Emerging Diversity in Lipid-Protein Interactions

Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid-protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid-protein interactions, and to link lipid-protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid-protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid-protein interactions, together with brief experimental and physiological contexts, aiming at comprehensive coverage of simulation papers in the last five years. Overall, a complex picture of lipid-protein interactions emerges, through a range of mechanisms including modulation of the physical properties of the lipid environment, detailed chemical interactions between lipids and proteins, and key functional roles of very specific lipids binding to well-defined binding sites on proteins. Computationally, despite important limitations, molecular dynamics simulations with current computer power and theoretical models are now in an excellent position to answer detailed questions about lipid-protein interactions

Cell-free expression and molecular modeling of the γ-secretase complex and G-protein-coupled receptors

Alzheimer’s disease (AD), which was first reported more than a century ago by Alhzeimer, is one of the commonest forms of dementia which affects >30 million people globally (>8 million in Europe). The origin and pathogenesis of AD is poorly understood and there is no cure available for the disease. AD is characterized by the accumulation of senile plaques composed of amyloid beta peptides (Ab 37-43) which is formed by the gamma secretase (GS) complex by cleaving amyloid precursor protein. Therefore GS can be an attractive drug target. Since GS processes several other substrates like Notch, CD44 and Cadherins, nonspecific inhibition of GS has many side effects. Due to the lack of crystal structure of GS, which is attributed to the extreme difficulties in purifying it, molecular modeling can be useful to understand its architecture. So far only low resolution cryoEM structures of the complex has been solved which only provides a rough structure of the complex at low 12-15 A resolution Furthermore the activity of GS in vitro can be achieved by means of cell-free (CF) expression. GS comprises catalytic subunits namely presenilins and supporting elements containing Pen-2, Aph-1 and Nicastrin. The origin of AD is hidden in the regulated intramembrnae proteolysis (RIP) which is involved in various physiological processes and also in leukemia. So far growth factors, cytokines, receptors, viral proteins, cell adhesion proteins, signal peptides and GS has been shown to undergo RIP. During RIP, the target proteins undergo extracellular shredding and intramembrane proteolysis. This thesis is based on molecular modeling, molecular dynamics (MD) simulations, cell-free (CF) expression, mass spectrometry, NMR, crystallization, activity assay etc of the components of GS complex and G-protein coupled receptors (GPCRs). First I validated the NMR structure of PS1 CTF in detergent micelles and lipid bilayers using coarse-grained MD simulations using MARTINI forcefield implemented in Gromacs. CTF was simulated in DPC micelles, DPPC and DLPC lipid bilayer. Starting from random configuration of detergent and lipids, micelle and lipid bilyer were formed respectively in presence of CTF and it was oriented properly to the micelle and bilyer during the simulation. Around DPC molecules formed micelle around CTF in agreement of the experimental results in which 80-85 DPC molecules are required to form micelles. The structure obtained in DPC was similar to that of NMR structure but differed in bilayer simulations showed the possibility of substrate docking in the conserved PAL motif. Simulations of CTF in implicit membrane (IMM1) in CHAMM yielded similar structure to that from coarse grained MD. I performed cell-free expression optimization, crystallization and NMR spectroscopy of Pen-2 in various detergent micelles. Additionally Pen-2 was modeled by a combination of rosetta membrane ab-initio method, HHPred distant homology modeling and incorporating NMR constraints. The models were validated by all atom and coarse grained MD simulations both in detergent micelles and POPC/DPPC lipid bilayers using MARTINI forcefield. GS operon consisting of all four subunits was co-expressed in CF and purified. The presence of of GS subunits after pull-down with Aph-1 was determined by western blotting (Pen-2) and mass spectrometry (Presenilin-1 and Aph-1). I also studied interactions of especially PS1 CTF, APP and NTF by docking and MD. I also made models and interfaces of Pen-2 with PS1 NTF and checked their stability by MD simulations and compared with experimental results. The goal is to model the interfaces between GS subunits using molecular modeling approaches based on available experimental data like cross-linking, mutations and NMR structure of C-terminal fragment of PS1 and transmembrane part of APP. The obtained interfaces of GS subunits may explain its catalysis mechanism which can be exploited for novel lead design. Due to lack of crystal/NMR structure of the GS subunits except the PS1 CTF, it is not possible to predict the effect of mutations in terms of APP cleavage. So I also developed a sequence based approach based on machine learning using support vector machine to predict the effect of PS1 CTF L383 mutations in terms of Aβ40/Aβ42 ratio with 88% accuracy. Mutational data derived from the Molgen database of Presenilin 1 mutations was using for training. GPCRs (also called 7TM receptors) form a large superfamily of membrane proteins, which can be activated by small molecules, lipids, hormones, peptides, light, pain, taste and smell etc. Although 50% of the drugs in market target GPCRs , only few are targeted therapeutically. Such wide range of targets is due to involvement of GPCRs in signaling pathways related to many diseases i.e. dementia (like Alzheimer's disease), metabolic (like diabetes) including endocrinological disorders, immunological including viral infections, cardiovascular, inflammatory, senses disorders, pain and cancer. Cannabinoid and adrenergic receptors belong to the class A (similar to rhodopsin) GPCRs. Docking of agonists and antagonists to CB1 and CB2 cannabinoid receptors revealed the importance of a centrally located rotamer toggle switch, and its possible role in the mechanism of agonist/antagonist recognition. The switch is composed of two residues, F3.36 and W6.48, located on opposite transmembrane helices TM3 and TM6 in the central part of the membranous domain of cannabinoid receptors. The CB1 and CB2 receptor models were constructed based on the adenosine A2A receptor template. The two best scored conformations of each receptor were used for the docking procedure. In all poses (ligand-receptor conformations) characterized by the lowest ligand-receptor intermolecular energy and free energy of binding the ligand type matched the state of the rotamer toggle switch: antagonists maintained an inactive state of the switch, whereas agonists changed it. In case of agonists of β2AR, the (R,R) and (S,S) stereoisomers of fenoterol, the molecular dynamics simulations provided evidence of different binding modes while preserving the same average position of ligands in the binding site. The (S,S) isomer was much more labile in the binding site and only one stable hydrogen bond was created. Such dynamical binding modes may also be valid for ligands of cannabinoid receptors because of the hydrophobic nature of their ligand-receptor interactions. However, only very long molecular dynamics simulations could verify the validity of such binding modes and how they affect the process of activation. Human N-formyl peptide receptors (FPRs) are G protein-coupled receptors (GPCRs) involved in many physiological processes, including host defense against bacterial infection and resolving inflammation. The three human FPRs (FPR1, FPR2 and FPR3) share significant sequence homology and perform their action via coupling to Gi protein. Activation of FPRs induces a variety of responses, which are dependent on the agonist, cell type, receptor subtype, and also species involved. FPRs are expressed mainly by phagocytic leukocytes. Together, these receptors bind a large number of structurally diverse groups of agonistic ligands, including N-formyl and nonformyl peptides of different composition, that chemoattract and activate phagocytes. For example, N-formyl-Met-Leu-Phe (fMLF), an FPR1 agonist, activates human phagocyte inflammatory responses, such as intracellular calcium mobilization, production of cytokines, generation of reactive oxygen species, and chemotaxis. This ligand can efficiently activate the major bactericidal neutrophil functions and it was one of the first characterized bacterial chemotactic peptides. Whereas fMLF is by far the most frequently used chemotactic peptide in studies of neutrophil functions, atomistic descriptions for fMLF-FPR1 binding mode are still scarce mainly because of the absence of a crystal structure of this receptor. Elucidating the binding modes may contribute to designing novel and more efficient non-peptide FPR1 drug candidates. Molecular modeling of FPR1, on the other hand, can provide an efficient way to reveal details of ligand binding and activation of the receptor. However, recent modelings of FPRs were confined only to bovine rhodopsin as a template. To locate specific ligand-receptor interactions based on a more appropriate template than rhodopsin we generated the homology models of FPR1 using the crystal structure of the chemokine receptor CXCR4, which shares over 30% sequence identity with FPR1 and is located in the same γ branch of phylogenetic tree of GPCRs (rhodopsin is located in α branch). Docking and model refinement procedures were pursued afterward. Finally, 40 ns full-atom MD simulations were conducted for the Apo form as well as for complexes of fMLF (agonist) and tBocMLF (antagonist) with FPR1 in the membrane. Based on locations of the N- and C-termini of the ligand the FPR1 extracellular pocket can be divided into two zones, namely, the anchor and activation regions. The formylated M1 residue of fMLF bound to the activation region led to a series of conformational changes of conserved residues. Internal water molecules participating in extended hydrogen bond networks were found to play a crucial role in transmitting the agonist-receptor interactions. A mechanism of initial steps of the activation concurrent with ligand binding is proposed. I accurately predicted the structure and ligand binding pose of dopamine receptor 3 (RMSD to the crystal structure: 2.13 Å) and chemokine receptor 4 (CXCR4, RMSD to the crystal structure 3.21 Å) in GPCR-Dock 2010 competition. The homology model of the dopamine receptor 3 was 8 th best overall in the competition

Multiscale Simulations of Biological Membranes : The Challenge To Understand Biological Phenomena in a Living Substance

Biological membranes are tricky to investigate. They are complex in terms of molecular composition and structure, functional over a wide range of time scales, and characterized by nonequilibrium conditions. Because of all of these features, simulations are a great technique to study biomembrane behavior. A significant part of the functional processes in biological membranes takes place at the molecular level; thus computer simulations are the method of choice to explore how their properties emerge from specific molecular features and how the interplay among the numerous molecules gives rise to function over spatial and time scales larger than the molecular ones. In this review, we focus on this broad theme. We discuss the current state-of-the-art of biomembrane simulations that, until now, have largely focused on a rather narrow picture of the complexity of the membranes. Given this, we also discuss the challenges that we should unravel in the foreseeable future. Numerous features such as the actin-cytoskeleton network, the glycocalyx network, and nonequilibrium transport under ATP-driven conditions have so far received very little attention; however, the potential of simulations to solve them would be exceptionally high. A major milestone for this research would be that one day we could say that computer simulations genuinely research biological membranes, not just lipid bilayers.Peer reviewe

Computational Modeling of Realistic Cell Membranes

Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead

A New Target for Pain: Development of Tools to Study Human Histidine Triad Nucleotide Binding proteins

University of Minnesota Ph.D. dissertation. September 2017. Major: Medicinal Chemistry. Advisors: Carston Wagner, Courtney Aldrich. 1 computer file (PDF); xviii, 259 pages.The research described in the current thesis elucidates the contributions to the characterization of Hint1 from the aspect of function, mechanism, and structural determinants. To do so, we describe development of variety of tools to study Human Hint proteins. The first two chapters provide the first strong evidence of the role of Hint1 active site in the CNS using chemical genetics, medicinal chemistry and neuropharmacological studies. In Chapter 3, we describe the covalent capture of the Hint1-adenylated intermediate using an alternate substrate and time-lapse crystallographic studies as part of our efforts to elucidate reaction trajectory of Hint1. In Chapter 4, we describe the development of switch-on fluorescent probes as tools for monitoring the active site and detection of Hint proteins. Chapter 5 describes the structural and functional characterization of genetic mutations of Hint1 associated with peripheral neuropathy as well as structural determinants of Hint1