Deciphering hierarchical features in the energy landscape of adenylate kinase folding/unfolding

Conformational dynamics of adenylate kinase in crystals

Adenylate kinase is a ubiquitous enzyme in living systems and undergoes dramatic conformational changes during its catalytic cycle. For these reasons, it is widely studied by genetic, biochemical, and biophysical methods, both experimental and theoretical. We have determined the basic crystal structures of three differently liganded states of adenylate kinase from Methanotorrus igneus, a hyperthermophilic organism whose adenylate kinase is a homotrimeric oligomer. The multiple copies of each protomer in the asymmetric unit of the crystal provide a unique opportunity to study the variation in the structure and were further analyzed using advanced crystallographic refinement methods and analysis tools to reveal conformational heterogeneity and, thus, implied dynamic behaviors in the catalytic cycle.

Moving beyond static snapshots: Protein dynamics and the Protein Data Bank

Proteins are the molecular machines of living systems. Their dynamics are an intrinsic part of their evolutionary selection in carrying out their biological functions. Although the dynamics are more difficult to observe than a static, average structure, we are beginning to observe these dynamics and form sound mechanistic connections between structure, dynamics, and function. This progress is highlighted in case studies from myoglobin and adenylate kinase to the ribosome and molecular motors where these molecules are being probed with a multitude of techniques across many timescales. New approaches to time-resolved crystallography are allowing simple “movies” to be taken of proteins in action, and new methods of mapping the variations in cryo-electron microscopy are emerging to reveal a more complete description of life’s machines. The results of these new methods are aided in their dissemination by continual improvements in curation and distribution by the Protein Data Bank and their partners around the world.

The road less traveled in protein folding: evidence for multiple pathways

Free Energy Landscape theory of Protein Folding, introduced over 20 years ago, implies that a protein has many paths to the folded conformation with the lowest free energy. Despite the knowledge in principle, it has been remarkably hard to detect such pathways. The lack of such observations is primarily due to the fact that no one experimental technique can detect many parts of the protein simultaneously with the time resolution necessary to see such differences in paths. However, recent technical developments and employment of multiple experimental probes and folding prompts have illuminated multiple folding pathways in a number of proteins that had all previously been described with a single path.

Single-molecule FRET methods to study the dynamics of proteins at work

Feynman commented that "Everything that living things do can be understood in terms of the jiggling and wiggling of atoms". Proteins can jiggle and wiggle large structural elements such as domains and subunits as part of their functional cycles. Single-molecule fluorescence resonance energy transfer (smFRET) is an excellent tool to study conformational dynamics and decipher coordinated large-scale motions within proteins. smFRET methods introduced in recent years are geared toward understanding the time scales and amplitudes of function-related motions. This review discusses the methodology for obtaining and analyzing smFRET temporal trajectories that provide direct dynamic information on transitions between conformational states. It also introduces correlation methods that are useful for characterizing intramolecular motions. This arsenal of techniques has been used to study multiple molecular systems, from membrane proteins through molecular chaperones, and we examine some of these studies here. Recent exciting methodological novelties permit revealing very fast, submillisecond dynamics, whose relevance to protein function is yet to be fully grasped.

Photobleaching statistics in single-molecule on-/off-time distributions

The on- and and off-time distributions from fluorescence single-molecule experiments are widely used to extract kinetics parameters with the goal to provide a quantitative description for the molecule's behavior on the ensemble level. Such experiments are inevitably influenced by photobleaching, where the fluorescent probe transitions to a nonemissive state. Yet, it appears that few reports went beyond acknowledging this unavoidable complication; in fact, it has so far been ignored when evaluating off-time distributions. Here, we present a theoretical framework that allows the derivation of analytical equations in which photobleaching kinetics are rigorously incorporated. Unexpectedly, our results indicate that the off-time distribution should be nonexponential even when all the rate processes are single exponential. With the analytical theory understood and demonstrated as easy to implement, such ubiquitous photochemical processes can now be readily included in routine experimental analyses.

Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease

Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional biochemical information to benefit histological disease diagnosis. Using a dietary model of nonalcoholic fatty liver disease in rats, we combine Raman imaging with machine learning and information theory to evaluate cellular-level information in liver tissue samples. After increasing signal-to-noise ratio in the Raman images through superpixel segmentation, we extract biochemically distinct regions within liver tissues, allowing for quantification of characteristic biochemical components such as vitamin A and lipids. Armed with microscopic information about the biochemical composition of the liver tissues, we group tissues having similar composition, providing a descriptor enabling inference of tissue states, contributing valuable information to histological inspection.

High-Resolution Raman Microscopic Detection of Follicular Thyroid Cancer Cells with Unsupervised Machine Learning

We use Raman microscopic images with high spatial and spectral resolution to investigate differences between human follicular thyroid (Nthy-ori 3-1) and follicular thyroid carcinoma (FTC-133) cells, a well-differentiated thyroid cancer. Through comparison to classification of single-cell Raman spectra, the importance of subcellular information in the Raman images is emphasized. Subcellular information is extracted through a coarse-graining of the spectra at high spatial resolution (∼1.7 μm²), producing a set of characteristic spectral groups representing locations having similar biochemical compositions. We develop a cell classifier based on the frequencies at which the characteristic spectra appear within each of the single cells. Using this classifier, we obtain a more accurate (89.8%) distinction of FTC-133 and Nthy-ori 3-1, in comparison to single-cell spectra (77.6%). We also infer which subcellular components are important to cellular distinction; we find that cancerous FTC-133 cells contain increased populations of lipid-containing components and decreased populations of cytochrome-containing components relative to Nthy-ori 3-1, and that the regions containing these contributions are largely outside the cell nuclei. In addition to increased classification accuracy, this approach provides rich subcellular information about biochemical differences and cellular locations associated with the distinction of the normal and cancerous follicular thyroid cells.

Nature's Shortcut to Protein Folding

This Feature Article presents a view of the protein folding transition based on the hypothesis that Nature has built features within the sequences that enable a Shortcut to efficient folding. Nature's Shortcut is proposed to be the early establishment of a set of nonlocal weak contacts, constituting protein loops that significantly constrain regions of the collapsed disordered protein into a native-like low-resolution fluctuating topology of major sections of the backbone. Nature's establishment of this scaffold of nonlocal contacts is claimed to bypass what would otherwise be a nearly hopeless unaided search for the final three-dimensional structure in proteins longer than ∼100 amino acids. To support this main contention of the Feature Article, the loop hypothesis (LH) description of early folding events is experimentally tested with time-resolved Förster resonance energy transfer techniques for adenylate kinase, and the data are shown to be consistent with theoretical predictions from the sequential collapse model (SCM). The experimentally based LH and the theoretically founded SCM are argued to provide a unified picture of the role of nonlocal contacts as constituting Nature's Shortcut to protein folding. Importantly, the SCM is shown to reliably predict key nonlocal contacts utilizing only primary sequence information. This view on Nature's Shortcut is open to the protein community for further detailed assessment, including its practical consequences, by suitable application of advanced experimental and computational techniques.

Phosphorylation Induces Conformational Rigidity at the C-Terminal Domain of AMPA Receptors

The intracellular C-terminal domain (CTD) of AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor undergoes phosphorylation at specific locations during long-term potentiation. This modification enhances conductance through the AMPA receptor ion channel and thus potentially plays a crucial role in modulating receptor trafficking and signaling. However, because the CTD structure is largely unresolved, it is difficult to establish if phosphorylation induces conformational changes that might play a role in enhancing channel conductance. Herein, we utilize single-molecule Förster resonance energy transfer (smFRET) spectroscopy to probe the conformational changes of a section of the AMPA receptor CTD, under the conditions of point-mutated phosphomimicry. Multiple analysis algorithms fail to identify stable conformational states within the smFRET distributions, consistent with a lack of well-defined secondary structure. Instead, our results show that phosphomimicry induces conformational rigidity to the CTD, and such rigidity is electrostatically tunable.

Preface: Special Topic on Single-Molecule Biophysics

Single-molecule measurements are now almost routinely used to study biological systems and processes. The scope of this special topic emphasizes the physics side of single-molecule observations, with the goal of highlighting new developments in physical techniques as well as conceptual insights that single-molecule measurements bring to biophysics. This issue also comprises recent advances in theoretical physical models of single-molecule phenomena, interpretation of single-molecule signals, and fundamental areas of statistical mechanics that are related to single-molecule observations. A particular goal is to illustrate the increasing synergy between theory, simulation, and experiment in single-molecule biophysics.