Annual Review of Biophysics - Volume 46, 2017

Electron cryotomography (ECT) can produce three-dimensional images of biological samples such as intact cells in a near-native, frozen-hydrated state to macromolecular resolution (∼4 nm). Because one of its first and most common applications has been to bacterial chemoreceptor arrays, ECT's contributions to this field illustrate well its past, present, and future. While X-ray crystallography and nuclear magnetic resonance spectroscopy have revealed the structures of nearly all the individual components of chemoreceptor arrays, ECT has revealed the mesoscale information about how the components are arranged within cells. Receptors assemble into a universally conserved 12-nm hexagonal lattice linked by CheA/CheW rings. Membrane-bound arrays are single layered; cytoplasmic arrays are double layered. Images of in vitro reconstitutions have led to a model of how arrays assemble, and images of native arrays in different states have shown that the conformational changes associated with signal transduction are subtle, constraining models of activation and system cooperativity. Phase plates, better detectors, and more stable stages promise even higher resolution and broader application in the near future.

Emerging protein design strategies are enabling the creation of diverse, self-assembling supramolecular structures with precision on the atomic scale. The design possibilities include various types of architectures: finite cages or shells, essentially unbounded two-dimensional and three-dimensional arrays (i.e., crystals), and linear or tubular filaments. In nature, structures of those types are generally symmetric, and, accordingly, symmetry provides a powerful guide for developing new design approaches. Recent design studies have produced numerous protein assemblies in close agreement with geometric specifications. For certain design approaches, a complete list of allowable symmetry combinations that can be used for construction has been articulated, opening a path to a rich diversity of geometrically defined protein materials. Future challenges include improving and elaborating on current strategies and endowing designed protein nanomaterials with properties useful in nanomedicine and material science applications.

The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling—the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes—protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation.

Eukaryotic gene transcription requires the assembly at the promoter of a large preinitiation complex (PIC) that includes RNA polymerase II (Pol II) and the general transcription factors TFIID, TFIIA, TFIIB, TFIIF, TFIIE, and TFIIH. The size and complexity of Pol II, TFIID, and TFIIH have precluded their reconstitution from heterologous systems, and purification relies on scarce endogenous sources. Together with their conformational flexibility and the transient nature of their interactions, these limitations had precluded structural characterization of the PIC. In the last few years, however, progress in cryo–electron microscopy (cryo-EM) has made possible the visualization, at increasingly better resolution, of large PIC assemblies in different functional states. These structures can now be interpreted in near-atomic detail and provide an exciting structural framework for past and future functional studies, giving us unique mechanistic insight into the complex process of transcription initiation.

For decades, rates of protein evolution have been interpreted in terms of the vague concept of functional importance. Slowly evolving proteins or sites within proteins were assumed to be more functionally important and thus subject to stronger selection pressure. More recently, biophysical models of protein evolution, which combine evolutionary theory with protein biophysics, have completely revolutionized our view of the forces that shape sequence divergence. Slowly evolving proteins have been found to evolve slowly because of selection against toxic misfolding and misinteractions, linking their rate of evolution primarily to their abundance. Similarly, most slowly evolving sites in proteins are not directly involved in function, but mutating these sites has a large impact on protein structure and stability. In this article, we review the studies in the emerging field of biophysical protein evolution that have shaped our current understanding of sequence divergence patterns. We also propose future research directions to develop this nascent field.

Whereas protein–ligand binding affinities have long-established prominence, binding rate constants and binding mechanisms have gained increasing attention in recent years. Both new computational methods and new experimental techniques have been developed to characterize the latter properties. It is now realized that binding mechanisms, like binding rate constants, can and should be quantitatively determined. In this review, we summarize studies and synthesize ideas on several topics in the hope of providing a coherent picture of and physical insight into binding kinetics. The topics include microscopic formulation of the kinetic problem and its reduction to simple rate equations; computation of binding rate constants; quantitative determination of binding mechanisms; and elucidation of physical factors that control binding rate constants and mechanisms.

Small RNAs (sRNAs) are central regulators of gene expression in bacteria, controlling target genes posttranscriptionally by base pairing with their mRNAs. sRNAs are involved in many cellular processes and have unique regulatory characteristics. In this review, we discuss the properties of regulation by sRNAs and how it differs from and combines with transcriptional regulation. We describe the global characteristics of the sRNA–target networks in bacteria using graph-theoretic approaches and review the local integration of sRNAs in mixed regulatory circuits, including feed-forward loops and their combinations, feedback loops, and circuits made of an sRNA and another regulator, both derived from the same transcript. Finally, we discuss the competition effects in posttranscriptional regulatory networks that may arise over shared targets, shared regulators, and shared resources and how they may lead to signal propagation across the network.

The ubiquitin proteasome system controls the concentrations of regulatory proteins and removes damaged and misfolded proteins from cells. Proteins are targeted to the protease at the center of this system, the proteasome, by ubiquitin tags, but ubiquitin is also used as a signal in other cellular processes. Specificity is conferred by the size and structure of the ubiquitin tags, which are recognized by receptors associated with the different cellular processes. However, the ubiquitin code remains ambiguous, and the same ubiquitin tag can target different proteins to different fates. After binding substrate protein at the ubiquitin tag, the proteasome initiates degradation at a disordered region in the substrate. The proteasome has pronounced preferences for the initiation site, and its recognition represents a second component of the degradation signal.

Chemokines and their cell surface G protein–coupled receptors are critical for cell migration, not only in many fundamental biological processes but also in inflammatory diseases and cancer. Recent X-ray structures of two chemokines complexed with full-length receptors provided unprecedented insight into the atomic details of chemokine recognition and receptor activation, and computational modeling informed by new experiments leverages these insights to gain understanding of many more receptor:chemokine pairs. In parallel, chemokine receptor structures with small molecules reveal the complicated and diverse structural foundations of small molecule antagonism and allostery, highlight the inherent physicochemical challenges of receptor:chemokine interfaces, and suggest novel epitopes that can be exploited to overcome these challenges. The structures and models promote unique understanding of chemokine receptor biology, including the interpretation of two decades of experimental studies, and will undoubtedly assist future drug discovery endeavors.

Telomerase is an RNA–protein complex that extends the 3′ ends of linear chromosomes, using a unique telomerase reverse transcriptase (TERT) and template in the telomerase RNA (TR), thereby helping to maintain genome integrity. TR assembles with TERT and species-specific proteins, and telomerase function in vivo requires interaction with telomere-associated proteins. Over the past two decades, structures of domains of TR and TERT as well as other telomerase- and telomere-interacting proteins have provided insights into telomerase function. A recently reported 9-Å cryo–electron microscopy map of the Tetrahymena telomerase holoenzyme has provided a framework for understanding how TR, TERT, and other proteins from ciliate as well as vertebrate telomerase fit and function together as well as unexpected insight into telomerase interaction at telomeres. Here we review progress in understanding the structural basis of human and Tetrahymena telomerase activity, assembly, and interactions.

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion–RNA and ligand–RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data–directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

A central goal in biochemistry is to explain the causes of protein sequence, structure, and function. Mainstream approaches seek to rationalize sequence and structure in terms of their effects on function and to identify function's underlying determinants by comparing related proteins to each other. Although productive, both strategies suffer from intrinsic limitations that have left important aspects of many proteins unexplained. These limits can be overcome by reconstructing ancient proteins, experimentally characterizing their properties, and retracing their evolution through time. This approach has proven to be a powerful means for discovering how historical changes in sequence produced the functions, structures, and other physical/chemical characteristics of modern proteins. It has also illuminated whether protein features evolved because of functional optimization, historical constraint, or blind chance. Here we review recent studies employing ancestral protein reconstruction and show how they have produced new knowledge not only of molecular evolutionary processes but also of the underlying determinants of modern proteins’ physical, chemical, and biological properties.

The neural code that relates the firing of neurons to the generation of behavior and mental states must be implemented by spatiotemporal patterns of activity across neuronal populations. These patterns engage selective groups of neurons, called neuronal ensembles, which are emergent building blocks of neural circuits. We review optical and computational methods, based on two-photon calcium imaging and two-photon optogenetics, to detect, characterize, and manipulate neuronal ensembles in three dimensions. We review data using these methods in the mammalian cortex that demonstrate the existence of neuronal ensembles in the spontaneous and evoked cortical activity in vitro and in vivo. Moreover, two-photon optogenetics enable the possibility of artificially imprinting neuronal ensembles into awake, behaving animals and of later recalling those ensembles selectively by stimulating individual cells. These methods could enable deciphering the neural code and also be used to understand the pathophysiology of and design novel therapies for neurological and mental diseases.

Many of the most important molecules of life are polymers. In animals, the most abundant of the proteinaceous polymers are the collagens, which constitute the fibrous matrix outside cells and which can also self-assemble into gels. The physically measurable stiffness of gels, as well as tissues, increases with the amount of collagen, and cells seem to sense this stiffness. An understanding of this mechanosensing process in complex tissues, including fibrotic disease states with high collagen, is now utilizing ’omics data sets and is revealing polymer physics–type, nonlinear scaling relationships between concentrations of seemingly unrelated biopolymers. The nuclear structure protein lamin A provides one example, with protein and transcript levels increasing with collagen 1 and tissue stiffness, and with mechanisms rooted in protein stabilization induced by cytoskeletal stress. Physics-based models of fibrous matrix, cytoskeletal force dipoles, and the lamin A gene circuit illustrate the wide range of testable predictions emerging for tissues, cell cultures, and even stem cell–based tissue regeneration. Beyond the epigenetics of mechanosensing, the scaling in cancer of chromosome copy number variations and other mutations with tissue stiffness suggests that genomic changes are occurring by mechanogenomic processes that now require elucidation.

Nuclear magnetic resonance (NMR) spectroscopy has been instrumental during the past two decades in providing high-resolution structures of protein complexes. It has been the method of choice for determining the structure of dynamic protein complexes, which are typically recalcitrant to other structural techniques. Until recently, NMR spectroscopy has yielded structures of small or medium-sized protein complexes, up to approximately 30–40 kDa. Major breakthroughs during the past decade, especially in isotope-labeling techniques, have enabled NMR characterization of large protein systems with molecular weights of hundreds of kDa. This has provided unique insights into the binding, dynamic, and allosteric properties of large systems. Notably, there is now a slowly but steadily growing list of large, dynamic protein complexes whose atomic structure has been determined by NMR. Many of these complexes are characterized by a high degree of flexibility and, thus, their structures could not have been obtained using other structural methods. Especially in the field of molecular chaperones, NMR has recently provided the first-ever high-resolution structures of their complexes with unfolded proteins. Further technological advances will establish NMR as the primary tool for obtaining atomic structures of challenging systems with even higher complexity.

The development of organisms starting from their zygotic state involves a tight integration of the myriad biochemical signaling interactions with the mechanical forces that eventually pattern and shape the resulting embryo. In the past decade, it has become increasingly evident that several important developmental processes involve mechanical forces in an essential manner. In this review, we highlight the multifaceted role of mechanics in pattern formation, from protein and cell sorting to the generation of tissue shape. We then review the ways in which the active cellular cytoskeleton self-organizes to form dynamic patterns. Finally, we focus on mechanochemical feedback, where signaling proteins can establish patterns via coupling to the activity of the cytoskeleton. Throughout the review, we focus on the generic physical principles of the establishment of active mechanochemical patterns and point toward future directions in studying how the principles of mechanics and chemistry combine to drive morphogenetic pattern formation.

Telomeres are specialized chromatin structures that protect chromosome ends from dangerous processing events. In most tissues, telomeres shorten with each round of cell division, placing a finite limit on cell growth. In rapidly dividing cells, including the majority of human cancers, cells bypass this growth limit through telomerase-catalyzed maintenance of telomere length. The dynamic properties of telomeres and telomerase render them difficult to study using ensemble biochemical and structural techniques. This review describes single-molecule approaches to studying how individual components of telomeres and telomerase contribute to function. Single-molecule methods provide a window into the complex nature of telomeres and telomerase by permitting researchers to directly visualize and manipulate the individual protein, DNA, and RNA molecules required for telomere function. The work reviewed in this article highlights how single-molecule techniques have been utilized to investigate the function of telomeres and telomerase.

Membrane lipids and cellular water (soft matter) are becoming increasingly recognized as key determinants of protein structure and function. Their influences can be ascribed to modulation of the bilayer properties or to specific binding and allosteric regulation of protein activity. In this review, we first consider hydrophobic matching of the intramembranous proteolipid boundary to explain the conformational changes and oligomeric states of proteins within the bilayer. Alternatively, membranes can be viewed as complex fluids, whose properties are linked to key biological functions. Critical behavior and nonideal mixing of the lipids have been proposed to explain how raft-like microstructures involving cholesterol affect membrane protein activity. Furthermore, the persistence length for lipid–protein interactions suggests the curvature force field of the membrane comes into play. A flexible surface model describes how curvature and hydrophobic forces lead to the emergence of new protein functional states within the membrane lipid bilayer.

Ubiquitous conserved processes that repair DNA damage are essential for the maintenance and propagation of genomes over generations. Then again, inaccuracies in DNA transactions and failures to remove mutagenic lesions cause heritable genome changes. Building on decades of research using genetics and biochemistry, unprecedented quantitative insight into DNA repair mechanisms has come from the new-found ability to measure single proteins in vitro and inside individual living cells. This has brought together biologists, chemists, engineers, physicists, and mathematicians to solve long-standing questions about the way in which repair enzymes search for DNA lesions and form protein complexes that act in DNA repair pathways. Furthermore, unexpected discoveries have resulted from capabilities to resolve molecular heterogeneity and cell subpopulations, provoking new questions about the role of stochastic processes in DNA repair and mutagenesis. These studies are leading to new technologies that will find widespread use in basic research, biotechnology, and medicine.

Understanding how distinct parts of proteins produce coordinated behavior has driven and continues to drive advances in protein science and enzymology. However, despite consensus about the conceptual basis for allostery, the idiosyncratic nature of allosteric mechanisms resists general approaches. Computational methods can identify conformational transition states from structural changes, revealing common switching mechanisms that impose multistate behavior. Thermodynamic cycles use factorial perturbations to measure coupling energies between side chains in molecular switches that mediate shear during domain motion. Such cycles have now been complemented by modular cycles that measure energetic coupling between separable domains. For one model system, energetic coupling between domains has been shown to be quantitatively equivalent to that between dynamic side chains. Linkages between domain motion, switching residues, and catalysis make nucleoside triphosphate hydrolysis conditional on domain movement, confirming an essential yet neglected aspect of free energy transduction and suggesting the potential generality of these studies.