Protein Dynamics Research Articles

Single-Molecule Tracking dataset for histone H3 (hht1) from live and fixed cells of Schizosaccharomyces pombe

Single-molecule tracking provides direct real-time measurements of protein dynamics in live cells with high spatiotemporal resolution. In the last decade, this method has been adopted by several labs to understand the dynamics of DNA replication, transcription, telomerase, chromatin remodeling, etc. in a variety of model systems (bacteria, yeast S. cerevisiae, cell lines, embryo). However, it has not been employed for the yeast Schizosaccharomyces pombe, despite being a valuable model system. Here, we present single-molecule tracking datasets for chromatin-bound histone H3 (Hht1) in live and fixed cells and freely diffusing GFP in S. pombe nuclei to benchmark the diffusion parameters (diffusion coefficient, mean squared displacement, fraction of bound molecules, residence time). These parameters will be used to differentiate chromatin-bound molecules from unbound (free) molecules of any protein. It will be a valuable resource for any lab that starts employing this method for studying protein dynamics. This dataset can also be used to validate the performance of various tracking software and as a training dataset for machine learning-based automated tracking.

Collapse and Protein Folding: Should We Be Surprised that Biothermodynamics Works So Well?

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.

PROTAC-induced Protein Structural Dynamics in Targeted Protein Degradation.

PROteolysis TArgeting Chimeras (PROTACs) are small molecules that induce target protein degradation via the ubiquitin-proteasome system. PROTACs recruit the target protein and E3 ligase; a critical first step is forming a ternary complex. However, while the formation a ternary complex is crucial, it may not always guarantee successful protein degradation. The dynamics of the PROTAC-induced degradation complex play a key role in ubiquitination and subsequent degradation. In this study, we computationally modelled protein complex structures and dynamics associated with a series of PROTACs featuring different linkers to investigate why these PROTACs, all of which formed ternary complexes with Cereblon (CRBN) E3 ligase and the target protein bromodomain-containing protein 4 (BRD4 BD1 ), exhibited varying degrees of degradation potency. We constructed the degradation machinery complexes with Culling-Ring Ligase 4A (CRL4A) E3 ligase scaffolds. Through atomistic molecular dynamics simulations, we illustrated how PROTAC-dependent protein dynamics facilitating the arrangement of surface lysine residues of BRD4 BD1 into the catalytic pocket of E2/ubiquitin cascade for ubiquitination. Despite featuring identical warheads in this PROTAC series, the linkers were found to affect the residue-interaction networks, and thus governing the essential motions of the entire degradation machine for ubiquitination. These findings offer a structural dynamic perspective on ligand-induced protein degradation, providing insights to guide future PROTAC design endeavors.

Sequence complexity and monomer rigidity control the morphologies and aging dynamics of protein aggregates

Understanding the biophysical basis of protein aggregation is important in biology because of the potential link to several misfolding diseases. Although experiments have shown that protein aggregates adopt a variety of morphologies, the dynamics of their formation are less well characterized. Here, we introduce a minimal model to explore the dependence of the aggregation dynamics on the structural and sequence features of the monomers. Using simulations, we demonstrate that sequence complexity (codified in terms of word entropy) and monomer rigidity profoundly influence the dynamics and morphology of the aggregates. Flexible monomers with low sequence complexity (corresponding to repeat sequences) form liquid-like droplets that exhibit ergodic behavior. Strikingly, these aggregates abruptly transition to more ordered structures, reminiscent of amyloid fibrils, when the monomer rigidity is increased. In contrast, aggregates resulting from monomers with high sequence complexity are amorphous and display nonergodic glassy dynamics. The heterogeneous dynamics of the low and high-complexity sequences follow stretched exponential kinetics, which is one of the characteristics of glassy dynamics. Importantly, at nonzero values of the bending rigidities, the aggregates age with the relaxation times that increase with the waiting time. Informed by these findings, we provide insights into aging dynamics in protein condensates and contrast the behavior with the dynamics expected in RNA repeat sequences. Our findings underscore the influence of the monomer characteristics in shaping the morphology and dynamics of protein aggregates, thus providing a foundation for deciphering the general rules governing the behavior of protein condensates.

Atomic molecular dynamics simulation advances of de novo-designed proteins.

Proteins are vital biological macromolecules that execute biological functions and form the core of synthetic biological systems. The history of de novo protein has evolved from initial successes in subordinate structural design to more intricate protein creation, challenging the complexities of natural proteins. Recent strides in protein design have leveraged computational methods to craft proteins for functions beyond their natural capabilities. Molecular dynamics (MD) simulations have emerged as a crucial tool for comprehending the structural and dynamic properties of de novo-designed proteins. In this study, we examined the pivotal role of MD simulations in elucidating the sampling methods, force field, water models, stability, and dynamics of de novo-designed proteins, highlighting their potential applications in diverse fields. The synergy between computational modeling and experimental validation continued to play a crucial role in the creation of novel proteins tailored for specific functions and applications.

High‐Efficiency Trifluoromethyl‐Methionine Incorporation into Cyclophilin A by Cell‐Free Synthesis for 19F NMR Studies

AbstractFluorine‐19 NMR spectroscopy has emerged as a powerful tool for studying protein structure, dynamics, and interactions. Of particular interest is the exploitation of trifluoromethyl (tfm) groups, given their high sensitivity and superior transverse relaxation properties, compared to single fluorine atoms. However, biosynthetic incorporation of tfm‐bearing amino acids remains challenging due to cytotoxicity and incompatibility with natural tRNA synthetases. Here, we report on overcoming this challenge using cell‐free synthesis, incorporating trifluoromethyl‐methionine (tfmM) into the protein Cyclophilin A (CypA) with remarkably high efficiency, impossible via biosynthetic means. Importantly, we demonstrate that tfmM CypA binds a native substrate, the N‐terminal domain of HIV‐1 capsid protein (HIV‐1 CA‐NTD), and retains peptidyl prolyl cis/trans isomerase activity. It also binds the peptide inhibitor Cyclosporine A (CsA) with the same affinity as non‐labeled, wild‐type CypA. Furthermore, we show that 19F isotope shifts and 19F solvent paramagnetic relaxation enhancements (PREs) provide valuable structural information on surface exposure. Taken together, our study illustrates that tfmM can be readily incorporated into proteins at very high levels by cell‐free synthesis without disturbing protein structure and function, significantly expanding the scope of 19F NMR spectroscopy for studying protein structure and dynamics.

Role of the αC-β4 loop in protein kinase structure and dynamics.

Although the αC-β4 loop is a stable feature of all protein kinases, the importance of this motif as a conserved element of secondary structure, as well as its links to the hydrophobic architecture of the kinase core, has been underappreciated. We first review the motif and then describe how it is linked to the hydrophobic spine architecture of the kinase core, which we first discovered using a computational tool, local spatial Pattern (LSP) alignment. Based on NMR predictions that a mutation in this motif abolishes the synergistic high-affinity binding of ATP and a pseudo substrate inhibitor, we used LSP to interrogate the F100A mutant. This comparison highlights the importance of the αC-β4 loop and key residues at the interface between the N- and C-lobes. In addition, we delved more deeply into the structure of the apo C-subunit, which lacks ATP. While apo C-subunit showed no significant changes in backbone dynamics of the αC-β4 loop, we found significant differences in the side chain dynamics of K105. The LSP analysis suggests disruption of communication between the N- and C-lobes in the F100A mutant, which would be consistent with the structural changes predicted by the NMR spectroscopy.

Role of the αC-β4 loop in protein kinase structure and dynamics

Although the αC-β4 loop is a stable feature of all protein kinases, the importance of this motif as a conserved element of secondary structure, as well as its links to the hydrophobic architecture of the kinase core, has been underappreciated. We first review the motif and then describe how it is linked to the hydrophobic spine architecture of the kinase core, which we first discovered using a computational tool, local spatial Pattern (LSP) alignment. Based on NMR predictions that a mutation in this motif abolishes the synergistic high-affinity binding of ATP and a pseudo substrate inhibitor, we used LSP to interrogate the F100A mutant. This comparison highlights the importance of the αC-β4 loop and key residues at the interface between the N- and C-lobes. In addition, we delved more deeply into the structure of the apo C-subunit, which lacks ATP. While apo C-subunit showed no significant changes in backbone dynamics of the αC-β4 loop, we found significant differences in the side chain dynamics of K105. The LSP analysis suggests disruption of communication between the N- and C-lobes in the F100A mutant, which would be consistent with the structural changes predicted by the NMR spectroscopy.

Mapping the FF domain folding pathway via structures of transiently populated folding intermediates

Despite the tremendous accomplishments of AlpaFold2/3 in predicting biomolecular structure, the protein folding problem remains unsolved in the sense that accurate atomistic models of how protein molecules fold into their native conformations from an unfolded ensemble are still elusive. Here, using chemical exchange saturation transfer (CEST) NMR experiments and a comprehensive four-state kinetic model of the folding trajectory of a 71 residue four-helix bundle FF domain from human HYPA/FBP11 we present an atomic resolution structure of a transiently formed intermediate, I2, that along with the structure of a second intermediate, I1, provides a description of the FF domain folding trajectory. By recording CEST profiles as a function of urea concentration the extent of compaction along the folding pathway is evaluated. Our data establish that unlike the partially disordered I1 state, the I2 intermediate that is also formed before the rate-limiting folding barrier is well ordered and compact like the native conformer, while retaining nonnative interactions similar to those found in I1. The slow-interconversion from I2 to F, involving changes in secondary structure and the breaking of nonnative interactions, proceeds via a compact transition-state. Interestingly, the native state of the FF1 domain from human p190-A Rho GAP resembles the I2 conformation, suggesting that well-ordered folding intermediates can be repurposed by nature in structurally related proteins to assume functional roles. It is anticipated that the strategy for elucidation of sparsely populated and transiently formed structures of intermediates along kinetic pathways described here will be of use in other studies of protein dynamics.

Site-specific incorporation of 19F-nulcei at protein C-terminus to probe allosteric conformational transitions of metalloproteins

Allosteric conformational change is an important paradigm in the regulation of protein function, which is typically triggered by the binding of small cofactors, metal ions or protein partners. Here, we found those conformational transitions can be effectively monitored by 19F NMR, facilitated by a site-specific 19F incorporation strategy at the protein C-terminus using asparaginyl endopeptidase (AEP). Three case studies show that C-terminal 19F-nuclei can reveal protein dynamics not only adjacent but also distal to C-terminus, including those occurring in a hemoprotein neuroglobin (Ngb), calmodulin (CaM), and a cobalt metalloregulator (CoaR) responding to both cobalt and tetrapyrrole. In Ngb, the heme orientation disorder is affected by missense mutations that perturb backbone rigidity or surface charges close to the heme axial ligands. In CaM, the C-terminal 19F-nuclei is an ideal probe for detecting the binding states of Ca2+, peptides and inhibitors. Furthermore, multiple 19F-moieties were incorporated into the two domains of CoaR, revealing the intrinsically disordered C-terminal metal binding tail might be an allosteric conformational switch to maintain cobalt homeostasis and balance corrinoid biosynthesis. This study demonstrates that the AEP-based 19F-modification strategy can be applied to various targets to study allosteric regulation, especially for those biological processes modulated by the protein C-terminus.

Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics.

The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic resonance (NMR) is the experimental method of choice to gain insight into protein dynamics at near physiological conditions. Computationally, methods such as molecular dynamics and Normal Mode Analysis (NMA) allow the estimation of a protein's intrinsic flexibility based on a single protein structure. This work addresses, on a large scale, the relationships for proteins between the AlphaFold2 pLDDT metric, the observed dynamics in solution from NMR metrics, interpreted MD simulations, and the computed dynamics with NMA from single AlphaFold2 models and NMR ensembles. We observe that these metrics agree well for rigid residues that adopt a single well-defined conformation, which are clearly distinct from residues that exhibit dynamic behavior and adopt multiple conformations. This direct order/disorder categorisation is reflected in the correlations observed between the parameters, but becomes very limited when considering only the likely dynamic residues. The gradations of dynamics observed by NMR in flexible protein regions are therefore not represented by these computational approaches. Our results are interactively available for each protein from https://bio2byte.be/af_nmr_nma/.

A pressure-jump EPR system to monitor millisecond conformational exchange rates of spin-labeled proteins.

Site-directed spin labeling electron paramagnetic resonance (SDSL-EPR) using nitroxide spin labels is a well-established technology for mapping site-specific secondary and tertiary structure and for monitoring conformational changes in proteins of any degree of complexity, including membrane proteins, with high sensitivity. SDSL-EPR also provides information on protein dynamics in the timescale of ps-μs using continuous wave lineshape analysis and spin lattice relaxation time methods. However, the functionally important time domain of μs-ms, corresponding to large-scale protein motions, is inaccessible to those methods. To extend SDSL-EPR to the longer time domain, the perturbation method of pressure-jump relaxation is implemented. Here, we describe a complete high-pressure EPR system at Q-band for both static pressure and ms-timescale pressure-jump measurements on spin-labeled proteins. The instrument enables pressure jumps both up and down from any holding pressure, ranging from atmospheric pressure to the maximum pressure capacity of the system components (~3500 bar). To demonstrate the utility of the system, we characterize a local folding-unfolding equilibrium of T4 lysozyme. The results illustrate the ability of the system to measure thermodynamic and kinetic parameters of protein conformational exchange on the ms timescale.

IN SILICO BASED RE-ENGINEERING OF A COMPUTATIONALLY DESIGNED BIOSENSOR WITH ALTERED SIGNALLING MODE AND IMPROVED DYNAMIC RANGE.

A current challenge in the rational design of biomolecular sensors is the ability to custom design binding affinities and detection mode in silico. To this end, we re-engineered a previously reported computationally-designed fluorescent maltooligosaccharide (MOS)-detecting biosensor to both alter its ligand-binding affinity and to analyse the underlying sensing mechanism. The dynamic range of the biosensor was expanded through the computer aided introduction of a series of amino acid substitutions in the starting protein scaffold (MalX from Streptococcus pneumoniae), which generated a biosensor set with binding affinities spanning over five orders of magnitude. The impact of the introduced substitutions on the underlying mode of signal generation was assessed in silico using our previously reported Computational Identification of Non-disruptive Conjugation sites (CINC) pipeline. CINC utilizes molecular dynamics simulations and an in-house developed algorithm to examine and exploit the structural dynamics of a protein at amino acid-level resolution. Using CINC, we demonstrate that re-engineering of the MOS-detecting biosensor set resulted in sensors with two distinct output modes which differed based on local conformational changes at the fluorescently modified reporter position. These output modes were classified as "ligand-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the ligand-bound state), and "apo-sensing"-type biosensors (readout based on the tool sensing a unique conformation in the apo state). Together, these results demonstrate that structural dynamics at the individual amino acid residue level can be used as an engineer-able feature to rationally alter the fluorescence reporting properties of a biosensing device. Moving forward, the CINC workflow can also be adapted for the rational design of protein dynamic properties maximizing its utility as an in silico design platform for custom biomolecular tools.

Integrating AlphaFold pLDDT Scores into CABS-flex for enhanced protein flexibility simulations

CABS-flex is a well-established method for fast protein flexibility simulations, offering an effective balance between computational efficiency and accuracy in modeling protein dynamics. To further enhance its predictive capabilities, we propose incorporating AlphaFold's predicted Local Distance Difference Test (pLDDT) scores into CABS-flex simulations. The pLDDT scores, which reflect the confidence of AlphaFold's structural predictions, were integrated with secondary structure information to refine the restraint schemes used in the simulations. We tested this approach on the ATLAS database, which includes molecular dynamics (MD) simulations of nearly 1400 proteins. The results showed improved alignment of flexibility predictions with the MD data compared to previous restraint schemes. The integration of pLDDT scores also offers a new perspective on protein flexibility by incorporating structural confidence into the analysis. This development enhances the utility of CABS-flex for investigating protein dynamics and motion.

Genetically encoded intrabody probes for labeling and manipulating AMPA-type glutamate receptors

Tools for visualizing and manipulating protein dynamics in living cells are critical for understanding cellular function. Here we leverage recently available monoclonal antibody sequences to generate a set of affinity tags for labeling and manipulating AMPA-type glutamate receptors (AMPARs), which mediate nearly all excitatory neurotransmission in the central nervous system. These antibodies can be produced from heterologous cells for exogenous labeling applications or directly expressed in living neurons as intrabodies, where they bind their epitopes in the endoplasmic reticulum and co-traffic to the cell surface for visualization with cell impermeant fluorescent dyes. We show these reagents do not perturb AMPAR trafficking, function, mobility, or synaptic recruitment during plasticity and therefore can be used as probes for monitoring endogenous receptors in living neurons. We also adapt these reagents to deplete AMPARs from the cell surface by trapping them in the endoplasmic reticulum, providing a simple approach for loss of excitatory neurotransmission. The strategies outlined here serve as a template for generating similar reagents targeting diverse proteins as more antibody sequences become available.

MdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics

Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science.

HeMiTo-dynamics: a characterisation of mammalian prion toxicity using non-dimensionalisation, linear stability and perturbation analyses.

Prion-like proteins play crucial parts in biological processes in organisms ranging from yeast to humans. For instance, many neurodegenerative diseases are believed to be caused by the production of prion-like proteins in neural tissue. As such, understanding the dynamics of prion-like protein production is a vital step toward treating neurodegenerative disease. Mathematical models of prion-like protein dynamics show great promise as a tool for predicting disease trajectories and devising better treatment strategies for prion-related diseases. Herein, we investigate a generic model for prion-like dynamics consisting of a class of non-linear ordinary differential equations (ODEs), establishing constraints through a linear stability analysis that enforce the expected properties of mammalian prion-like toxicity. Furthermore, we identify that prion toxicity evolves through three distinct phases for which we provide analytical descriptions using perturbation analyses. Specifically, prion-toxicity is initially characterised by the healthy phase, where the dynamics are dominated by the healthy form of prions, thereafter the system enters the mixed phase, where both healthy and toxic prions interact, and lastly, the system enters the toxic phase, where toxic prions dominate, and we refer to these phases as HeMiTo-dynamics. These findings hold the potential to aid researchers in developing precise mathematical models for prion-like dynamics, enabling them to better understand underlying mechanisms and devise effective treatments for prion-related diseases.

Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes.

Collective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its nonlinear successor, time-lagged autoencoder (tAE), are widely used, they often struggle to capture protein dynamics due to interference from random fluctuations along fast DOFs. To address this issue, we propose a novel approach integrating discrete wavelet transform (DWT) with dimensionality reduction techniques. DWT effectively separates fast and slow motion in protein simulation trajectories by decoupling high- and low-frequency signals. Based on the trajectory after filtering out high-frequency signals, which corresponds to fast motion, tICA and tAE can accurately extract CVs representing slow DOFs, providing reliable insights into protein dynamics. Our method demonstrates superior performance in identifying CVs that distinguish metastable states compared to standard tICA and tAE, as validated through analyses of conformational changes of alanine dipeptide and tripeptide and folding of CLN025. Moreover, we show that DWT can be used to improve the performance of a variety of CV-finding algorithms by combining it with Deep-tICA, a cutting-edge CV-finding algorithm, to extract CVs for enhanced-sampling calculations. Given its negligible computational cost and remarkable ability to screen fast motion, we propose DWT as a "free lunch" for CV extraction, applicable to a wide range of CV-finding algorithms.

Interaction-Dependent Secondary Structure of Peptides in Biomolecular Condensates.

Biomolecular condensates provide a mechanism for compartmentalization of biomolecules in eukaryotic cells. These liquid-like condensates are formed via liquid-liquid phase separation, by a plethora of interactions, and can mediate several biological processes in healthy cells. Expansions of dipeptide repeat proteins, DPRs, in which arginine rich DPRs like poly-proline-arginine (PR), and poly-glycine-arginine (GR), partition RNA into condensates can however induce cell toxicity. Here, we use (GR)20 as a model for biological poly-GR and condense it using either excluded volume interactions with polyethylene glycol (PEG) as a crowder or direct electrostatic interactions with RNA oligomers. Using two-dimensional infrared (2D IR) spectroscopy, we observe that (GR)20 condensed through an excluded volume forms β-sheet structures, whereas (GR)20 condensed with RNA forms loops. We also investigate local hydrogen-bond dynamics in the condensate and compare the measurements with molecular dynamics simulations. Hydrogen bond lifetimes undergo a marked slowdown compared to dynamics in the dilute phase. This is representative of confined water within the percolated networks inside the condensate due to the interaction present in the condensate disrupting H-bond networks. Overall, our results show that both protein structure and dynamics are inherently dependent on the type of interactions that stabilize the condensates.

Systematic analysis of the relationship between fold-dependent flexibility and artificial intelligence protein structure prediction.

Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about complicated biological problems and empowered a new generation of structure-based hypothesis testing. It is well-recognized that proteins have a modular organization according to archetypal folds. However, it is yet to be determined if predicted structures are tuned to one conformation of flexible proteins or if they represent average conformations. Further, whether or not the answer is protein fold-dependent. Therefore, in this study, we analyzed 2878 proteins with at least ten distinct experimental structures available, from which we can estimate protein topological rigidity verses heterogeneity from experimental measurements. We found that AlphaFold v2 (AF2) predictions consistently return one specific form to high accuracy, with 99.68% of distinct folds (n = 623 out of 628) having an experimental structure within 2.5Å RMSD from a predicted structure. Yet, 27.70% and 10.82% of folds (174 and 68 out of 628 folds) have at least one experimental structure over 2.5Å and 5Å RMSD, respectively, from their AI-predicted structure. This information is important for how researchers apply and interpret the output of AF2 and similar tools. Additionally, it enabled us to score fold types according to how homogeneous versus heterogeneous their conformations are. Importantly, folds with high heterogeneity are enriched among proteins which regulate vital biological processes including immune cell differentiation, immune activation, and metabolism. This result demonstrates that a large amount of protein fold flexibility has already been experimentally measured, is vital for critical cellular processes, and is currently unaccounted for in structure prediction databases. Therefore, the structure-prediction revolution begets the protein dynamics revolution!