Annual Review of Biophysics最新文献

Synthetic biology seeks to probe fundamental aspects of biological form and function by construction [i.e., (re)synthesis] rather than deconstruction (analysis). In this sense, biological sciences now follow the lead given by the chemical sciences. Synthesis can complement analytic studies but also allows novel approaches to answering fundamental biological questions and opens up vast opportunities for the exploitation of biological processes to provide solutions for global problems. In this review, we explore aspects of this synthesis paradigm as applied to the chemistry and function of nucleic acids in biological systems and beyond, specifically, in genome resynthesis, synthetic genetics (i.e., the expansion of the genetic alphabet, of the genetic code, and of the chemical make-up of genetic systems), and the elaboration of orthogonal biosystems and components.

The author first describes his childhood in the South and the ways in which it fostered the values he has espoused throughout his life, his development of a keen fascination with science, and the influences that supported his progress toward higher education. His experiences in ROTC as a student, followed by two years in the US Army during the Vietnam War, honed his leadership skills. The bulk of the autobiography is a chronological journey through his scientific career, beginning with arrival at the University of California, Irvine in 1972, with an emphasis on the postdoctoral students and colleagues who have contributed substantially to each phase of his lab's progress. White's fundamental findings played a key role in the development of membrane biophysics, helping establish it as fertile ground for research. A story gradually unfolds that reveals the deeply collaborative and painstakingly executed work necessary for a successful career in science.

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

The advent of biotechnology has enabled metabolic engineers to assemble heterologous pathways in cells to produce a variety of products of industrial relevance, often in a sustainable way. However, many pathways face challenges of low product yield. These pathways often suffer from issues that are difficult to optimize, such as low pathway flux and off-target pathway consumption of intermediates. These issues are exacerbated by the need to balance pathway flux with the health of the cell, particularly when a toxic intermediate builds up. Nature faces similar challenges and has evolved spatial organization strategies to increase metabolic pathway flux and efficiency. Inspired by these strategies, bioengineers have developed clever strategies to mimic spatial organization in nature. This review explores the use of spatial organization strategies, including protein scaffolding and protein encapsulation inside of proteinaceous shells, toward overcoming bottlenecks in metabolic engineering efforts.

The Hsp40, Hsp70, and Hsp90 chaperone families are ancient, highly conserved, and critical to cellular protein homeostasis. Hsp40 chaperones can transfer their protein clients to Hsp70, and Hsp70 can transfer clients to Hsp90, but the functional benefits of these transfers are unclear. Recent structural and mechanistic work has opened up the possibility of uncovering how Hsp40, Hsp70, and Hsp90 work together as unified system. In this review, we compile mechanistic data on the ER J-domain protein 3 (ERdj3) (an Hsp40), BiP (an Hsp70), and Grp94 (an Hsp90) chaperones within the endoplasmic reticulum; what is known about how these chaperones work together; and gaps in this understanding. Using calculations, we examine how client transfer could impact the solubilization of aggregates, the folding of soluble proteins, and the triage decisions by which proteins are targeted for degradation. The proposed roles of client transfer among Hsp40-Hsp70-Hsp90 chaperones are new hypotheses, and we discuss potential experimental tests of these ideas.

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.