Quarterly Reviews of Biophysics最新文献

Allosteric communication is established by networks through which strain energy generated at the allosteric site by an allosteric event, such as ligand binding, can propagate to the functional site. Exerted on multiple molecules in the cell, it can wield a biased function. Here, we discuss allosteric networks and allosteric signaling bias. Networks are graphs specified by nodes (residues) and edges (their connections). Allosteric bias is a property of a population. It is described by allosteric effector-specific dynamic distributions of conformational ensembles, as classically exemplified by G protein-coupled receptors (GPCRs). An ensemble describes the likelihood of a specific (strong/weak) allosteric signal propagating to a specific functional site. A network description provides the propagation route in a specific conformation, pinpointing key residues whose mutations could promote drug resistance. Efficiency is influenced by path length, relative stabilities and allosteric transitions. Through specific contacts, specific ligands can bias signaling in proteins, for example, in receptor tyrosine kinases (RTKs) toward specific phosphorylation sites and cell signaling activation. Thus, rather than the two - active and inactive - states, and a single pathway, we consider multiple states and favored pathways. This allows us to consider biased allosteric switches among minor, invisible states and observable outcomes. Within this framework, we further consider signaling strength and duration as key determinants of cell fate: If weak and sustained, it may induce differentiation; If bursts of strong and short, proliferation.

Time-resolved (TR) intrinsic fluorescence of tryptophan (Trp) provides a wealth of information on the structure and localization of proteins and peptides and their interactions with one another, with drugs, lipid membranes, lipid- and surfactant-based drug delivery systems, et cetera. Intrinsic Trp eliminates the need for labeling and avoids the perturbation of the system by the label; introduced Trp is a rather conservative and small label compared to others. Whereas custom-tailored fluorophores are often optimized for a special technique, Trp can be employed to monitor a wide variety of effects. We address interactions of Trp with surrounding molecules, dynamic quenchers and Förster resonance energy transfer (FRET) acceptors that affect the fluorescence decay. Speed and range of angular motion of Trp are characterized by TR anisotropy. Electrostatic interactions of Trp with charged and polar molecules, including water, are monitored by decay-associated spectra (DAS) or TR emission spectra (TRES) and quantified in terms of TR shifts of the spectral center of gravity. This versatility is a great advantage and, at the same time, comes with a complexity of the behavior that can render it a challenge to interpret the data in detail properly. This review provides an overview of applications of TR fluorescence of Trp bulk samples in biomolecular, biophysical, and pharmaceutical studies. The aim is not only to point out the diversity of the read-out of these techniques, but also critically examine their current use. Therefore, we identify most common technical pitfalls and evaluate the degree of reliability of the interpretational approaches. This should aid a more extensive and meaningful use of TR fluorescence of Trp.

The question of whether PCR is reliable sounds strange at first. However, looking at the scientific literature from the 1950s and 60s, one will find many publications on the physicochemistry of DNA that have been forgotten meanwhile. Quite a few of these studies have shown that DNA is thermolabile, which consequently raises the question of whether this thermolability is relevant in the context of PCR, namely in the denaturation phase. However, it can be shown that this is not the case: losses due to thermal hydrolysis are irrelevant for the performance of contemporary PCR protocols and their specificity as well as for the significance of their results. There is now a huge amount of scientifically verified and published data on technical and molecular aspects of PCR, a small selection of which we quote here. In addition, we present some primary data that also clearly demonstrate the reliability of PCR.

Neurotransmitter release via synaptic vesicle fusion with the plasma membrane is driven by SNARE proteins (Synaptobrevin, Syntaxin, and SNAP-25) and accessory proteins (Synaptotagmin, Complexin, Munc13, and Munc18). While extensively studied experimentally, the precise mechanisms and dynamics remain elusive due to spatiotemporal limitations. Molecular dynamics (MD) simulations-both all-atom (AA) and coarse-grained (CG)-bridge these gaps by capturing fusion dynamics beyond experimental resolution. This review explores the use of these simulations in understanding SNARE-mediated membrane fusion and its regulation by Synaptotagmin and Complexin. We first examine two competing hypotheses regarding the driving force of fusion: (1) SNARE zippering transducing energy through rigid juxtamembrane domains (JMDs) and (2) SNAREs generating entropic forces via flexible JMDs. Despite different origins of forces, the conserved fusion pathway - from membrane adhesion to stalk and fusion pore (FP) formation - emerges across models. We also highlight the critical role of SNARE transmembrane domains (TMDs) and their regulation by post-translational modifications like palmitoylation in fast fusion. Further, we review Ca²⁺-dependent interactions of Synaptotagmin's C2 domains with lipids and SNAREs at the primary and tripartite interfaces, and how these interactions regulate fusion timing. Complexin's role in clamping spontaneous fusion while facilitating evoked release via its central and accessory helices is also discussed. We present a case study leveraging AA and CG simulations to investigate ion selectivity in FPs, balancing timescale and accuracy. We conclude with the limitations in current simulations and using AI tools to construct complete fusion machinery and explore isoform-specific functions in fusion machinery.

Riboswitches are RNA elements with a defined structure found in noncoding sections of genes that allow the direct control of gene expression by the binding of small molecules functionally related to the gene product. In most cases, this is a metabolite in the same (typically biosynthetic) pathway as an enzyme (or transporter) encoded by the gene that is controlled. The structures of many riboswitches have been determined and this provides a large database of RNA structure and ligand binding. In this review, we extract general principles of RNA structure and the manner or ligand binding from this resource.

Binding sites are key components of biomolecular structures, such as proteins and RNAs, serving as hubs for interactions with other molecules. Identification of the binding sites in macromolecules is essential for structure-based molecular and drug design. However, experimental methods for binding site identification are resource-intensive and time-consuming. In contrast, computational methods enable large-scale binding site identification, structure flexibility analysis, as well as assessment of intermolecular interactions within the binding sites. In this review, we describe recent advances in binding site identification using machine learning methods; we classify the approaches based on the encoding of the macromolecule information about its sequence, structure, template knowledge, geometry, and energetic characteristics. Importantly, we categorize the methods based on the type of the interacting molecule, namely, small molecules, peptides, and ions. Finally, we describe perspectives, limitations, and challenges of the state-of-the-art methods with an emphasis on deep learning-based approaches. These computational approaches aim to advance drug discovery by expanding the druggable genome through the identification of novel binding sites in pharmacological targets and facilitating structure-based hit identification and lead optimization.

This review article describes the co-evolution of structural biology as a discipline and the Protein Data Bank (PDB), established in 1971 as the first open-access data resource in biology by like-minded structural scientists. As the PDB archive grew in size and scope to encompass macromolecular crystallography, NMR spectroscopy, and cryo-electron microscopy, new technologies were developed to ingest, validate, curate, store, and distribute the information. Community engagement ensured that the needs of structural biologists (data depositors) and data consumers were met. Today, the archive houses more than 230,000 experimentally determined structures of proteins, nucleic acids, and macromolecular machines and their complexes with one another and small-molecule ligands. Aggregate costs of PDB data preservation are ~1% of the cost of structure determination. The enormous impact of PDB data on basic and applied research and education across the natural and medical sciences is presented and highlighted with illustrative examples. Enablement of de novo protein structure prediction (AlphaFold2, RoseTTAfold, OpenFold, etc.) is the most widely appreciated benefit of having a corpus of rigorously validated, expertly curated 3D biostructure data.