Annual Review of Biophysics最新文献

Proteins guide the flows of information, energy, and matter that make life possible by accelerating transport and chemical reactions, by allosterically modulating these reactions, and by forming dynamic supramolecular assemblies. In these roles, conformational change underlies functional transitions. Time-resolved X-ray diffraction methods characterize these transitions either by directly triggering sequences of functionally important motions or, more broadly, by capturing the motions of which proteins are capable. To date, most successful have been experiments in which conformational change is triggered in light-dependent proteins. In this review, I emphasize emerging techniques that probe the dynamic basis of function in proteins lacking natively light-dependent transitions and speculate about extensions and further possibilities. In addition, I review how the weaker and more distributed signals in these data push the limits of the capabilities of analytical methods. Taken together, these new methods are beginning to establish a powerful paradigm for the study of the physics of protein function.

Large biomolecular systems are at the heart of many essential cellular processes. The dynamics and energetics of an increasing number of these systems are being studied by computer simulations. Pushing the limits of length- and timescales that can be accessed by current hard- and software has expanded the ability to describe biomolecules at different levels of detail. We focus in this review on the ribosome, which exemplifies the close interplay between experiment and various simulation approaches, as a particularly challenging and prototypic nanomachine that is pivotal to cellular biology due to its central role in translation. We sketch widely used simulation methods and demonstrate how the combination of simulations and experiments advances our understanding of the function of the translation apparatus based on fundamental physics.

Faithful translation of messenger RNA (mRNA) into protein is essential to maintain protein homeostasis in the cell. Spontaneous translation errors are very rare due to stringent selection of cognate aminoacyl transfer RNAs (tRNAs) and the tight control of the mRNA reading frame by the ribosome. Recoding events, such as stop codon readthrough, frameshifting, and translational bypassing, reprogram the ribosome to make intentional mistakes and produce alternative proteins from the same mRNA. The hallmark of recoding is the change of ribosome dynamics. The signals for recoding are built into the mRNA, but their reading depends on the genetic makeup of the cell, resulting in cell-specific changes in expression programs. In this review, I discuss the mechanisms of canonical decoding and tRNA-mRNA translocation; describe alternative pathways leading to recoding; and identify the links among mRNA signals, ribosome dynamics, and recoding.

Diffusion is a pervasive process present in a broad spectrum of cellular reactions. Its mathematical description has existed for nearly two centuries and permits the construction of simple rules for evaluating the characteristic timescales of diffusive processes and some of their determinants. Although the term diffusion originally referred to random motions in three-dimensional (3D) media, several biological diffusion processes in lower dimensions have been reported. One-dimensional (1D) diffusions have been reported, for example, for translocations of various proteins along DNA or protein (e.g., microtubule) lattices and translation of helical peptides along the coiled-coil interface. Two-dimensional (2D) diffusion has been shown for dynamics of proteins along membranes. The microscopic mechanisms of these 1-3D diffusions may vary significantly depending on the nature of the diffusing molecules, the substrate, and the interactions between them. In this review, we highlight some key examples of 1-3D biomolecular diffusion processes and illustrate the roles that electrostatic interactions and intrinsic disorder may play in modulating these processes.

Single particle cryo-electron microscopy (cryo-EM) has matured into a robust method for the determination of biological macromolecule structures in the past decade, complementing X-ray crystallography and nuclear magnetic resonance. Constant methodological improvements in both cryo-EM hardware and image processing software continue to contribute to an exponential growth in the number of structures solved annually. In this review, we provide a historical view of the many steps that were required to make cryo-EM a successful method for the determination of high-resolution protein complex structures. We further discuss aspects of cryo-EM methodology that are the greatest pitfalls challenging successful structure determination to date. Lastly, we highlight and propose potential future developments that would improve the method even further in the near future.

My accidental encounter with protein hydrogen exchange (HX) at its very beginning and its continued development through my scientific career have led us to a series of advances in HX measurement, interpretation, and cutting edge biophysical applications. After some thoughts about how life brought me there, I take the opportunity to reflect on our early studies of allosteric structure and energy change in hemoglobin, the still-current protein folding problem, and our most recent forward-looking studies on protein machines.

Cooperativity (homotropic allostery) is the primary mechanism by which evolution steepens the binding curves of biomolecular receptors to produce more responsive input-output behavior in biomolecular systems. Motivated by the ubiquity with which nature employs this effect, over the past 15 years we, together with other groups, have engineered this mechanism into several otherwise noncooperative receptors. These efforts largely aimed to improve the utility of such receptors in artificial biotechnologies, such as synthetic biology and biosensors, but they have also provided the first quantitative, experimental tests of longstanding ideas about the mechanisms underlying cooperativity. In this article, we review the literature on the design of this effect, paying particular attention to the design strategies involved, the extent to which each can be rationally applied to (and optimized for) new receptors, and what each teaches us about the origins and optimization of this important phenomenon.

Super-resolution fluorescence microscopy allows the investigation of cellular structures at nanoscale resolution using light. Current developments in super-resolution microscopy have focused on reliable quantification of the underlying biological data. In this review, we first describe the basic principles of super-resolution microscopy techniques such as stimulated emission depletion (STED) microscopy and single-molecule localization microscopy (SMLM), and then give a broad overview of methodological developments to quantify super-resolution data, particularly those geared toward SMLM data. We cover commonly used techniques such as spatial point pattern analysis, colocalization, and protein copy number quantification but also describe more advanced techniques such as structural modeling, single-particle tracking, and biosensing. Finally, we provide an outlook on exciting new research directions to which quantitative super-resolution microscopy might be applied.

Recent advances in cryo-electron microscopy have marked only the beginning of the potential of this technique. To bring structure into cell biology, the modality of cryo-electron tomography has fast developed into a bona fide in situ structural biology technique where structures are determined in their native environment, the cell. Nearly every step of the cryo-focused ion beam-assisted electron tomography (cryo-FIB-ET) workflow has been improved upon in the past decade, since the first windows were carved into cells, unveiling macromolecular networks in near-native conditions. By bridging structural and cell biology, cryo-FIB-ET is advancing our understanding of structure-function relationships in their native environment and becoming a tool for discovering new biology.

Efforts to combine theory and experiment to advance our knowledge of molecular processes relevant to biophysics have been considerably enhanced by the contribution of statistical-mechanics simulations. Key to the understanding of such molecular processes is the underlying free-energy change. Being able to accurately predict this change from first principles represents an appealing prospect. Over the past decades, the synergy between steadily growing computational resources and unrelenting methodological developments has brought free-energy calculations into the arsenal of tools commonly utilized to tackle important questions that experiment alone has left unresolved. The continued emergence of new options to determine free energies has also bred confusion amid the community of users, who may find it difficult to choose the best-suited algorithm to address the problem at hand. In an attempt to clarify the current landscape, this review recounts how the field has been shaped and how the broad gamut of methods available today is rooted in a few foundational principles laid down many years ago.Three examples of molecular processes central to biophysics illustrate where free-energy calculations stand and what are the conceptual and practical obstacles that we must overcome to increase their predictive power.