Determination of thermodynamic and kinetic properties of biomolecules by mass spectrometry

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Weight average approaches for predicting dynamical properties of biomolecules

Recent advances in atomistic molecular dynamics (MD) simulations of biomolecules allow us to explore their conformational spaces widely, observing large-scale conformational fluctuations or transitions between distinct structures. To reproduce or refine experimental data using MD simulations, structure ensembles, which are characterized by multiple structures and their statistical weights on the rugged free-energy landscapes, are often used. Here, we summarize weight average approaches for various experimental measurements. Weight average approaches are now applied to hybrid quantum mechanics/molecular mechanics MD simulations to predict fast vibrational motions in a protein with a high accuracy for better understanding of molecular functions from atomic structures.     

Poisson-Boltzmann continuum-solvation models: applications to pH-dependent properties of biomolecules

All molecules can be viewed as either discrete or continuous assemblies of electric charges, and electrostatics plays a major role in intermolecular and intramolecular interactions. Moreover, charge distribution within molecules may fluctuate due to the presence of ionizable groups capable of exchanging protons with the environment, leading to pH-dependence of phenomena involving such molecules. Electrostatic aspects of complex shapes and environments of biological molecules, in vitro and in vivo, are relatively well amenable to treatment by Poisson-Boltzmann models, which are attractive in that they possess a clear physical meaning, and can be readily solved by several mathematically sound methods. Here we describe applications of these models to obtain valuable insights into some biologically important pH-dependent properties of biomolecules, such as stability, binding of ligands (including potential drugs), enzymatic activity, conformational transitions, membrane transport and viral entry.     

Emergent properties of conspecific attraction in fragmented landscapes

Attraction to conspecifics may have wide-ranging implications for habitat selection and metapopulation theory, yet little is known about the process of attraction and its effects relative to other habitat selection strategies. Using individual-based simulations, I investigated the emergent properties of conspecific attraction during habitat selection on survival, fecundity, short-term fitness (survival x fecundity), and distributions in fragmented landscapes. I simulated conspecific attraction during searching and settlement decisions and compared attraction with random, habitat-based (searching for the presence of habitat), and habitat quality sampling strategies (searching for and settling in high-quality habitat). Conspecific attraction during searching or settlement decisions had different consequences for animals: attraction while searching increased survival by decreasing time spent in nonsuitable habitat, whereas attraction during settlement increased fecundity by aggregating animals in high-quality habitats. Habitat-based sampling did not improve fitness over attraction, but directly sampling habitat quality resulted in the highest short-term fitness among strategies. These results suggest that attraction can improve fitness when animals cannot directly assess habitat quality. Interestingly, conspecific attraction influenced distributions by generating patch size effects and weak edge effects, highlighting that attraction is one potential, yet previously unappreciated, mechanism to explain the widespread patterns of animal sensitivity to habitat fragmentation.     

Energy landscapes of rotary DNA origami devices determined by fluorescence particle tracking

Biomolecular nanomechanical devices are of great interest as tools for the processing and manipulation of molecules, thereby mimicking the function of nature’s enzymes. DNA nanotechnology provides the capability to build molecular analogs of mechanical machine elements such as joints and hinges via sequence-programmable self-assembly, which are otherwise known from traditional mechanical engineering. Relative to their size, these molecular machine elements typically do not reach the same relative precision and reproducibility that we know from their macroscopic counterparts; however, as they are scaled down to molecular sizes, physical effects typically not considered by mechanical engineers such as Brownian motion, intramolecular forces, and the molecular roughness of the devices begin to dominate their behavior. In order to investigate the effect of different design choices on the roughness of the mechanical energy landscapes of DNA nanodevices in greater detail, we here study an exemplary DNA origami-based structure, a modularly designed rotor-stator arrangement, which resembles a rotatable nanorobotic arm. Using fluorescence tracking microscopy, we follow the motion of individual rotors and record their corresponding energy landscapes. We then utilize the modular construction of the device to exchange its constituent parts individually and systematically test the effect of different design variants on the movement patterns. This allows us to identify the design parameters that most strongly affect the shape of the energy landscapes of the systems. Taking into account these insights, we are able to create devices with significantly flatter energy landscapes, which translates to mechanical nanodevices with improved performance and behaviors more closely resembling those of their macroscopic counterparts.     

Protein conformational landscapes: Energy minimization and clustering of a long molecular dynamics trajectory

Using energy minimization and cluster analysis, we have analyzed a 1020 ps molecular dynamics trajectory of solvated bovine pancreatic trypsin inhibitor. Elucidation of conformational sub states in this way both illustrates the degree of conformational convergence in the simulation and reduces the structural data to a tractable subset. The relative movement of structures upon energy minimization was used to estimate the sizes of features on the protein potential energy surface. The structures were analyzed using their pairwise root-mean-square C_α deviations, which gave a global measure of conformational changes that would not be apparent by monitoring single degrees of freedom. At time scales of 0.1 ps, energy minimization detected sharp transitions between energy minima separated by 0.1 Å rms deviation. Larger conformational clusters containing these smaller minima and separated by 0.25 Å were seen at 1 ps time scales. Both of these small features of the conformational landscape were characterized by movements in loop regions associated with small, correlated backbone dihedral angle shifts. On a nanosecond time scale, the main features of the protein energy landscape were clusters separated by over 0.7 Å rms deviation, with only seven of these sub states visited over the 1 ns trajectory. These substates, discernible both before and after energy minimization, differ mainly in a monotonic pivot of the loop residues 11–18 over the course of the simulation. This loop contains lysine 17, which specifically binds to trypsin in the active site. The trajectory did not return to previously visited clusters, indicating that this trajectory has not been shown to have completely sampled the conformational substates available to it. Because the apparent convergence to a single region of conformation space depends on both the time scale of observation and the size of the conformational features examined, convergence must be operationally defined within the context of the simulation. © 1995 Wiley-Liss, Inc.     

Energy landscapes shape microbial communities in hydrothermal systems on the Arctic Mid-Ocean Ridge

Methods developed in geochemical modelling combined with recent advances in molecular microbial ecology provide new opportunities to explore how microbial communities are shaped by their chemical surroundings. Here, we present a framework for analyses of how chemical energy availability shape chemotrophic microbial communities in hydrothermal systems through an investigation of two geochemically different basalt-hosted hydrothermal systems on the Arctic Mid-Ocean Ridge: the Soria Moria Vent field (SMVF) and the Loki''s Castle Vent Field (LCVF). Chemical energy landscapes were evaluated through modelling of the Gibbs energy from selected redox reactions under different mixing ratios between seawater and hydrothermal fluids. Our models indicate that the sediment-influenced LCVF has a much higher potential for both anaerobic and aerobic methane oxidation, as well as aerobic ammonium and hydrogen oxidation, than the SMVF. The modelled energy landscapes were used to develop microbial community composition models, which were compared with community compositions in environmental samples inside or on the exterior of hydrothermal chimneys, as assessed by pyrosequencing of partial 16S rRNA genes. We show that modelled microbial communities based solely on thermodynamic considerations can have a high predictive power and provide a framework for analyses of the link between energy availability and microbial community composition.     

Comparative analyses of biomolecules

Systematic examination of the gene encoding CYP2B6, a human cytochrome P450, has characterized genetic polymorphisms that might account for its variability in expression and function between individuals.     

Transgenic modifications of silkworms as a means to obtain therapeutic biomolecules and protein fibers with exceptional properties

Transgenic modification of Bombyx mori silkworms is a benign approach for the production of silk fibers with extraordinary properties and also to generate therapeutic proteins and other biomolecules for various applications. Silk fibers with fluorescence lasting more than a year, natural protein fibers with strength and toughness exceeding that of spider silk, proteins and therapeutic biomolecules with exceptional properties have been developed using transgenic technology. The transgenic modifications have been done primarily by modifying the silk sericin and fibroin genes and also the silk producing glands. Although the genetic modifications were typically performed using the sericin 1 and other genes, newer techniques such as CRISPR/Cas9 have enabled successful modifications of both the fibroin H-chain and L-chain. Such modifications have led to the production of therapeutic proteins and other biomolecules in reasonable quantities at affordable costs for tissue engineering and other medical applications. Transgenically modified silkworms also have distinct and long-lasting fluorescence useful for bioimaging applications. This review presents an overview of the transgenic techniques for modifications of B. mori silkworms and the properties obtained due to such modifications with particular focus on production of growth factors, fluorescent proteins, and high performance protein fibers.     

Diffraction imaging of single particles and biomolecules

Theory predicts that with a very short and very intense X-ray pulse, the image of a single diffraction pattern may be recorded from a large macromolecule, a virus, or a nanocluster of proteins without the need for a crystal. A three-dimensional data set can be assembled from such images when many copies of the molecule are exposed to the beam one by one in random orientations. We outline a method for structure reconstruction from such a data set in which no independent information is available about the orientation of the images. The basic requirement for reconstruction and/or signal averaging is the ability to tell whether two noisy diffraction patterns represent the same view of the sample or two different views. With this knowledge, averaging techniques can be used to enhance the signal and extend the resolution in a redundant data set. Based on statistical properties of the diffraction pattern, we present an analytical solution to the classification problem. The solution connects the number of incident X-ray photons with the particle size and the achievable resolution. The results are surprising in that they show that classification can be done with less than one photon per pixel in the limiting resolution shell, assuming Poisson-type photon noise in the image. The results can also be used to provide criteria for improvements in other image classification procedures, e.g., those used in electron tomography or diffraction.     

High pressure simulations of biomolecules

Pressure is a thermodynamic variable which is particularly suitable for exploration of the properties of biological macromolecules. For proteins, in particular, denaturation induced by pressure is different from that induced by temperature or denaturants. The response of proteins to pressure changes can provide information on properties of their native and non-native states. This review focuses on molecular dynamics studies of the effect of pressure on detailed atomic models of proteins. It also reports on other theoretical approaches, such as Monte Carlo simulations, which have been used to study simplified models. Another purpose of this review is to try to point out potential future studies that may be both interesting and feasible, with constantly increasing computing power.     

Pressure-temperature phase diagrams of biomolecules

The pressure-temperature phase diagram of various biomolecules is reviewed. Special attention is focused on the elliptic phase diagram of proteins. The phenomenological thermodynamic theory describing this diagram explains the heat, cold and pressure denaturations in a unified picture. The limitations and possible developments of this theory are discussed as well. It is pointed out that a more complex diagram can be obtained when the intermolecular interactions are also taken into account. In this case metastable states appear on the pressure-temperature (p-T) diagram due to intermolecular interactions. Pressure-temperature phase diagrams of other biopolymers are also discussed. While the p-T diagrams of helix-coil transition of nucleic acids and of gel-liquid crystal transition of lipid bilayers are non-elliptical, those of gelatinization of starch and of phase separation of some synthetic polymers show an elliptic profile, similar to that of proteins. Finally, the p-T diagram of bacterial inactivation is shown to be elliptic. From the point of view of basic science, this fact shows that the key factor of inactivation should be the protein type, and from the viewpoint of practical applications, it serves as the theoretical basis of pressure treatment of biosystems.     

Jahn-Teller effect in biomolecules

The role played by the Jahn-Teller effect for a correct interpretation of the spectroscopic and structural data of some biological systems is stressed and a short review of the investigations so far performed is presented. Afterwards, on the basis of the angular overlap model, several expressions are given for the coupling constants of the transition metal ions, which mainly occur in biomolecules (Fe, Co, Cu, Mo) in order to have an approximate evaluation of the Jahn-Teller effect magnitude. The above expressions are then applied to obtain the vibronic coupling constants of the deoxygenated hemoglobin and myoglobin. The results obtained are compared with the predictions previously made by the author on these systems.