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 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.

• DOI:  10.1007/s00607-012-0191-2

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

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.

• DOI:  10.1145/2003351.2003354

The pK_a-cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pK_a values and protein electrostatics in general. The first round of the pK_a -cooperative, which challenged computational labs to carry out blind predictions against pK_as 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 pK_a calculations, emphasizing methods that were used by the participants in calculating the blind pK_a 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 pK_a calculations.

• DOI:  10.1002/prot.23189

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

There are several issues to be addressed concerning the management and effective use of information (or data), generated from nanotechnology studies in biomedical research and medicine. These data are large in volume, diverse in content, and are beset with gaps and ambiguities in the description and characterization of nanomaterials.  In this work, we have reviewed three areas of nanomedicine informatics: information resources; taxonomies, controlled vocabularies, and ontologies; and information standards.  Informatics methods and standards in each of these areas are critical for enabling collaboration, data sharing, unambiguous representation and interpretation of data, semantic (meaningful) search and integration of data; and for ensuring data quality, reliability, and reproducibility.   In particular, we have considered four types of information standards in this review, which are standard characterization protocols, common terminology standards, minimum information standards, and standard data communication (exchange) formats.  Currently, due to gaps and ambiguities in the data, it is also difficult to  apply computational methods and machine learning techniques to analyze, interpret and recognize patterns in data that are high dimensional in nature, and also to relate variations in nanomaterial properties to variations in their chemical composition, synthesis, characterization protocols, etc. Progress towards resolving the issues of information management in nanomedicine using informatics methods and standards discussed in this review will be essential to the rapidly growing field of nanomedicine informatics.

• PubMed ID:  21721140
• DOI:  10.1002/wnan.152

Protein pKa calculation methods are developed partly to provide fast non-experimental estimates of the ionization constants of protein side chains. However, the most
significant reason for developing such methods is that a good pKa calculation method is presumed to provide an accurate physical model of protein electrostatics, which can be
applied in methods for drug design, protein design and other structure-based energy calculation methods. We explore the validity of this presumption by simulating the
development of a pKa calculation method using artificial experimental data derived from a human-defined physical reality. We examine the ability of an RMSD-guided
development protocol to retrieve the correct (artificial) physical reality and find that a rugged optimization landscape and a huge parameter space prevent the identification of
the correct physical reality. We examine the importance of the training set in developing pKa calculation methods and investigate the effect of experimental noise on our ability
to identify the correct physical reality, and find that both effects have a significant and detrimental impact on the physical reality of the optimal model identified. Our findings
are of relevance to all structure-based methods for protein energy calculations and simulation, and have large implications for all types of current pKa calculation methods.
Our analysis furthermore suggests that careful and extensive validation on many types of experimental data can go some way in making current models more realistic.

• DOI:  10.1002/prot.23091
• PubMed ID:  21744393

Solvation is an elementary process in nature and is of paramount importance to more sophisticated chemical, biological and biomolecular processes. The understanding of solvation is an essential prerequisite for the quantitative description and analysis of biomolecular systems. This work presents a Lagrangian formulation of our differential geometry based solvation model. The Lagrangian representation of biomolecular surfaces has a few utilities/advantages. First, it provides an essential basis for biomolecular visualization, surface electrostatic potential map and visual perception of biomolecules. Additionally, it is consistent with the conventional setting of implicit solvent theories and thus, many existing theoretical algorithms and computational software packages can be directly employed. Finally, the Lagrangian representation does not need to resort to artificially enlarged van der Waals radii as often required by the Eulerian representation in solvation analysis. The main goal of the present work is to analyze the connection, similarity and difference between the Eulerian and Lagrangian formalisms of the solvation model. Such analysis is important to the understanding of the differential geometry based solvation model. The present model extends the scaled particle theory (SPT) of nonpolar solvation model with a solvent-solute interaction potential. The nonpolar solvation model is completed with a Poisson-Boltzmann (PB) theory based polar solvation model. The differential geometry theory of surfaces is employed to provide a natural description of solvent-solute interfaces. The minimization of the total free energy functional, which encompasses the polar and nonpolar contributions, leads to coupled potential driven geometric flow and Poisson-Boltzmann equations. Due to the development of singularities and nonsmooth manifolds in the Lagrangian representation, the resulting potential-driven geometric flow equation is embedded into the Eulerian representation for the purpose of computation, thanks to the equivalence of the Laplace-Beltrami operator in the two representations. The coupled partial differential equations (PDEs) are solved with an iterative procedure to reach a steady state, which delivers desired solvent-solute interface and electrostatic potential for problems of interest. These quantities are utilized to evaluate the solvation free energies and protein-protein binding affinities. A number of computational methods and algorithms are described for the interconversion of Lagrangian and Eulerian representations, and for the solution of the coupled PDE system. The proposed approaches have been extensively validated. We also verify that the mean curvature flow indeed gives rise to the minimal molecular surface (MMS) and the proposed variational procedure indeed offers minimal total free energy. Solvation analysis and applications are considered for a set of 17 small compounds and a set of 23 proteins. The salt effect on protein-protein binding affinity is investigated with two protein complexes by using the present model. Numerical results are compared to the experimental measurements and to those obtained by using other theoretical methods in the literature.

• DOI:  10.1007/s00285-011-0402-z
• PubMed ID:  21279359