Annual Review of Biophysics最新文献

DNA origami enables the bottom-up construction of chemically addressable, nanoscale objects with user-defined shapes and tailored functionalities. As such, not only can DNA origami objects be used to improve existing experimental methods in biophysics, but they also open up completely new avenues of exploration. In this review, we discuss basic biophysical concepts that are relevant for prospective DNA origami users. We summarize biochemical strategies for interfacing DNA origami with biomolecules of interest. We describe various applications of DNA origami, emphasizing the added value or new biophysical insights that can be generated: rulers and positioning devices, force measurement and force application devices, alignment supports for structural analysis for biomolecules in cryogenic electron microscopy and nuclear magnetic resonance, probes for manipulating and interacting with lipid membranes, and programmable nanopores. We conclude with some thoughts on so-far little explored opportunities for using DNA origami in more complex environments such as the cell or even organisms.

Notch signaling is a conserved system of communication between adjacent cells, influencing numerous cell fate decisions in the development of multicellular organisms. Aberrant signaling is also implicated in many human pathologies. At its core, Notch has a mechanotransduction module that decodes receptor-ligand engagement at the cell surface under force to permit proteolytic cleavage of the receptor, leading to the release of the Notch intracellular domain (NICD). NICD enters the nucleus and acts as a transcriptional effector to regulate expression of Notch-responsive genes. In this article, we review and integrate current understanding of the detailed molecular basis for Notch signal transduction, highlighting quantitative, structural, and dynamic features of this developmentally central signaling mechanism. We discuss the implications of this mechanistic understanding for the functionality of the signaling pathway in different molecular and cellular contexts.

Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

Sampling and genomic efforts over the past decade have revealed an enormous quantity and diversity of life in Earth's extreme environments. This new knowledge of life on Earth poses the challenge of understandingits molecular basis in such inhospitable conditions, given that such conditions lead to loss of structure and of function in biomolecules from mesophiles. In this review, we discuss the physicochemical properties of extreme environments. We present the state of recent progress in extreme environmental genomics. We then present an overview of our current understanding of the biomolecular adaptation to extreme conditions. As our current and future understanding of biomolecular structure-function relationships in extremophiles requires methodologies adapted to extremes of pressure, temperature, and chemical composition, advances in instrumentation for probing biophysical properties under extreme conditions are presented. Finally, we briefly discuss possible future directions in extreme biophysics.

Two groundbreaking papers published in 1954 laid out the theory of the mechanism of muscle contraction based on force-generating interactions between myofilaments in the sarcomere that cause filaments to slide past one another during muscle contraction. The succeeding decades of research in muscle physiology have revealed a unifying interest: to understand the multiscale processes-from atom to organ-that govern muscle function. Such an understanding would have profound consequences for a vast array of applications, from developing new biomimetic technologies to treating heart disease. However, connecting structural and functional properties that are relevant at one spatiotemporal scale to those that are relevant at other scales remains a great challenge. Through a lens of multiscale dynamics, we review in this article current and historical research in muscle physiology sparked by the sliding filament theory.

G-quadruplexes have raised considerable interest during the past years for the development of therapies against cancer. These noncanonical structures of DNA may be found in telomeres and/or oncogene promoters, and it has been observed that the stabilization of such G-quadruplexes may disturb tumor cell growth. Nevertheless, the mechanisms leading to folding and stabilization of these G-quadruplexes are still not well established, and they are the focus of much current work in this field. In seminal works, stabilization was observed to be produced by cations. However, subsequent studies showed that different kinds of small molecules, from planar and nonplanar organic molecules to square-planar and octahedral metal complexes, may also lead to the stabilization of G-quadruplexes. Thus, the comprehension and rationalization of the interaction of these small molecules with G-quadruplexes are also important topics of current interest in medical applications. To shed light on the questions arising from the literature on the formation of G-quadruplexes, their stabilization, and their interaction with small molecules, synergies between experimental studies and computational works are needed. In this review, we mainly focus on in silico approaches and provide a broad compilation of different leading studies carried out to date by different computational methods. We divide these methods into twomain categories: (a) classical methods, which allow for long-timescale molecular dynamics simulations and the corresponding analysis of dynamical information, and (b) quantum methods (semiempirical, quantum mechanics/molecular mechanics, and density functional theory methods), which allow for the explicit simulation of the electronic structure of the system but, in general, are not capable of being used in long-timescale molecular dynamics simulations and, therefore, give a more static picture of the relevant processes.

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

Membrane potential (V_mem) is a fundamental biophysical signal present in all cells. V_mem signals range in time from milliseconds to days, and they span lengths from microns to centimeters. V_mem affects many cellular processes, ranging from neurotransmitter release to cell cycle control to tissue patterning. However, existing tools are not suitable for V_mem quantification in many of these areas. In this review, we outline the diverse biology of V_mem, drafting a wish list of features for a V_mem sensing platform. We then use these guidelines to discuss electrode-based and optical platforms for interrogating V_mem. On the one hand, electrode-based strategies exhibit excellent quantification but are most effective in short-term, cellular recordings. On the other hand, optical strategies provide easier access to diverse samples but generally only detect relative changes in V_mem. By combining the respective strengths of these technologies, recent advances in optical quantification of absolute V_mem enable new inquiries into V_mem biology.

Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype-phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure-function landscape and propose practical guidelines for navigating these ecological landscapes.

Giant unilamellar vesicles (GUVs) have gained great popularity as mimicries for cellular membranes. As their sizes are comfortably above the optical resolution limit, and their lipid composition is easily controlled, they are ideal for quantitative light microscopic investigation of dynamic processes in and on membranes. However, reconstitution of functional proteins into the lumen or the GUV membrane itself has proven technically challenging. In recent years, a selection of techniques has been introduced that tremendously improve GUV-assay development and enable the precise investigation of protein-membrane interactions under well-controlled conditions. Moreover, due to these methodological advances, GUVs are considered important candidates as protocells in bottom-up synthetic biology. In this review, we discuss the state of the art of the most important vesicle production and protein encapsulation methods and highlight some key protein systems whose functional reconstitution has advanced the field.