Annual Review of Biophysics最新文献

Low-molecular-weight oligomers formed from amyloidogenic peptides and proteins have been identified as key cytotoxins across a range of neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease. Developing therapeutic strategies that target oligomers is therefore emerging as a promising approach for combating protein misfolding diseases. As such, there is a great need to understand the fundamental properties, dynamics, and mechanisms associated with oligomer formation. In this review, we discuss how chemical kinetics provides a powerful tool for studying these systems. We review the chemical kinetics approach to determining the underlying molecular pathways of protein aggregation and discuss its applications to oligomer formation and dynamics. We discuss how this approach can reveal detailed mechanisms of primary and secondary oligomer formation, including the role of interfaces in these processes. We further use this framework to describe the processes of oligomer conversion and dissociation, and highlight the distinction between on-pathway and off-pathway oligomers. Furthermore, we showcase on the basis of experimental data the diversity of pathways leading to oligomer formation in various in vitro and in silico systems. Finally, using the lens of the chemical kinetics framework, we look at the current oligomer inhibitor strategies both in vitro and in vivo.

The cytoskeleton comprises networks of different biopolymers, which serve various cellular functions. To accomplish these tasks, their mechanical properties are of particular importance. Understanding them requires detailed knowledge of the mechanical properties of the individual filaments that make up these networks, in particular, microtubules, actin filaments, and intermediate filaments. Far from being homogeneous beams, cytoskeletal filaments have complex mechanical properties, which are directly related to the specific structural arrangement of their subunits. They are also versatile, as the filaments' mechanics and biochemistry are tightly coupled, and their properties can vary with the cellular context. In this review, we summarize decades of research on cytoskeletal filament mechanics, highlighting their most salient features and discussing recent insights from this active field of research.

Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components.

The growing appreciation of native state conformational fluctuations mediating protein function calls for critical reevaluation of protein evolution and adaptation. If proteins are ensembles, does nature select solely for ground state structure, or are conformational equilibria between functional states also conserved? If so, what is the mechanism and how can it be measured? Addressing these fundamental questions, we review our investigation into the role of local unfolding fluctuations in the native state ensembles of proteins. We describe the functional importance of these ubiquitous fluctuations, as revealed through studies of adenylate kinase. We then summarize elucidation of thermodynamic organizing principles, which culminate in a quantitative probe for evolutionary conservation of protein energetics. Finally, we show that these principles are predictive of sequence compatibility for multiple folds, providing a unique thermodynamic perspective on metamorphic proteins. These research areas demonstrate that the locally unfolded ensemble is an emerging, important mechanism of protein evolution.

Like their prokaryotic counterparts, eukaryotic transcription factors must recognize specific DNA sites, search for them efficiently, and bind to them to help recruit or block the transcription machinery. For eukaryotic factors, however, the genetic signals are extremely complex and scattered over vast, multichromosome genomes, while the DNA interplay occurs in a varying landscape defined by chromatin remodeling events and epigenetic modifications. Eukaryotic factors are rich in intrinsically disordered regions and are also distinct in their recognition of short DNA motifs and utilization of open DNA interaction interfaces as ways to gain access to DNA on nucleosomes. Recent findings are revealing the profound, unforeseen implications of such characteristics for the mechanisms of DNA interplay. In this review we discuss these implications and how they are shaping the eukaryotic transcription control paradigm into one of promiscuous signal recognition, highly dynamic interactions, heterogeneous DNA scanning, and multiprong conformational control.

Microbial cell factories have been developed to produce various compounds in a sustainable and economically viable manner. The yeast Saccharomyces cerevisiae has been used as a platform cell factory in industrial biotechnology with numerous advantages, including ease of operation, rapid growth, and tolerance for various industrial stressors. Advances in synthetic biology and metabolic models have accelerated the design-build-test-learn cycle in metabolic engineering, significantly facilitating the development of yeast strains with complex phenotypes, including the redirection of metabolic fluxes to desired products, the expansion of the spectrum of usable substrates, and the improvement of the physiological properties of strain. Strains with enhanced titer, rate, and yield are now competing with traditional petroleum-based industrial approaches. This review highlights recent advances and perspectives in the metabolic engineering of yeasts for the production of a variety of compounds, including fuels, chemicals, proteins, and peptides, as well as advancements in synthetic biology tools and mathematical modeling.

In this article I review mechanisms that underpin epigenetic inheritance of CpG methylation and histone H3 lysine 9 methylation (H3K9me) in chromatin in fungi and mammals. CpG methylation can be faithfully inherited epigenetically at some sites for a lifetime in vertebrates and, remarkably, can be propagated for millions of years in some fungal lineages. Transmission of methylation patterns requires maintenance-type DNA methyltransferases (DNMTs) that recognize hemimethylated CpG DNA produced by replication. DNMT1 is the maintenance enzyme in vertebrates; we recently identified DNMT5 as an ATP-dependent CpG maintenance enzyme found in fungi and protists. In vivo, CpG methylation is coupled to H3K9me. H3K9me is itself reestablished after replication via local histone H3-H4 tetramer recycling involving mobile and nonmobile chaperones, de novo nucleosome assembly, and read-write mechanisms that modify naive nucleosomes. Additional proteins recognize hemimethylated CpG or fully methylated CpG-containing motifs and enhance restoration of methylation by recruiting and/or activating the maintenance methylase.

A complete understanding of protein function and dynamics requires the characterization of the multiple thermodynamic states, including the denatured state ensemble (DSE). Whereas residual structure in the DSE (as well as in partially folded states) is pertinent in many biological contexts, here we are interested in how such structure affects protein thermodynamics. We examine issues related to chain collapse in light of new developments, focusing on potential complications arising from differences in the DSE's properties under various conditions. Despite some variability in the degree of collapse and structure in the DSE, stability measurements are remarkably consistent between two standard methods, calorimetry and chemical denaturation, as well as with hydrogen-deuterium exchange. This robustness is due in part to the DSEs obtained with different perturbations being thermodynamically equivalent and hence able to serve as a common reference state. An examination of the properties of the DSE points to it as being a highly expanded ensemble with minimal amounts of stable hydrogen bonded structure. These two features are likely to be critical in the broad and successful application of thermodynamics to protein folding. Our review concludes with a discussion of the impact of these findings on folding mechanisms and pathways.

Double electron-electron resonance (DEER) combined with site-directed spin labeling can provide distance distributions between selected protein residues to investigate protein structure and conformational heterogeneity. The utilization of the full quantitative information contained in DEER data requires effective protein and spin label modeling methods. Here, we review the application of DEER data to protein modeling. First, we discuss the significance of spin label modeling for accurate extraction of protein structural information and review the most popular label modeling methods. Next, we review several important aspects of protein modeling with DEER, including site selection, how DEER restraints are applied, common artifacts, and the unique potential of DEER data for modeling structural ensembles and conformational landscapes. Finally, we discuss common applications of protein modeling with DEER data and provide an outlook.

The rate of protein synthesis is slower than many folding reactions and varies depending on the synonymous codons encoding the protein sequence. Synonymous codon substitutions thus have the potential to regulate cotranslational protein folding mechanisms, and a growing number of proteins have been identified with folding mechanisms sensitive to codon usage. Typically, these proteins have complex folding pathways and kinetically stable native structures. Kinetically stable proteins may fold only once over their lifetime, and thus, codon-mediated regulation of the pioneer round of protein folding can have a lasting impact. Supporting an important role for codon usage in folding, conserved patterns of codon usage appear in homologous gene families, hinting at selection. Despite these exciting developments, there remains few experimental methods capable of quantifying translation elongation rates and cotranslational folding mechanisms in the cell, which challenges the development of a predictive understanding of how biology uses codons to regulate protein folding.