Light-harvesting and structural organization of Photosystem II: from individual complexes to thylakoid membrane

Photosystem II (PSII) is responsible for the water oxidation in photosynthesis and it consists of many proteins and pigment-protein complexes in a variable composition, depending on environmental conditions. Sunlight-induced charge separation lies at the basis of the photochemical reactions and it occurs in the reaction center (RC). The RC is located in the PSII core which also contains light-harvesting complexes CP43 and CP47. The PSII core of plants is surrounded by external light-harvesting complexes (lhcs) forming supercomplexes, which together with additional external lhcs, are located in the thylakoid membrane where they perform their functions. In this paper we provide an overview of the available information on the structure and organization of pigment-protein complexes in PSII and relate this to experimental and theoretical results on excitation energy transfer (EET) and charge separation (CS). This is done for different subcomplexes, supercomplexes, PSII membranes and thylakoid membranes. Differences in experimental and theoretical results are discussed and the question is addressed how results and models for individual complexes relate to the results on larger systems. It is shown that it is still very difficult to combine all available results into one comprehensive picture.     

Dynamic imaging of single DNA-protein interactions using atomic force microscopy

Atomic force microscopy (AFM) imaging of static DNA-protein complexes, in air and in liquid, can be used to directly obtain quantitative and qualitative information on the structure of different complexes. For example, DNA length, the location of preferential binding sites for proteins and bending of DNA as a result of the complexation can all be measured. Recording consecutive AFM images of DNA and protein molecules under conditions that they are still able to move and interact, or dynamic AFM imaging, however, can reveal information on the dynamic aspects of the interactions between these molecules. Here, an overview is given of the technical challenges that need to be considered for successful dynamic AFM imaging studies of individual DNA-protein interactions. Necessary technical improvements to the AFM set-up and the development of new sample preparation methods are described in this paper.     

The resonance Raman spectra of spheroidene revisited with a first-principles approach

The resonance Raman (RR) spectra of different configurations of spheroidene are calculated by means of quantum chemical methods to investigate the nature of the cis configuration of this carotenoid molecule in the photosynthetic reaction center (RC) of the purple bacterium Rhodobacter sphaeroides. For validation of our methodology, we also calculate the spectrum of the all-trans structure present in the light-harvesting complexes of this bacterium. While former theoretical resonance Raman studies only considered truncated models of spheroidene, we report on calculations employing the full pigment here. The calculated frequencies for the all-trans configuration are in good agreement with former experimental and simulated data. Among the possible cis structures, the 15,15'-cis configuration shows a RR spectrum that is in best agreement with the experimental spectrum of spheroidene in the RC. In order to assess model truncation effects, we compare calculations for the full spheroidene molecule to those for the truncated model. While the main features can already be found in the latter, the full model leads to considerably different intensities in the region around 1150 cm(-1), which improve the agreement with experiment. A slight mismatch for the vibrational frequencies in the C=C stretch region is investigated by considering a model for spheroidene in the binding pocket comprising more than 500 atoms in total. The results do not lead to improved agreement with experiment, in contrast to the simpler strategy of introducing constraints in the structural optimization of a truncated spheroidene model. The calculated RR spectrum of the 13,14-cis configuration shows additional features which can also be identified in the experimental RR spectrum. This shows that the most likely cis structure is the 15,15'-cis configuration. Besides this, the 13,14-cis configuration remains a candidate for an additional spheroidene structure in the RC of Rhodobacter sphaeroides mutant R26.     

Directed formation of micro- and nanoscale patterns of functional light-harvesting LH2 complexes

The precision placement of the desired protein components on a suitable substrate is an essential prelude to any hybrid "biochip" device, but a second and equally important condition must also be met: the retention of full biological activity. Here we demonstrate the selective binding of an optically active membrane protein, the light-harvesting LH2 complex from Rhodobacter sphaeroides, to patterned self-assembled monolayers at the micron scale and the fabrication of nanometer-scale patterns of these molecules using near-field photolithographic methods. In contrast to plasma proteins, which are reversibly adsorbed on many surfaces, the LH2 complex is readily patterned simply by spatial control of surface polarity. Near-field photolithography has yielded rows of light-harvesting complexes only 98 nm wide. Retention of the native optical properties of patterned LH2 molecules was demonstrated using in situ fluorescence emission spectroscopy.     

Lipid/protein interactions and the membrane/water interfacial region

Despite the importance of lipid/protein interactions in the folding, assembly, stability, and function of membrane proteins, information at an atomic level on how such proteins interact with the lipids that surround them remains sparse. The dynamics and flexible nature of the protein/bilayer interaction make it difficult to study, for example, by crystallographic means. However, based on recent progress in molecular simulations of membranes it is possible to address this problem computationally. This communication reports one of the first attempts to use multiple ns molecular simulations to establish a qualitative picture of the intermolecular interactions between the lipids of a bilayer and two topologically different membrane proteins for which a high resolution (2 A or better) X-ray structure is available.     

Efficient and non-denaturing membrane solubilization combined with enrichment of membrane protein complexes by detergent/polymer aqueous two-phase partitioning for proteome analysis

It is of central interest in membrane proteomics to establish methods that combine efficient solubilization with enrichment of proteins and intact protein complexes. We have investigated the quantitative and qualitative solubilization efficiency of five commercially available detergents using mitochondria from the yeast Saccharomyces cerevisiae as model system. Combining the zwitterionic detergent Zwittergent 3-10 and the non-ionic detergent Triton X-114 resulted in a complementary solubilization of proteins, which was similar to that of the anionic detergent sodium dodecyl sulfate (SDS). The subsequent removal of soluble proteins by detergent/polymer two-phase system partitioning was further enhanced by addition of SDS and increasing pH. A large number of both integral and peripheral membrane protein subunits from mitochondrial membrane protein complexes were identified in the detergent phase. We suggest that the optimized solubilization protocol in combination with detergent/polymer two-phase partitioning is a mild and efficient method for initial enrichment of membrane proteins and membrane protein complexes in proteomic studies.     

Individually double minimum-distance definition of protein–RNA binding residues and application to structure-based prediction

Identifying protein–RNA binding residues is essential for understanding the mechanism of protein–RNA interactions. So far, rigid distance thresholds are commonly used to define protein–RNA binding residues. However, after investigating 182 non-redundant protein–RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein–RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein–RNA binding sites.     

Structure, topology, and dynamics of membrane peptides and proteins from solid-state NMR spectroscopy

The high-resolution structure of membrane proteins is notoriously difficult to determine due to the hydrophobic nature of the protein-membrane complexes. Solid-state NMR spectroscopy is a unique and powerful atomic-resolution probe of the structure and dynamics of these important biological molecules. A number of new solid-state NMR methods for determining the depth of insertion, orientation, oligomeric structure, and long-range (10-15 A) distances of membrane proteins are summarized. Membrane protein depths can now be determined using several complementary techniques with varying site-specificity, distance precision, and mobility requirement on the protein. Membrane protein orientation can now be determined with or without macroscopic alignment, the latter providing a novel alternative for orientation determination of intrinsically curvature-inducing proteins. The novel analyses of beta-sheet membrane protein orientation are described. The quaternary structure of membrane peptide assemblies can now be elucidated using a 19F spin diffusion technique that simultaneously yields the oligomeric number and intermolecular distances up to 15 A. Finally, long-range distances up to approximately 10 A can now be measured using 1H spins with an accuracy of better than 1 A. These methods are demonstrated on several beta-sheet membrane peptides with antimicrobial activities and on two alpha-helical ion-channel proteins. Finally, we show that the nearly ubiquitous dynamics of membrane proteins can be readily examined using 2D correlation experiments. An intimate appreciation of molecular motion in these systems not only leads to important insights into the specific function of these membrane proteins but also may be exploited for other purposes such as orientation determination.     

HADDOCK: a protein-protein docking approach based on biochemical or biophysical information

The structure determination of protein-protein complexes is a rather tedious and lengthy process, by both NMR and X-ray crystallography. Several methods based on docking to study protein complexes have also been well developed over the past few years. Most of these approaches are not driven by experimental data but are based on a combination of energetics and shape complementarity. Here, we present an approach called HADDOCK (High Ambiguity Driven protein-protein Docking) that makes use of biochemical and/or biophysical interaction data such as chemical shift perturbation data resulting from NMR titration experiments or mutagenesis data. This information is introduced as Ambiguous Interaction Restraints (AIRs) to drive the docking process. An AIR is defined as an ambiguous distance between all residues shown to be involved in the interaction. The accuracy of our approach is demonstrated with three molecular complexes. For two of these complexes, for which both the complex and the free protein structures have been solved, NMR titration data were available. Mutagenesis data were used in the last example. In all cases, the best structures generated by HADDOCK, that is, the structures with the lowest intermolecular energies, were the closest to the published structure of the respective complexes (within 2.0 A backbone RMSD).     

Machine learning algorithms for predicting protein folding rates and stability of mutant proteins: comparison with statistical methods

Machine learning algorithms have wide range of applications in bioinformatics and computational biology such as prediction of protein secondary structures, solvent accessibility, binding site residues in protein complexes, protein folding rates, stability of mutant proteins, and discrimination of proteins based on their structure and function. In this work, we focus on two aspects of predictions: (i) protein folding rates and (ii) stability of proteins upon mutations. We briefly introduce the concepts of protein folding rates and stability along with available databases, features for prediction methods and measures for prediction performance. Subsequently, the development of structure based parameters and their relationship with protein folding rates will be outlined. The structure based parameters are helpful to understand the physical basis for protein folding and stability. Further, basic principles of major machine learning techniques will be mentioned and their applications for predicting protein folding rates and stability of mutant proteins will be illustrated. The machine learning techniques could achieve the highest accuracy of predicting protein folding rates and stability. In essence, statistical methods and machine learning algorithms are complimenting each other for understanding and predicting protein folding rates and the stability of protein mutants. The available online resources on protein folding rates and stability will be listed.     

Use of engineered unique cysteine residues to facilitate oriented coupling of proteins directly to a gold substrate

A prerequisite for any "lab on a chip" device that utilizes an electrical signal from the sensor protein is the ability to attach the protein in a specific orientation onto a conducting substrate. Here, we demonstrate the covalent attachment to a gold surface of light-harvesting membrane proteins, from Rhodobacter sphaeroides, via cysteine (Cys) residues engineered on either the cytoplasmic or periplasmic face. This simple directed attachment is superior in its ability to retain light-harvesting complex (LHC) function, when compared to a similar attachment procedure utilizing a self-assembled monolayer on gold. LH 1 has previously been observed to have superior photostability over LH 2 (Magis et al. [2010] Biochim. Biophys. Acta, 1798, 637-645); this characteristic is maintained even with the introduction of Cys residues.     

Structure–function relationships of the supramolecular assembly of the bacterial photosynthetic antenna complexes in lipid membranes

Appropriate experimental platforms are required to clarify the structure–function relationships of membrane protein assemblies. In photosynthetic bacteria, light-harvesting complex 2 and light-harvesting/reaction center core complex play key roles in capturing and transferring light energy and facilitating subsequent charge separation. These photosynthetic apparatuses form a supramolecular assembly in the photosynthetic membrane. However, the mechanism through which this assembly influences the efficiency of energy conversion remains to be clarified. We review our recent studies that were conducted to evaluate the structure–function relationship of the supramolecular assembly of photosynthetic antenna complexes in various lipid bilayer systems, as well as the construction of novel systems of planar lipid membranes for use as experimental platforms.     

Protein structure prediction and biomolecular recognition: from protein sequence to peptidomimetic design with the human beta3 integrin

Computational tools can bridge the gap between sequence and protein 3D structure based on the notion that information is to be retrieved from the databases and that knowledge-based methods can help in approaching a solution of the protein-folding problem. To this aim our group has implemented neural network-based predictors capable of performing with some success in different tasks, including predictions of the secondary structure of globular and membrane proteins, the topology of membrane proteins and porins and stable alpha-helical segments suited for protein design. Moreover we have developed methods for predicting contact maps in proteins and the probability of finding a cysteine in a disulfide bridge, tools which can contribute to the goal of predicting the 3D structure starting from the sequence (the so called ab initio prediction). All our predictors take advantage of evolution information derived from the structural alignments of homologous (evolutionary related) proteins and taken from the sequence and structure databases. When it is necessary to build models for proteins of unknown spatial structure, which have very little homology with other proteins of known structure, non-standard techniques need to be developed and the tools for protein structure predictions may help in protein modeling. The results of a recent simulation performed in our lab highlights the role of high performing computing technology and of tools of computational biology in protein modeling and peptidomimetic design.     

Solvent and lipid accessibility prediction as a basis for model quality assessment in soluble and membrane proteins

On-going efforts to improve protein structure prediction stimulate the development of scoring functions and methods for model quality assessment (MQA) that can be used to rank and select the best protein models for further refinement. In this work, sequence-based prediction of relative solvent accessibility (RSA) is employed as a basis for a simple MQA method for soluble proteins, and subsequently extended to the much less explored case of (alpha-helical) membrane proteins. In analogy to soluble proteins, the level of exposure to the lipid of amino acid residues in transmembrane (TM) domains is captured in terms of the relative lipid accessibility (RLA), which is predicted from sequence using low-complexity Support Vector Regression models. On an independent set of 23 TM proteins, the new SVR-based predictor yields correlation coefficient (CC) of 0.56 between the predicted and observed RLA profiles, as opposed to CC of 0.13 for a baseline predictor that utilizes TMLIP2H empirical lipophilicity scale (with standard deviations of about 0.15). A simple MQA approach is then defined by ranking models of membrane proteins in terms of consistency between predicted and observed RLA profiles, as a measure of similarity to the native structure. The new method does not require a set of decoy models to optimize parameters, circumventing current limitations in this regard. Several different sets of models, including those generated by fragment based folding simulations, and decoys obtained by swapping TM helices to mimic errors in template based assignment, are used to assess the new approach. Predicted RLA profiles can be used to successfully discriminate near native models from non-native decoys in most cases, significantly improving the separation of correct and incorrectly folded models compared to a simple baseline approach that utilizes TMLIP2H. As suggested by the robust performance of a simple MQA method for soluble proteins that utilizes more accurate RSA predictions, further significant improvements are likely to be achieved. The steady growth in the number of resolved membrane protein structures is expected to yield enhanced RLA predictions, facilitating further efforts to improve de novo and template based prediction of membrane protein structure.     

A sequence-based computational model for the prediction of the solvent accessible surface area for α-helix and β-barrel transmembrane residues

Predicting the solvent accessible surface area (ASA) of transmembrane (TM) residues is of great importance for experimental researchers to elucidate diverse physiological processes. TM residues fall into two major structural classes (α-helix membrane protein and β-barrel membrane protein). The reported solvent ASA prediction models were developed for these two types of TM residues respectively. However, this prevents the general use of these methods because one cannot determine which model is suitable for a given TM residue without information of its type. To conquer this limitation, we developed a new computational model that can be used for predicting the ASA of both TM α-helix and β-barrel residues. The model was developed from 78 α-helix membrane protein chains and 24 β-barrel membrane protein. Its prediction ability was evaluated by cross validation method and its prediction result on an independent test set of 20 membrane protein chains. The results show that our model performs well for both types of TM residues and outperforms other prediction model which was developed for the specific type of TM residues. The prediction results also proved that the random forest model incorporating conservation score is an effective sequence-based computational approach for predicting the solvent ASA of TM residues.     

Increasing the accuracy of solution NMR structures of membrane proteins by application of residual dipolar couplings. High-resolution structure of outer membrane protein A

The structure determination of membrane proteins is one of the most challenging applications of solution NMR spectroscopy. The paucity of distance information available from the highly deuterated proteins employed requires new approaches in structure determination. Here we demonstrate that significant improvement in the structure accuracy of the membrane protein OmpA can be achieved by refinement with residual dipolar couplings (RDCs). The application of charged polyacrylamide gels allowed us to obtain two alignments and accurately measure numerous heteronuclear dipolar couplings. Furthermore, we have demonstrated that using a large set of RDCs in the refinement can yield a structure with 1 A rms deviation to the backbone of the high-resolution crystal structure. Our simulations with various data sets indicate that dipolar couplings will be critical for obtaining accurate structures of membrane proteins.     

Insertion and assembly of membrane proteins via simulation

Interactions of lipids are central to the folding and stability of membrane proteins. Coarse-grained molecular dynamics simulations have been used to reveal the mechanisms of self-assembly of protein/membrane and protein/detergent complexes for representatives of two classes of membrane protein, namely, glycophorin (a simple alpha-helical bundle) and OmpA (a beta-barrel). The accuracy of the coarse-grained simulations is established via comparison with the equivalent atomistic simulations of self-assembly of protein/detergent micelles. The simulation of OmpA/bilayer self-assembly reveals how a folded outer membrane protein can be inserted in a bilayer. The glycophorin/bilayer simulation supports the two-state model of membrane folding, in which transmembrane helix insertion precedes dimer self-assembly within a bilayer. The simulations also suggest that a dynamic equilibrium exists between the glycophorin helix monomer and dimer within a bilayer. The simulated glycophorin helix dimer is remarkably close in structure to that revealed by NMR. Thus, coarse-grained methods may help to define mechanisms of membrane protein (re)folding and will prove suitable for simulation of larger scale dynamic rearrangements of biological membranes.     

Separation and identification of photosynthetic antenna membrane proteins by high- performance liquid chromatography electrospray ionization mass spectrometry

Functional proteomics of membrane proteins is an important tool for the understanding of protein networks in biological membranes. Nevertheless, structural studies on this part of the proteome are limited. The present review attempts to cover the vast array of methods that have appeared in the last few years for separation and identification of photosynthetic proteins of thylakoid membranes present in chloroplasts, a good model for setting up analytical methods suitable for membrane proteins. The two major methods for the separation of thylakoid membrane proteins are gel electrophoresis and liquid chromatography. Isoelectric focusing in a first dimension followed by denaturing sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) in a second dimension is an effective way to resolve large numbers of soluble and peripheral membrane proteins. However, it is not applicable for isolation of native protein complexes or for the separation of highly hydrophobic membrane proteins. High-performance liquid chromatography (HPLC), on the other hand, is highly suitable for any type of membrane protein separation due to its compatibility with detergents that are necessary to keep the hydrophobic proteins in solution. With regard to the identification of the separated proteins, several methods are available, including immunological and mass spectrometric methods. Besides immunological identification, peptide mass fingerprinting, peptide fragment fingerprinting or intact molecular mass determination by electrospray ionization mass spectrometry (ESI-MS) or matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) have been shown to be very sensitive and effective. In particular, identification of proteins by their intact molecular mass is advantageous for the investigation of numerous biological problems, because it is rapid and reflects the full sequence of the protein and all its posttranslational modifications. However, intact molecular mass determinations of gel-separated membrane proteins are hampered due to the difficulties in extracting the hydrophobic proteins from the gel, whereas HPLC on-line interfaced with ESI-MS enables the rapid and accurate determination of intact molecular masses and consequently an unequivocal protein identification. This strategy can be viewed as a multidimensional separation technique distinguishing between hydrophobicity in the first dimension and between different mass-to-charge ratios in the second dimension, allowing the separation and identification even of isomeric forms.     

Selective recruitment of membrane protein complexes onto gold substrates patterned by dip-pen nanolithography

Dip-pen nanolithography (DPN) is employed to develop a generic array platform for the selective recruitment of membrane protein complexes. An atomic force microscope tip inked with HS(CH2)16NH2 is used to generate amino-terminated domains on gold. These domains can be arranged into microscopic and submicroscopic patterns, and the untreated gold substrate is subsequently blocked with HS(CH2)2CONH(CH2CH2O)15CH3, a compound known to resist the unspecific binding of proteins and cells. The patterned gold substrate is exposed to an enriched membrane fraction from mutant Rhodobacter sphaeroides, which contains photosynthetic core complexes consisting of the reaction center and the light-harvesting complex LH1. The selective recruitment to the patterned domains, governed primarily by electrostatic interactions, is confirmed by contact mode atomic force microscopy.     

Protein structure prediction and biomolecular recognition: From protein sequence to peptidomimetic design with the human β 3 integrin

Computational tools can bridge the gap between sequence and protein 3D structure based on the notion that information is to be retrieved from the databases and that knowledge-based methods can help in approaching a solution of the protein-folding problem. To this aim our group has implemented neural network-based predictors capable of performing with some success in different tasks, including predictions of the secondary structure of globular and membrane proteins, the topology of membrane proteins and porins and stable f -helical segments suited for protein design. Moreover we have developed methods for predicting contact maps in proteins and the probability of finding a cysteine in a disulfide bridge, tools which can contribute to the goal of predicting the 3D structure starting from the sequence (the so called ab initio prediction). All our predictors take advantage of evolution information derived from the structural alignments of homologous (evolutionary related) proteins and taken from the sequence and structure databases. When it is necessary to build models for proteins of unknown spatial structure, which have very little homology with other proteins of known structure, non-standard techniques need to be developed and the tools for protein structure predictions may help in protein modeling. The results of a recent simulation performed in our lab highlights the role of high performing computing technology and of tools of computational biology in protein modeling and peptidomimetic design.