. J. 77, 70-84 (1999). [PDF]
[4] H. Fujisaki, Y. Zhang, and J.E. Straub, “Non-Markovian theory of vibrational energy relaxation and its applications to bimolecular systems,” Adv. Chem. Phys. 145, 1-33 (2011). [PDF]
[5] “Proteins: Energy, Heat and Signal Flow,” D. M. Leitner and J. E. Straub, Editors, Taylor and Francis Group, CRC Press (Boca Raton, 2009).
Enhanced Sampling and Global Optimization
Over the last twenty years, we have developed a variety of effective computational algorithms for global energy minimization and enhanced sampling. That work includes the development of the “quantum annealing” methods for molecular systems [1] and work on applications of highly effective generalized ensemble approaches for the simulation of complex molecular systems [2]. Recent work on Statistical Temperature Molecular Dynamics [3] algorithms as well as Generalized Replica Exchange Method [4] have show remarkable promise for use in simulating systems undergoing strong phase change [5].
[1] P. Amara, D. Hsu and J.E. Straub, “Global energy minimum searches using an approximate solution of the imaginary time Schroedinger equation,” J. Phys. Chem. 97, 6715-6721 (1993).[PDF]
[2] I. Andricioaei and J. E. Straub, Rapid Communication Generalized simulated annealing algorithms using Tsallis statistics: Application to conformational optimization of a tetrapeptide,” Phys. Rev. E 53, R3055-3058 (1996). [PDF]
[3] J. Kim, J. E. Straub, and T. Keyes, “Statistical-temperature Monte Carlo and Molecular Dynamics algorithms,” Phys. Rev. Lett. 97, 050601 (2006). [PDF]
[4] J. Kim, T. Keyes, and J. E. Straub, “Generalized Replica Exchange Method,” J. Chem. Phys. 132, 224107 (2010). [PDF]
[5] Q. Lu, J. Kim, and J. E. Straub, “Order parameter free enhanced sampling of the vapor-liquid transition using the generalized replica exchange method,” J. Chem. Phys. 138, 104119 (2013). [PDF]
Energy Landscapes and Reaction Paths
We carried out early work extending the “inherent structure” picture of Stillinger and coworkers to biomolecular systems [1]. That work led to the first application of instantaneous normal mode models to explore the energy landscape of proteins [2]. In the mid-1990s, we created the temperature-dependent reaction path methods for simulating variationally optimized reaction pathways for complex molecular systems, where the overall reactive flux is expressed as a bundle of varying reactive trajectories [3]. Our early work predates the subsequent explosion of work on the transition path sampling and other variational approaches to reaction path determination.
[1] B. J. Berne and J. E. Straub, “Novel methods of sampling phase space in the simulation of biological systems,” Curr. Opin. Struc. Bio. 7, 181-189 (1997).[PDF]
[2] J.E. Straub and D. Thirumalai, “Exploring the energy landscape in proteins,” Proc. Natl. Acad. Sci. USA 90, 809-813 (1993).[PDF]
[3] S. Huo and J. E. Straub, “The MaxFlux algorithm for calculating variationally optimized reaction paths for conformational transitions in many body systems at finite temperature,” J. Chem. Phys. 107, 5000-5006 (1997).[PDF]
[4] P. Zhang, S. W. Ahn, and J. E. Straub, “‘Strange Kinetics’ in the temperature dependence of methionine ligand rebinding dynamics in cytochrome c,” J. Phys. Chem. B 117, 7190-7202 (2013). [PDF]
