Biological insertion of computationally designed short transmembrane segments

The great majority of helical membrane proteins are inserted co-translationally into the ER membrane through a continuous ribosome-translocon channel. The efficiency of membrane insertion depends on transmembrane (TM) helix amino acid composition, the helix length and the position of the amino acids within the helix. In this work, we conducted a computational analysis of the composition and location of amino acids in transmembrane helices found in membrane proteins of known structure to obtain an extensive set of designed polypeptide segments with naturally occurring amino acid distributions. Then, using an in vitro translation system in the presence of biological membranes, we experimentally validated our predictions by analyzing its membrane integration capacity. Coupled with known strategies to control membrane protein topology, these findings may pave the way to de novo membrane protein design

A single AKH neuropeptide activating three different fly AKH-receptors: an insecticide study via computational methods

Flies are a widely distributed pest insect that poses a significant threat to food security. Flight is essential for the dispersal of the adult flies to find new food sources and ideal breeding spots. The supply of metabolic fuel to power the flight muscles of insects is regulated by adipokinetic hormones (AKHs). The fruit fly, Drosophila melanogaster, the flesh fly, Sarcophaga crassipalpis, and the oriental fruit fly, Bactrocera dorsalis all have the same AKH that is present in the blowfly, Phormia terraenovae; this AKH has the code-name Phote-HrTH. Binding of the AKH to the extracellular binding site of a G protein-coupled receptor causes its activation. In this thesis, the structure of Phote-HrTH in SDS micelle solution was determined using NMR restrained molecular dynamics. The peptide was found to bind to the micelle and be reasonably rigid, with an S 2 order parameter of 0.96. The translated protein sequence of the AKH receptor from the fruit fly, Drosophila melanogaster, the flesh fly, Sarcophaga crassipalpis, and the oriental fruit fly, Bactrocera dorsalis were used to construct two models for each receptor: Drome-AKHR, Sarcr-AKHR, and Bacdo-AKHR. It is proposed that these two models represent the active and inactive state of the receptor. The models based on the crystal structure of the β-2 adrenergic receptor were found to bind Phote-HrTH with a predicted binding free energy of –107 kJ mol–1 for Drome-AKHR, –102 kJ mol–1 for Sarcr-AKHR and –102 kJ mol–1 for Bacdo-AKHR. Under molecular dynamics simulation, in a POPC membrane, the β-2AR receptor-like complexes transformed to rhodopsin-like. The identification and characterisation of the ligand-binding site of each receptor provide novel information on ligand-receptor interactions, which could lead to the development of species-specific control substances to use discriminately against these pest flies

Novel Strategies for Model-Building of G Protein-Coupled Receptors

The G protein-coupled receptors constitute still the most densely populated proteinfamily encompassing numerous disease-relevant drug targets. Consequently, medicinal chemistry is expected to pursue targets from that protein family in that hits need to be generated and subsequently optimized towards viable clinical candidates for a variety of therapeutic areas. For the purpose of rationalizing structure-activity relationships within such optimization programs, structural information derived from the ligand's as well as the macromolecule's perspective is essential. While it is relatively straightforward to define pharmacophore hypotheses based on comparative modelling of structurally and biologically characterized low-molecular weight ligands, a deeper understanding of the molecular recognition event underlying, remains challenging, since the principally available amount of experimentally derived structural data on GPCRs is extremely scarse when compared to, e.g., soluble enzymes. In this context, the protein modelling methodologies introduced, developed, optimized, and applied in this thesis provide structural models that are capable of assisting in the development of structural hypotheses on ligand-receptor complexes. As such they provide a valuable structural framework not only for a more detailed insight into ligand-GPCR interaction, but also for guiding the design process towards next-generation compounds which should display enhanced affinity. The model building procedure developed in this thesis systematically follows a hierarchical approach, sequentially generating a 1D topology, followed by a 2D topology that is finally converted into a 3D topology. The determination of a 1D topology is based on a compartmentalization of the linear amino acid sequence of a GPCR of interest into the extracellular, intracellular, and transmembrane sequence stretches. The entire chapter 3 of this study elaborates on the strengths and weaknesses of applying automated prediction tools for the purpose of identifying the transmembrane sequence domains. Based on an once derived 1D topology, a type of in-plane projection structure for the seven transmembrane helices can be derived with the aide of calculated vectorial property moments, yielding the 2D topology. Thorough bioinformatics studies revealed that only a consensus approach based on a conceptual combination of different methods employing a carefully made selection of parameter sets gave reliable results, emphasizing the danger to fully automate a GPCR modelling procedure. Chapter 4 describes a procedure to further expand the 2D topological findings into 3D space, exemplified on the human CCK-B receptor protein. This particular GPCR was chosen as the receptor of interest, since an enormous experimentally derived and structurally relevant data-set was available. Within the computational refinement procedure of constructed GPCR models, major emphasis was laid on the explicit treatment of a non-isotropic solvent environment during molecular mechanics (i.e. energy minimization and molecular dynamics simulations) calculations. The majority of simulations was therefore carried out in a tri-phasic solvent box accounting for a central lipid environment, flanked by two aqueous compartments, mimicking the extracellular and cytoplasmic space. Chapter 5 introduces the reference compound set, comprising low-molecular weight compounds modulating CCK receptors, that was used for validation purposes of the generated models of the receptor protein. Chapter 6 describes how the generated model of the CCK-B receptor was subjected to intensive docking studies employing compound series introduced in chapter 5. It turned out that by applying the DRAGHOME methodology viable structural hypotheses on putative receptor-ligand complexes could be generated. Based on the methodology pursued in this thesis a detailed model of the receptor binding site could be devised that accounts for known structure-activity relationships as well as for results obtained by site-directed mutagenesis studies in a qualitative manner. The overall study presented in this thesis is primarily aimed to deliver a feasibility study on generating model structures of GPCRs by a conceptual combination of tailor-made bioinformatics techniques with the toolbox of protein modelling, exemplified on the human CCK-B receptor. The generated structures should be envisioned as models only, not necessarily providing a detailed image of reality. However, consistent models, when verified and refined against experimental data, deliver an extremely useful structural contextual platform on which different scientific disciplines such as medicinal chemistry, molecular biology, and biophysics can effectively communicate

Development of a suite of bioinformatics tools for the analysis and prection of membrane protein structure.

This thesis describes the development of a novel approach for prediction of the three-dimensional structure of transmembrane regions of membrane proteins directly from amino acid sequence and basic transmembrane region topology.The development rationale employed involved a knowledge-based approach. Based on determined membrane protein structures, 20x20 association matrices were generated to summarise the distance associations between amino acid side chains on different alpha helical transmembrane regions of membrane proteins. Using these association matrices, combined with a knowledge-based scale for propensity for residue orientation in transmembrane segments (kPROT) (Pilpel et al., 1999), the software predicts the optimal orientations and associations of transmembrane regions and generates a 3D structural model of a given membrane protein, based on the amino acid sequence composition of its transmembrane regions. During the development, several structural and biostatistical analyses of determined membrane protein structures were undertaken with the aim of ensuring a consistent and reliable association matrix upon which to base the predictions. Evaluation of the model structures obtained for the protein sequences of a dataset of 17 membrane proteins of determined structure based on cross-validated leave-one-out testing revealed general1y high accuracy of prediction, with over 80% of associations between transmembrane regions being correctly predicted. These results provide a promising basis for future development and refinement of the algorithm, and to this end, work is underway using evolutionary computing approaches. As it stands, the approach gives scope for significant immediate benefit to researchers as a valuable starting point in the prediction of structure for membrane proteins of hitherto unknown structure.Tese (Doutorado em Filosofia) - University of Bedfordshire

MemProtMD: Automated Insertion of Membrane Protein Structures into Explicit Lipid Membranes

SummaryThere has been exponential growth in the number of membrane protein structures determined. Nevertheless, these structures are usually resolved in the absence of their lipid environment. Coarse-grained molecular dynamics (CGMD) simulations enable insertion of membrane proteins into explicit models of lipid bilayers. We have automated the CGMD methodology, enabling membrane protein structures to be identified upon their release into the PDB and embedded into a membrane. The simulations are analyzed for protein-lipid interactions, identifying lipid binding sites, and revealing local bilayer deformations plus molecular access pathways within the membrane. The coarse-grained models of membrane protein/bilayer complexes are transformed to atomistic resolution for further analysis and simulation. Using this automated simulation pipeline, we have analyzed a number of recently determined membrane protein structures to predict their locations within a membrane, their lipid/protein interactions, and the functional implications of an enhanced understanding of the local membrane environment of each protein

Development of a suite of bioinformatics tools for the analysis and prediction of membrane protein structure

This thesis describes the development of a novel approach for prediction of the three-dimensional structure of transmembrane regions of membrane proteins directly from amino acid sequence and basic transmembrane region topology. The development rationale employed involved a knowledge-based approach. Based on determined membrane protein structures, 20x20 association matrices were generated to summarise the distance associations between amino acid side chains on different alpha helical transmembrane regions of membrane proteins. Using these association matrices, combined with a knowledge-based scale for propensity for residue orientation in transmembrane segments (kPROT) (Pilpel et al., 1999), the software predicts the optimal orientations and associations of transmembrane regions and generates a 3D structural model of a gi ven membrane protein, based on the amino acid sequence composition of its transmembrane regions. During the development, several structural and biostatistical analyses of determined membrane protein structures were undertaken with the aim of ensuring a consistent and reliable association matrix upon which to base the predictions. Evaluation of the model structures obtained for the protein sequences of a dataset of 17 membrane proteins of detennined structure based on cross-validated leave-one-out testing revealed generally high accuracy of prediction, with over 80% of associations between transmembrane regions being correctly predicted. These results provide a promising basis for future development and refinement of the algorithm, and to this end, work is underway using evolutionary computing approaches. As it stands, the approach gives scope for significant immediate benefit to researchers as a valuable starting point in the prediction of structure for membrane proteins of hitherto unknown structure

Chemogenomic Analysis of G-Protein Coupled Receptors and Their Ligands Deciphers Locks and Keys Governing Diverse Aspects of Signalling

Understanding the molecular mechanism of signalling in the important super-family of G-protein-coupled receptors (GPCRs) is causally related to questions of how and where these receptors can be activated or inhibited. In this context, it is of great interest to unravel the common molecular features of GPCRs as well as those related to an active or inactive state or to subtype specific G-protein coupling. In our underlying chemogenomics study, we analyse for the first time the statistical link between the properties of G-protein-coupled receptors and GPCR ligands. The technique of mutual information (MI) is able to reveal statistical inter-dependence between variations in amino acid residues on the one hand and variations in ligand molecular descriptors on the other. Although this MI analysis uses novel information that differs from the results of known site-directed mutagenesis studies or published GPCR crystal structures, the method is capable of identifying the well-known common ligand binding region of GPCRs between the upper part of the seven transmembrane helices and the second extracellular loop. The analysis shows amino acid positions that are sensitive to either stimulating (agonistic) or inhibitory (antagonistic) ligand effects or both. It appears that amino acid positions for antagonistic and agonistic effects are both concentrated around the extracellular region, but selective agonistic effects are cumulated between transmembrane helices (TMHs) 2, 3, and ECL2, while selective residues for antagonistic effects are located at the top of helices 5 and 6. Above all, the MI analysis provides detailed indications about amino acids located in the transmembrane region of these receptors that determine G-protein signalling pathway preferences

Computational Protein Design and Molecular Dynamics Simulations: A Study of Membrane Proteins, Small Peptides and Molecular Systems

Molecular design and modeling can provide stringent assessment of our understanding of the structure and function of proteins. Due to the subtleness of the interactions that largely stabilize proteins, computational methods have been particularly valuable in establishing practical, formal and physically grounded protocols to study the structure and function of these biomolecules. Especifically, computational protein design seeks to identify sequences that fold into a desired structure and have specific structural and functional properties using computational methodologies. Among current techniques, an entropy-based formalism that efficiently determines the number and composition of sequences satisfying a predefined set of constraints seems particularly promising and powerful. Complementary to this methodology are the well-established molecular dynamics simulation techniques that have been extensively used to study structure, function and dynamics of biologically relevant systems. Herein different studies of systems using computational techniques to address particular molecular problems are described. Efforts to redesign membrane proteins to generate water-soluble variants were applied to a widely studied pentameric ligand-gated ion channel, the nicotinic acetylchoilne receptor (nAChR). NMR structures and binding studies demostrated the robustness and applicability of the computational design approach. Toward the creation of water-soluble variants of a G protein–coupled receptor (GPCR), comparative modeling and docking calculations were used to investigate the structure of the human μ opioid receptor and presented in light of previous mutagenesis studies of structure and agonist-induced activation. Candidate peptides for possible therapeutic agents were computationally analyzed. Peptide design, loop modeling and MD simulations were applied to investigate the stromal cell-derived factor-1&a; (SDF-1&a;). SDF-1&a; displays promising therapeutic benefits to treat blood-supply related heart disease and elicit growth of microvasculature. Simplified analogs of SDF-1&a; exhibit enhanced therapeutic properties in cell-based assays. MD simulations provide insights about the molecular features of this enhancement. One simplified peptide offers a potentially clinically translatable neovasculogenic therapy. Lastly, MD simulations were utilized to analyze a molecule with hindered internal rotors, a tribenzylamine hemicryptophane. The molecule was characterized by different experimental and computational techniques. The structural and dynamic features of the hemicryptophane molecule make it an attractive starting point for controlling internal rotation of aromatic rings within molecular systems

The MemProtMD database : a resource for membrane-embedded protein structures and their lipid interactions

Integral membrane proteins fulfil important roles in many crucial biological processes, including cell signalling, molecular transport and bioenergetic processes. Advancements in experimental techniques are revealing high resolution structures for an increasing number of membrane proteins. Yet, these structures are rarely resolved in complex with membrane lipids. In 2015, the MemProtMD pipeline was developed to allow the automated lipid bilayer assembly around new membrane protein structures, released from the Protein Data Bank (PDB). To make these data available to the scientific community, a web database (http://memprotmd.bioch.ox.ac.uk) has been developed. Simulations and the results of subsequent analysis can be viewed using a web browser, including interactive 3D visualizations of the assembled bilayer and 2D visualizations of lipid contact data and membrane protein topology. In addition, ensemble analyses are performed to detail conserved lipid interaction information across proteins, families and for the entire database of 3506 PDB entries. Proteins may be searched using keywords, PDB or Uniprot identifier, or browsed using classification systems, such as Pfam, Gene Ontology annotation, mpstruc or the Transporter Classification Database. All files required to run further molecular simulations of proteins in the database are provided

Novel Strategies for Model-Building of G Protein-Coupled Receptors

The G protein-coupled receptors constitute still the most densely populated proteinfamily encompassing numerous disease-relevant drug targets. Consequently, medicinal chemistry is expected to pursue targets from that protein family in that hits need to be generated and subsequently optimized towards viable clinical candidates for a variety of therapeutic areas. For the purpose of rationalizing structure-activity relationships within such optimization programs, structural information derived from the ligand's as well as the macromolecule's perspective is essential. While it is relatively straightforward to define pharmacophore hypotheses based on comparative modelling of structurally and biologically characterized low-molecular weight ligands, a deeper understanding of the molecular recognition event underlying, remains challenging, since the principally available amount of experimentally derived structural data on GPCRs is extremely scarse when compared to, e.g., soluble enzymes. In this context, the protein modelling methodologies introduced, developed, optimized, and applied in this thesis provide structural models that are capable of assisting in the development of structural hypotheses on ligand-receptor complexes. As such they provide a valuable structural framework not only for a more detailed insight into ligand-GPCR interaction, but also for guiding the design process towards next-generation compounds which should display enhanced affinity. The model building procedure developed in this thesis systematically follows a hierarchical approach, sequentially generating a 1D topology, followed by a 2D topology that is finally converted into a 3D topology. The determination of a 1D topology is based on a compartmentalization of the linear amino acid sequence of a GPCR of interest into the extracellular, intracellular, and transmembrane sequence stretches. The entire chapter 3 of this study elaborates on the strengths and weaknesses of applying automated prediction tools for the purpose of identifying the transmembrane sequence domains. Based on an once derived 1D topology, a type of in-plane projection structure for the seven transmembrane helices can be derived with the aide of calculated vectorial property moments, yielding the 2D topology. Thorough bioinformatics studies revealed that only a consensus approach based on a conceptual combination of different methods employing a carefully made selection of parameter sets gave reliable results, emphasizing the danger to fully automate a GPCR modelling procedure. Chapter 4 describes a procedure to further expand the 2D topological findings into 3D space, exemplified on the human CCK-B receptor protein. This particular GPCR was chosen as the receptor of interest, since an enormous experimentally derived and structurally relevant data-set was available. Within the computational refinement procedure of constructed GPCR models, major emphasis was laid on the explicit treatment of a non-isotropic solvent environment during molecular mechanics (i.e. energy minimization and molecular dynamics simulations) calculations. The majority of simulations was therefore carried out in a tri-phasic solvent box accounting for a central lipid environment, flanked by two aqueous compartments, mimicking the extracellular and cytoplasmic space. Chapter 5 introduces the reference compound set, comprising low-molecular weight compounds modulating CCK receptors, that was used for validation purposes of the generated models of the receptor protein. Chapter 6 describes how the generated model of the CCK-B receptor was subjected to intensive docking studies employing compound series introduced in chapter 5. It turned out that by applying the DRAGHOME methodology viable structural hypotheses on putative receptor-ligand complexes could be generated. Based on the methodology pursued in this thesis a detailed model of the receptor binding site could be devised that accounts for known structure-activity relationships as well as for results obtained by site-directed mutagenesis studies in a qualitative manner. The overall study presented in this thesis is primarily aimed to deliver a feasibility study on generating model structures of GPCRs by a conceptual combination of tailor-made bioinformatics techniques with the toolbox of protein modelling, exemplified on the human CCK-B receptor. The generated structures should be envisioned as models only, not necessarily providing a detailed image of reality. However, consistent models, when verified and refined against experimental data, deliver an extremely useful structural contextual platform on which different scientific disciplines such as medicinal chemistry, molecular biology, and biophysics can effectively communicate