Simulation of fusion-mediated nanoemulsion interactions with model lipid bilayers
Lee S-J, Schlesinger PH, Wickline SA, Lanza GM, Baker NA. Simulation of fusion-mediated nanoemulsion interactions with model lipid bilayers. Soft Matter, in press.
Perfluorocarbon-based nanoemulsion particles have become promising platforms for the delivery of therapeutic and diagnostic agents to specific target cells in a non-invasive manner. A “contact-facilitated” delivery mechanism has been proposed wherein the emulsifying phospholipid monolayer on the nanoemulsion surface contacts and forms a lipid complex with the outer monolayer of target cell plasma membrane, allowing cargo to diffuse to the surface of target cell. While this mechanism is supported by experimental evidence, its molecular details are unknown.The present study develops a coarse-grained model of nanoemulsion particles that are compatible with the MARTINI force field. Simulations using this coarse-grained model have demonstrated multiple fusion events between the particles and a model vesicular lipid bilayer. The fusion proceeds in the following sequence: dehydration at the interface, close apposition of the particles, protrusion of hydrophobic molecules to the particle surface, transient lipid complex formation, absorption of nanoemulsion into the liposome. The initial monolayer disruption acts as a rate-limiting step and is strongly influenced by particle size as well as by the presence of phospholipids supporting negative spontaneous curvature. The core-forming perfluorocarbons play critical roles in initiating the fusion process by facilitating protrusion of hydrophobic moieties into the interface between the two particles. This study directly supports the hypothesized nanoemulsion delivery mechanism and provides the underlying molecular details that enable engineering of nanoemulsions for a variety of medical applications.
Nanoinformatics: developing new computing applications for nanomedicine
Maojo V, Fritts M, Martin-Sanchez F, De la Iglesia D, Cachau RE, Garcia-Remesal M, Crespo J, Mitchell JA, Anguita A, Baker N, Barreiro JM, Benitez SE, De la Calle G, Facelli JC, Ghazal P, Geissbuhler A, Gonzalez-Nilo F, Graf N, Grangeat P, Hermosilla I, Hussein R, Kern J, Koch S, Legre Y, Lopez-Alonso V, Lopez-Campos G, Otero P, Pazos A, Perez-Rey D, Potamias G, Sanz F, Kulikowski C. Nanoinformatics: developing new computing applications for nanomedicine. Computing, in press.
Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended “nanotype” to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others.
Biomolecular electrostatics and solvation: a computational perspective
Ren P, Chun J, Thomas DG, Schnieders MJ, Marucho M, Zhang J, Baker NA. Biomolecular electrostatics and solvation: a computational perspective. Quart Rev Biophys, in press.
An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view towards describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g., solvent structure, polarization, ion binding, and nonpolar behavior) in order to provide a background to understand the different types of solvation models.
Interaction of Melittin Peptides with Perfluorocarbon Nanoemulsion Particles
Lee SJ, Schlesinger PH, Wickline SA, Lanza GM, Baker NA. Interaction of Melittin Peptides with Perfluorocarbon Nanoemulsion Particles. J Phys Chem B, 115 (51), 15271-15279, 2011.
Melittin, an anti-microbial peptide, forms pores in biological membranes and triggers cell death. Therefore it has potential as an anti-cancer therapy. However, until recently, the therapeutic application of melittin has been impractical because a suitable platform for delivery was not available. Recently, we showed that phospholipid stabilized perfluorooctylbromide- based nanoemulsion particles (PFOB-NEPs) were resistant to destruction by melittin and enabled specific delivery of melittin to tumor cells, killing them and reducing tumor growth. Earlier prior work also showed that melittin adsorbed onto the stabilizing phospholipid monolayer of PFOB-NEP but did not disrupt the phospholipid monolayer or produce “cracking” of the PFOB-NEPs. The present work identifies the important structural motifs for melittin binding to PFOB-NEPs through a series of atomistic molecular dynamics simulations. The conformational ensemble of melittin bound to PFOB-NEP lipid monolayer was compared to structure from a control simulation of melittin bound to a lipid bilayer to identify several differences in melittin-lipid interactions between the two systems. First, melittin was deeply buried in the hydrophobic tail region of bilayer, while its depth was attenuated in the PFOB-NEP monolayer. Second, a helical conformation was the major secondary structure in the bilayer, but the fraction of helix was reduced in the PFOB-NEP. Finally, the overall pattern for the direct interaction of melittin with surrounding lipids was similar between liposome and PFOB-NEP, but the level of interaction was slightly decreased in the PFOB-NEP. These results suggest that melittin interacts with the monolayer of PFOB-NEP in a way that is similar way to its interaction with bilayers but that deeper penetration into the hydrophobic interior is inhibited.
- PubMed ID: 22050303
- DOI: 10.1021/jp209543c
Using physicochemical properties of amino acids to induce graphical models of residue couplings
Tozammel Hossain KSM, Bailey-Kellogg C, Friedman AM, Bradley MJ, Baker N, Ramakrishnan N. Using physicochemical properties of amino acids to induce graphical models of residue couplings. BIOKDD ’11, 2011.
Residue coupling in protein families is an important indicator for structural and functional conservation. Two residues are coupled if changes of amino acid at one residue location are correlated with changes in the other. Many algorithmic techniques have been proposed to discover couplings in protein families. These approaches discover couplings over amino acid combinations but do not yield mechanistic or other explanations for such couplings. We propose to study couplings in terms of amino acid classes such as polarity, hydrophobicity, size, and reactivity, and present two algorithms for learning probabilistic graphical models of amino acid class-based residue couplings. Our probabilistic graphical models provide a sound basis for predictive, diagnostic, and abductive reasoning. Further, our methods can take optional structural priors into account for building graphical models. The resulting models are useful in assessing the likelihood of a new protein to be a member of a family and for designing new protein sequences by sampling from the graphical model. We apply our approaches to understand couplings in two protein families: Nickel-responsive transription factors (NikR) and G-protein coupled receptors (GPCRs). The results demonstrate that our graphcial models based on sequences, physicochemical properties, and protein structure are capable of detecting amino acid class-based couplings between important residues that play roles in activities of these two families.
Progress in the prediction of pKa values in proteins
Alexov E, Mehler EL, Baker N, Baptista A, Huang Y, Milletti F, Nielsen JE, Farrell D, Carstensen T, Olsson MHM, Shen JK, Warwicker J, Williams, Word MJ. Progress in the prediction of pKa values in proteins. Proteins, 79, 3260-3275, 2011.
The pKa-cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pKa values and protein electrostatics in general. The first round of the pKa -cooperative, which challenged computational labs to carry out blind predictions against pKas experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6-10, 2009). This paper serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here we briefly outline existing approaches for pKa calculations, emphasizing methods that were used by the participants in calculating the blind pKa values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pKa calculations.
- DOI: 10.1002/prot.23189
Side-chain oxysterols: from cells to membranes to molecules
Olsen BN, Schlesinger PH, Ory DS, Baker NA. Side-chain oxysterols: from cells to membranes to molecules. BBA Biomembranes, 1818, 330-336, 2012.
This review discusses the application of cellular biology, molecular biophysics, and computational simulation to understand membrane-mediated mechanisms by which oxysterols regulate cholesterol homeostasis. Side-chain oxysterols, which are produced enzymatically in vivo, are physiological regulators of cholesterol homeostasis and primarily serve as cellular signals for excess cholesterol. These oxysterols regulate cholesterol homeostasis through both transcriptional and non-transcriptional pathways; however, many molecular details of their interactions in these pathways are still not well understood. Cholesterol trafficking provides one mechanism for regulation. The current model of cholesterol trafficking regulation is based on the existence of two distinct cholesterol pools in the membrane: a low and a high availability/activity pool. It is proposed that the low availability/activity pool of cholesterol is integrated into tightly packing phospholipids and relatively inaccessible to water or cellular proteins, while the high availability cholesterol pool is more mobile in the membrane and is present in membranes where the phospholipids are not as compressed. Recent results suggest that that oxysterols may promote cholesterol egress from membranes by shifting cholesterol from the low to the high activity pools. Furthermore, molecular simulations suggest a potential mechanism for oxysterol “activation” of cholesterol through its displacement in the membrane. This review discusses these results as well as several other important interactions between oxysterols and cholesterol in cellular and model lipid membranes.
- DOI: 10.1016/j.bbamem.2011.06.014
- PubMed ID: 21745458
