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AbstractIntrinsically disordered proteins (IDPs) are a subset of proteins that lack stable secondary structure. Given their polymeric nature, previous mean-field approximations have been used to describe the statistical structure of IDPs. However, the amino-acid sequence heterogeneity and complex intermolecular interaction network have significantly impeded the ability to get proper approximations. One such case is the intrinsically disordered tail domain of neurofilament low (NFLt), which comprises a 50 residue-long uncharged domain followed by a 96 residue-long negatively charged domain. Here, we measure two NFLt variants to identify the impact of the NFLt two main subdomains on its complex interactions and statistical structure. Using synchrotron small-angle x-ray scattering, we find that the uncharged domain of the NFLt induces attractive interactions that cause it to self-assemble into star-like polymer brushes. On the other hand, when the uncharged domain is truncated, the remaining charged N-terminal domains remain isolated in solution with typical polyelectrolyte characteristics. We further discuss how competing long- and short-ranged interactions within the polymer brushes dominate their ensemble structure and, in turn, their implications on previously observed phenomena in NFL native and diseased states. Graphic abstractVisual schematic of the SAXS measurement results of the Neurofilament-low tail domain IDP (NFLt). NFLts assemble into star-like brushes through their hydrophobic N-terminal domains (marked in blue). In increasing salinity, brush height (h) is initially increased following a decrease while gaining additional tails to their assembly. Isolating the charged sub-domain of the NFLt (marked in red) results in isolated polyelectrolytes 

The confluence of recent discoveries of the roles of biomolecular liquids in living systems and modern abilities to precisely synthesize and modify nucleic acids (NAs) has led to a surge of interest in liquid phases of NAs. These phases can be formed primarily from NAs, as driven by base-pairing interactions, or from the electrostatic combination (coacervation) of negatively charged NAs and positively charged molecules. Generally, the use of sequence-engineered NAs provides the means to tune microsopic particle properties, and thus imbue specific, customizable behaviors into the resulting liquids. In this way, researchers have used NA liquids to tackle fundamental problems in the physics of finite valence soft materials, and to create liquids with novel structured and/or multi-functional properties. Here, we review this growing field, discussing the theoretical background of NA liquid phase separation, quantitative understanding of liquid material properties, and the broad and growing array of functional demonstrations in these materials. We close with a few comments discussing remaining open questions and challenges in the field. 

Liquid–liquid phase separation (LLPS) in macromolecular solutions (e.g., coacervation) is relevant both to technology and to the process of mesoscale structure formation in cells. The LLPS process is characterized by a phase diagram, i.e., binodal lines in the temperature/concentration plane, which must be quantified to predict the system’s behavior. Experimentally, this can be difficult due to complications in handling the dense macromolecular phase. Here, we develop a method for accurately quantifying the phase diagram without direct handling: We confine the sample within micron-scale, water-in-oil emulsion droplets and then use precision fluorescent imaging to measure the volume fraction of the condensate within the droplet. We find that this volume fraction grows linearly with macromolecule concentration; thus, by applying the lever rule, we can directly extract the dense and dilute binodal concentrations. We use this approach to study a model LLPS system of self-assembled, fixed-valence DNA particles termed nanostars (NSs). We find that temperature/concentration phase diagrams of NSs display, with certain exceptions, a larger co-existence regime upon increasing salt or valence, in line with expectations. Aspects of the measured phase behavior validate recent predictions that account for the role of valence in modulating the connectivity of the condensed phase. Generally, our results on NS phase diagrams give fundamental insight into limited-valence phase separation, while the method we have developed will likely be useful in the study of other LLPS systems. 

Short-range interactions and long-range contacts drive the 3D folding of structured proteins. The proteins’ structure has a direct impact on their biological function. However, nearly 40% of the eukaryotes proteome is composed of intrinsically disordered proteins (IDPs) and protein regions that fluctuate between ensembles of numerous conformations. Therefore, to understand their biological function, it is critical to depict how the structural ensemble statistics correlate to the IDPs’ amino acid sequence. Here, using small-angle X-ray scattering and time-resolved Förster resonance energy transfer (trFRET), we study the intramolecular structural heterogeneity of the neurofilament low intrinsically disordered tail domain (NFLt). Using theoretical results of polymer physics, we find that the Flory scaling exponent of NFLt subsegments correlates linearly with their net charge, ranging from statistics of ideal to self-avoiding chains. Surprisingly, measuring the same segments in the context of the whole NFLt protein, we find that regardless of the peptide sequence, the segments’ structural statistics are more expanded than when measured independently. Our findings show that while polymer physics can, to some level, relate the IDP’s sequence to its ensemble conformations, long-range contacts between distant amino acids play a crucial role in determining intramolecular structures. This emphasizes the necessity of advanced polymer theories to fully describe IDPs ensembles with the hope that it will allow us to model their biological function. 

Single-molecule force spectroscopy (SMFS) instruments (e.g., magnetic and optical tweezers) often use video tracking to measure the three-dimensional position of micron-scale beads under an applied force. The force in these experiments is calibrated by comparing the bead trajectory to a thermal motion-based model with the drag coefficient, γ , and trap spring constant, κ , as parameters. Estimating accurate parameters is complicated by systematic biases from spectral distortions, the camera exposure time, parasitic noise, and least-squares fitting methods. However, while robust calibration methods exist that correct for these biases, they are not always used because they can be complex to implement computationally. To address this barrier, we present Tweezepy: a Python package for calibrating forces in SMFS video-tracking experiments. Tweezepy uses maximum likelihood estimation (MLE) to estimate parameters and their uncertainties from a single bead trajectory via the power spectral density (PSD) and Allan variance (AV). It is well-documented, fast, easy to use, and accounts for most common sources of biases in SMFS video-tracking experiments. Here, we provide a comprehensive overview of Tweezepy’s calibration scheme, including a review of the theory underlying thermal motion-based parameter estimates, a discussion of the PSD, AV, and MLE, and an explanation of their implementation.