Overall energy conversion efficiency of a photosynthetic vesicle

From Monochrome to Technicolor: Simple Generic Approaches to Multicomponent Protein Nanopatterning Using Siloxanes with Photoremovable Protein-Resistant Protecting Groups

We show that sequential protein deposition is possible by photodeprotection of films formed from a tetraethylene-glycol functionalized nitrophenylethoxycarbonyl-protected aminopropyltriethoxysilane (NPEOC-APTES). Exposure to near-UV irradiation removes the protein-resistant protecting group, and allows protein adsorption onto the resulting aminated surface. The protein resistance was tested using proteins with fluorescent labels and microspectroscopy of two-component structures formed by micro- and nanopatterning and deposition of yellow and green fluorescent proteins (YFP/GFP). Nonspecific adsorption onto regions where the protecting group remained intact was negligible. Multiple component patterns were also formed by near-field methods. Because reading and writing can be decoupled in a near-field microscope, it is possible to carry out sequential patterning steps at a single location involving different proteins. Up to four different proteins were formed into geometric patterns using near-field lithography. Interferometric lithography facilitates the organization of proteins over square cm areas. Two-component patterns consisting of 150 nm streptavidin dots formed within an orthogonal grid of bars of GFP at a period of ca. 500 nm could just be resolved by fluorescence microscopy.

First moves towards photoautotrophic synthetic cells: In vitro study of photosynthetic reaction centre and cytochrome bc1 complex interactions

Following a bottom-up synthetic biology approach it is shown that vesicle-based cell-like systems (shortly "synthetic cells") can be designed and assembled to perform specific function (for biotechnological applications) and for studies in the origin-of-life field. We recently focused on the construction of synthetic cells capable to converting light into chemical energy. Here we first present our approach, which has been realized so far by the reconstitution of photosynthetic reaction centre in the membrane of giant lipid vesicles. Next, the details of our ongoing research program are presented. It involves the use of the reaction centre, the coenzyme Q-cytochrome c oxidoreductase, and the ATP synthase for creating an autonomous synthetic cell. We show experimental results on the chemistry of the first two proteins showing that they can efficiently sustain light-driven chemical oscillations. Moreover, the cyclic pattern has been reproduced in silico by a minimal kinetic model.

Lateral Segregation of Photosystem I in Cyanobacterial Thylakoids

Photosystem I (PSI) is the dominant photosystem in cyanobacteria and it plays a pivotal role in cyanobacterial metabolism. Despite its biological importance, the native organization of PSI in cyanobacterial thylakoid membranes is poorly understood. Here, we use atomic force microscopy (AFM) to show that ordered, extensive macromolecular arrays of PSI complexes are present in thylakoids from Thermosynechococcus elongatus, Synechococcus sp PCC 7002, and Synechocystis sp PCC 6803. Hyperspectral confocal fluorescence microscopy and three-dimensional structured illumination microscopy of Synechocystis sp PCC 6803 cells visualize PSI domains within the context of the complete thylakoid system. Crystallographic and AFM data were used to build a structural model of a membrane landscape comprising 96 PSI trimers and 27,648 chlorophyll a molecules. Rather than facilitating intertrimer energy transfer, the close associations between PSI primarily maximize packing efficiency; short-range interactions with Complex I and cytochrome b6f are excluded from these regions of the membrane, so PSI turnover is sustained by long-distance diffusion of the electron donors at the membrane surface. Elsewhere, PSI-photosystem II contact zones provide sites for docking phycobilisomes and the formation of megacomplexes. PSI-enriched domains in cyanobacteria might foreshadow the partitioning of PSI into stromal lamellae in plants, similarly sustained by long-distance diffusion of electron carriers.

Challenges of Integrating Stochastic Dynamics and Cryo-Electron Tomograms in Whole-Cell Simulations

Cryo-electron tomography (cryo-ET) has rapidly emerged as a powerful tool to investigate the internal, three-dimensional spatial organization of the cell. In parallel, the GPU-based technology to perform spatially resolved stochastic simulations of whole cells has arisen, allowing the simulation of complex biochemical networks over cell cycle time scales using data taken from -omics, single molecule experiments, and in vitro kinetics. By using real cell geometry derived from cryo-ET data, we have the opportunity to imbue these highly detailed structural data-frozen in time-with realistic biochemical dynamics and investigate how cell structure affects the behavior of the embedded chemical reaction network. Here we present two examples to illustrate the challenges and techniques involved in integrating structural data into stochastic simulations. First, a tomographic reconstruction of Saccharomyces cerevisiae is used to construct the geometry of an entire cell through which a simple stochastic model of an inducible genetic switch is studied. Second, a tomogram of the nuclear periphery in a HeLa cell is converted directly to the simulation geometry through which we study the effects of cellular substructure on the stochastic dynamics of gene repression. These simple chemical models allow us to illustrate how to build whole-cell simulations using cryo-ET derived geometry and the challenges involved in such a process.

Determination of Cell Doubling Times from the Return-on-Investment Time of Photosynthetic Vesicles Based on Atomic Detail Structural Models

Cell doubling times of the purple bacterium Rhodobacter sphaeroides during photosynthetic growth are determined experimentally and computationally as a function of illumination. For this purpose, energy conversion processes in an intracytoplasmic membrane vesicle, the chromatophore, are described based on an atomic detail structural model. The cell doubling time and its illumination dependence are computed in terms of the return-on-investment (ROI) time of the chromatophore, determined computationally from the ATP production rate, and the mass ratio of chromatophores in the cell, determined experimentally from whole cell absorbance spectra. The ROI time is defined as the time it takes to produce enough ATP to pay for the construction of another chromatophore. The ROI time of the low light-growth chromatophore is 4.5-2.6 h for a typical illumination range of 10-100 μmol photons m^-2 s^-1, respectively, with corresponding cell doubling times of 8.2-3.9 h. When energy expenditure is considered as a currency, the benefit-to-cost ratio computed for the chromatophore as an energy harvesting device is 2-8 times greater than for photovoltaic and fossil fuel-based energy solutions and the corresponding ROI times are approximately 3-4 orders of magnitude shorter for the chromatophore than for synthetic systems.

The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems

Understanding the dynamics of biomolecules is the key to understanding their biological activities. Computational methods ranging from all-atom molecular dynamics simulations to coarse-grained normal-mode analyses based on simplified elastic networks provide a general framework to studying these dynamics. Despite recent successes in studying very large systems with up to a 100,000,000 atoms, those methods are currently limited to studying small- to medium-sized molecular systems due to computational limitations. One solution to circumvent these limitations is to reduce the size of the system under study. In this paper, we argue that coarse-graining, the standard approach to such size reduction, must define a hierarchy of models of decreasing sizes that are consistent with each other, i.e., that each model contains the information of the dynamics of its predecessor. We propose a new method, Decimate, for generating such a hierarchy within the context of elastic networks for normal-mode analysis. This method is based on the concept of the renormalization group developed in statistical physics. We highlight the details of its implementation, with a special focus on its scalability to large systems of up to millions of atoms. We illustrate its application on two large systems, the capsid of a virus and the ribosome translation complex. We show that highly decimated representations of those systems, containing down to 1% of their original number of atoms, still capture qualitatively and quantitatively their dynamics. Decimate is available as an OpenSource resource.