Protein Functional Dynamics Research Articles

Field-domain rapid-scan EPR at 240 GHz for studies of protein functional dynamics at room temperature

We present field-domain rapid-scan (RS) electron paramagnetic resonance (EPR) at 8.6T and 240GHz. To enable this technique, we upgraded a home-built EPR spectrometer with an FPGA-enabled digitizer and real-time processing software. The software leverages the Hilbert transform to recover the in-phase (I) and quadrature (Q) channels, and therefore the raw absorptive and dispersive signals, χ′ and χ′′, from their combined magnitude (I2+Q2). Averaging a magnitude is simpler than real-time coherent averaging and has the added benefit of permitting long-timescale signal averaging (up to at least 2.5×106 scans) because it eliminates the effects of source-receiver phase drift. Our rapid-scan (RS) EPR provides a signal-to-noise ratio that is approximately twice that of continuous wave (CW) EPR under the same experimental conditions, after scaling by the square root of acquisition time. We apply our RS EPR as an extension of the recently reported time-resolved Gd-Gd EPR (TiGGER) [Maity et al., 2023], which is able to monitor inter-residue distance changes during the photocycle of a photoresponsive protein through changes in the Gd-Gd dipolar couplings. RS, opposed to CW, returns field-swept spectra as a function of time with 10ms time resolution, and thus, adds a second dimension to the static field transients recorded by TiGGER. We were able to use RS TiGGER to track time-dependent and temperature-dependent kinetics of AsLOV2, a light-activated phototropin domain found in oats. The results presented here combine the benefits of RS EPR with the improved spectral resolution and sensitivity of Gd chelates at high magnetic fields. In the future, field-domain RS EPR at high magnetic fields may enable studies of other real-time kinetic processes with time resolutions that are otherwise difficult to access in the solution state.

Illuminating Protein Allostery by Chemically Accurate Contact Response Analysis (ChACRA).

Decoding allostery at the atomic level is essential for understanding the relationship between a protein's sequence, structure, and dynamics. Recently, we have shown that decomposing temperature responses of inter-residue contacts can reveal allosteric couplings and provide useful insight into the functional dynamics of proteins. The details of this Chemically Accurate Contact Response Analysis (ChACRA) are presented here along with its application to two well-known allosteric proteins. The first protein, IGPS, is a model of ensemble allostery that lacks clear structural differences between the active and inactive states. We show that the application of ChACRA reveals the experimentally identified allosteric coupling between effector and active sites of IGPS. The second protein, ATCase, is a classic example of allostery with distinct active and inactive structural states. Using ChACRA, we directly identify the most significant residue level interactions underlying the enzyme's cooperative behavior. Both test cases demonstrate the utility of ChACRA's unsupervised machine learning approach for dissecting allostery at the residue level.

PROTAC-induced Protein Functional 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 (BRD4BD1), 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 facilitate the arrangement of surface lysine residues of BRD4BD1 into the catalytic pocket of E2/ubiquitin 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 dynamic perspective on ligand induced protein degradation, providing insights to guide future PROTAC design endeavors.

Functional protein dynamics in a crystal.

Proteins are molecular machines and to understand how they work, we need to understand how they move. New pump-probe time-resolved X-ray diffraction methods open up ways to initiate and observe protein motions with atomistic detail in crystals on biologically relevant timescales. However, practical limitations of these experiments demands parallel development of effective molecular dynamics approaches to accelerate progress and extract meaning. Here, we establish robust and accurate methods for simulating dynamics in protein crystals, a nontrivial process requiring careful attention to equilibration, environmental composition, and choice of force fields. With more than seven milliseconds of sampling of a single chain, we identify critical factors controlling agreement between simulation and experiments and show that simulated motions recapitulate ligand-induced conformational changes. This work enables a virtuous cycle between simulation and experiments for visualizing and understanding the basic functional motions of proteins.

Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction.

Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may not be the optimal choice for elucidating transition mechanisms within low-dimensional representations, especially for intricate biosystems. Unraveling the association mechanism in such complex systems always necessitates permutations of several essential independent components or collective variables, a process that is inherently obscure and may require empirical knowledge for selection. To address these challenges, we have implemented an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN). This approach effectively generates low-dimensional configurational embeddings. The hierarchical application of this architecture, in conjunction with MSM, reveals global kinetic connectivity while identifying local conformational states. Consequently, our methodology establishes a multiscale mechanistic elucidation framework. Leveraging the benefits of the uniform sample distribution and a denoising approach, our model demonstrates robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area. The interpretability of hyperparameter selection and compatibility with downstream tasks are cross-validated across various simulation data sets, utilizing both computational evaluation metrics and experimental kinetic observables. Furthermore, the predicted Mcl1-BH3 association kinetics (0.76 s-1) is in close agreement with surface plasmon resonance experiments (0.12 s-1), affirming the plausibility of the identified pathway composed of representative conformations. We anticipate that the devised workflow will serve as a foundational framework for studying recognition patterns in complex biological systems. Its contributions extend to the exploration of protein functional dynamics and rational drug design, offering a potent avenue for advancing research in these domains.

Functional protein dynamics in a crystal

Functional protein dynamics in a crystal

Synergism between x-ray crystallography and NMR residual dipolar couplings in characterizing protein dynamics

The important role of structural dynamics in protein function is widely recognized. Thermal or B-factors and their anisotropy, seen in x-ray analysis of protein structures, report on the presence of atomic coordinate heterogeneity that can be attributed to motion. However, their quantitative evaluation in terms of protein dynamics by x-ray ensemble refinement remains challenging. NMR spectroscopy provides quantitative information on the amplitudes and time scales of motional processes. Unfortunately, with a few exceptions, the NMR data do not provide direct insights into the atomic details of dynamic trajectories. Residual dipolar couplings, measured by solution NMR, are very precise parameters reporting on the time-averaged bond-vector orientations and may offer the opportunity to derive correctly weighted dynamic ensembles of structures for cases where multiple high-resolution x-ray structures are available. Applications to the SARS-CoV-2 main protease, Mpro, and ubiquitin highlight this complementarity of NMR and crystallography for quantitative assessment of internal motions.

Functional Protein Dynamics Directly from Sequences.

The sequence correlations within a protein multiple sequence alignment are routinely being used to predict contacts within its structure, but here we point out that these data can also be used to predict a protein's dynamics directly. The elastic network protein dynamics models rely directly upon the contacts, and the normal modes of motion are obtained from the decomposition of the inverse of the contact map. To make the direct connection between sequence and dynamics, it is necessary to apply coarse-graining to the structure at the level of one point per amino acid, which has often been done, and protein coarse-grained dynamics from elastic network models has been highly successful, particularly in representing the large-scale motions of proteins that usually relate closely to their functions. The interesting implication of this is that it is not necessary to know the structure itself to obtain its dynamics and instead to use the sequence information directly to obtain the dynamics.

Exact reaction coordinates for functional protein dynamics: Flap opening in HIV-1 protease.

The primary goal of protein science is to understand how proteins function, which requires understanding the functional dynamics responsible for transitions between different functional structures of a protein. A central concept is the exact reaction coordinates that can determine the value of committor for any protein configuration, which provide optimal characterization of functional dynamics. Despite intensive efforts, identifying exact reaction coordinates in complex molecules remains a formidable challenge. Using the recently developed generalized work functional, we report the discovery of the exact reaction coordinates for an important functional process--the flap opening of HIV-1 protease. Our results show that this process has six reaction coordinates, each one is a linear combination of ∼240 backbone dihedrals, providing exact definition of collectivity and cooperativity in functional dynamics of proteins. Applying bias potential along each reaction coordinate can accelerate flap opening by 103 to 104 folds. The success in identifying the reaction coordinates of a protein with 198 residues represents a significant progress beyond that of the alanine dipeptide, currently the only other complex molecule for which the exact reaction coordinates for its conformational changes are known. Our results suggest that the generalized work functional might be the fundamental mechanical operator that controls protein dynamics.

Quantitative views of protein dynamics through the computational microscope.

The complexity of the energy landscape of proteins and subjectivity in choosing reaction coordinates pose significant hurdles in the quantitative characterization of functional protein dynamics using computational techniques. We have proposed the cumulative variance of coordinate fluctuations (CVCF) as a parameter free and energy landscape sensitive metric to identify sections of Molecular dynamics (MD) and Monte-Carlo (MC) trajectories which achieve Boltzmann sampling conditions. Our CVCF-trace analysis can provide quantitative estimates of the effective curvature and roughness of the energy landscape of proteins. We have applied these conceptual advances to compare the overall and directional flexibilities of multiple protein systems in monomeric form as well as within complexes. The results not only correlate well with existing experimental single molecule force spectroscopy data but also provide clear predictions which can be tested by such experiments. Moving beyond CVCF, we propose a mode evolution metric (MEM) to track the evolution of protein dynamics along MD trajectories. MEM enables us to deduce the dimensionality of protein motions dictated by the underlying energy landscape, thereby providing an objective means to identify reaction coordinates to describe dynamics. We will discuss improved biomolecular energy landscape sampling formulations based on our set of dynamical metrics.

Computation-aided novel epitope prediction by targeting spike protein's functional dynamics in Omicron

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High Pressure CPMG and CEST Reveal That Cavity Position Dictates Distinct Dynamic Disorder in the PP32 Repeat Protein.

Given the central role of conformational dynamics in protein function, it is essential to characterize the time scales and structures associated with these transitions. High pressure (HP) perturbation favors transitions to excited states because they typically occupy a smaller molar volume, thus facilitating characterization of conformational dynamics. Repeat proteins, with their straightforward architecture, provide good models for probing the sequence dependence of protein conformational dynamics. Investigations of chemical exchange by 15N CPMG relaxation dispersion analysis revealed that introduction of a cavity via substitution of isoleucine 7 by alanine in the N-terminal capping motif of the pp32 leucine-rich repeat protein leads to pressure-dependent conformational exchange detected on the 500 μs-2 ms CPMG time scale. Exchange amplitude decreased from the N- to C-terminus, revealing a gradient of conformational exchange across the protein. In contrast, introduction of a cavity in the central core of pp32 via the L60A mutation led to pressure-induced exchange on a slower (>2 ms) time scale detected by 15N-CEST analysis. Excited state 15N chemical shifts indicated that in the excited state detected by HP CEST, the N-terminal region is mostly unfolded, while the core retains native-like structure. These HP chemical exchange measurements reveal that cavity position dictates exchange on distinct time scales, highlighting the subtle, yet central role of sequence in determining protein conformational dynamics.

A Rigorous Method for Identifying a One-Dimensional Reaction Coordinate in Complex Molecules.

Understanding the mechanism of functional protein dynamics is critical to understanding protein functions. Reaction coordinate (RC) is a central topic in protein dynamics, and the grail is to find the one-dimensional RC (1D-RC) that can fully determine the value of a committor (i.e., the reaction probability in configuration space) for any protein configuration. We present a new method that, for the first time, uses a fundamental mechanical operator, the generalized work functional, to identify the rigorous 1D-RC in complex molecules. For a prototypical biomolecular isomerization reaction, the 1D-RC identified by the current method can determine the committor with an accuracy far exceeding what was achieved by previous methods. This method only requires modest computational cost and can be readily applied to large molecules. Most importantly, the generalized work functional is the physical determinant of the collectivity in functional protein dynamics and provides a tentative roadmap that connects the structure of a protein to its function.

Photophysics of the Blue Light Using Flavin Domain

Light activated proteins are at the heart of photobiology and optogenetics, so there is wide interest in understanding the mechanisms coupling optical excitation to protein function. In addition, such light activated proteins provide unique insights into the real-time dynamics of protein function. Using pump-probe spectroscopy, the function of a photoactive protein can be initiated by a sub-100 fs pulse of light, allowing subsequent protein dynamics to be probed from femtoseconds to milliseconds and beyond. Among the most interesting photoactive proteins are the blue light using flavin (BLUF) domain proteins, which regulate the response to light of a wide range of bacterial and some euglenoid processes. The photosensing mechanism of BLUF domains has long been a subject of debate. In contrast to other photoactive proteins, the electronic and nuclear structure of the chromophore (flavin) is the same in dark- and light-adapted states. Thus, the driving force for photoactivity is unclear.To address this question requires real-time observation of both chromophore excited state processes and their effect on the structure and dynamics of the surrounding protein matrix. In this Account we describe how time-resolved infrared (IR) experiments, coupled with chemical biology, provide important new insights into the signaling mechanism of BLUF domains. IR measurements are sensitive to changes in both chromophore electronic structure and protein hydrogen bonding interactions. These contributions are resolved by isotope labeling of the chromophore and protein separately. Further, a degree of control over BLUF photochemistry is achieved through mutagenesis, while unnatural amino acid substitution allows us to both fine-tune the photochemistry and time resolve protein dynamics with spatial resolution.Ultrafast studies of BLUF domains reveal non-single-exponential relaxation of the flavin excited state. That relaxation leads within one nanosecond to the original flavin ground state bound in a modified hydrogen-bonding network, as seen in transient and steady-state IR spectroscopy. The change in H-bond configuration arises from formation of an unusual enol (imine) form of a critical glutamine residue. The dynamics observed, complemented by quantum mechanical calculations, suggest a unique sequential electron then double proton transfer reaction as the driving force, followed by rapid reorganization in the binding site and charge recombination. Importantly, studies of several BLUF domains reveal an unexpected diversity in their dynamics, although the underlying structure appears highly conserved. It is suggested that this diversity reflects structural dynamics in the ground state at standard temperature, leading to a distribution of structures and photochemical outcomes. Time resolved IR measurements were extended to the millisecond regime for one BLUF domain, revealing signaling state formation on the microsecond time scale. The mechanism involves reorganization of a β-sheet connected to the chromophore binding pocket via a tryptophan residue. The potential of site-specific labeling amino acids with IR labels as a tool for probing protein structural dynamics was demonstrated.In summary, time-resolved IR studies of BLUF domains (along with related studies at visible wavelengths and quantum and molecular dynamics calculations) have resolved the photoactivation mechanism and real-time dynamics of signaling state formation. These measurements provide new insights into protein structural dynamics and will be important in optimizing the potential of BLUF domains in optobiology.

Recognition of the ligand-induced spatiotemporal residue pair pattern of β2-adrenergic receptors using 3-D residual networks trained by the time series of protein distance maps

G protein-coupled receptors (GPCRs) are promising drug targets because they play a large role in physiological processes by modulating diverse signaling pathways in the human body. The GPCR-mediated signaling pathways are regulated by four types of ligands—agonists, neutral antagonists, partial agonists, and inverse agonists. Once each type of ligand is bound to the binding site, it activates, deactivates, or does not perturb signaling by shifting the conformational ensemble of GPCRs. Predicting the ligand's effect on the conformation at the binding moment could be a powerful screening tool for rational GPCR drug design. Here, we detected conformational differences by capturing the spatiotemporal residue pair pattern of the ligand-bound β2-adrenergic receptor (β2AR) using a 3-dimensional residual network, 3D-ResNets. The network was trained with the time series of protein distance maps extracted from hundreds of molecular dynamics (MD) simulation trajectories of ten β2AR-ligand complexes. The MD system was constructed with a lipid bilayer embedded in an inactive β2AR X-ray crystal structure and solvated with explicit water molecules. To train the network, three hyperparameters were tested, and it was found that the number of MD trajectories in the training set significantly affected the model's accuracy. The classification of agonists and neutral antagonists was successful, but inverse agonists were not. Between the agonists and antagonists, different residue pair patterns were spotted on the extracellular loop segment. This result demonstrates the potential application of a 3-D neural network in GPCR drug screening, as well as an analysis tool for protein functional dynamics

Protein functional dynamics from the rigorous global analysis of DEER data: Conditions, components, and conformations.

The potential of spin labeling to reveal the dynamic dimension of macromolecules has been recognized since the dawn of the methodology in the 1960s. However, it was the development of pulsed electron paramagnetic resonance spectroscopy to detect dipolar coupling between spin labels and the availability of turnkey instrumentation in the 21st century that realized the full promise of spin labeling. Double electron-electron resonance (DEER) spectroscopy has seen widespread applications to channels, transporters, and receptors. In these studies, distance distributions between pairs of spin labels obtained under different biochemical conditions report the conformational states of macromolecules, illuminating the key movements underlying biological function. These experimental studies have spurred the development of methods for the rigorous analysis of DEER spectroscopic data along with methods for integrating these distributions into structural models. In this tutorial, we describe a model-based approach to obtaining a minimum set of components of the distance distribution that correspond to functionally relevant protein conformations with a set of fractional amplitudes that define the equilibrium between these conformations. Importantly, we review and elaborate on the error analysis reflecting the uncertainty in the various parameters, a critical step in rigorous structural interpretation of the spectroscopic data.

Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.

Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.

Single-Molecule Study of Thermomyces lanuginosus Lipase in a Detergency Application System Reveals Diffusion Pattern Remodeling by Surfactants and Calcium.

Lipases comprise one of the major enzyme classes in biotechnology with applications within, e.g., baking, brewing, biocatalysis, and the detergent industry. Understanding the mechanisms of lipase function and regulation is therefore important to facilitate the optimization of their function by protein engineering. Advances in single-molecule studies in model systems have provided deep mechanistic insights on lipase function, such as the existence of functional states, their dependence on regulatory cues, and their correlation to activity. However, it is unclear how these observations translate to enzyme behavior in applied settings. Here, single-molecule tracking of individual Thermomyces lanuginosus lipase (TLL) enzymes in a detergency application system allowed real-time direct observation of spatiotemporal localization, and thus diffusional behavior, of TLL enzymes on a lard substrate. Parallelized imaging of thousands of individual enzymes allowed us to observe directly the existence and quantify the abundance and interconversion kinetics between three diffusional states that we recently provided evidence to correlate with function. We observe redistribution of the enzyme's diffusional pattern at the lipid-water interface as well as variations in binding efficiency in response to surfactants and calcium, demonstrating that detergency effectors can drive the sampling of lipase functional states. Our single-molecule results combined with ensemble activity assays and enzyme surface binding efficiency readouts allowed us to deconvolute how application conditions can significantly alter protein functional dynamics and/or surface binding, both of which underpin enzyme performance. We anticipate that our results will inspire further efforts to decipher and integrate the dynamic nature of lipases, and other enzymes, in the design of new biotechnological solutions.

HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps.

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.

Spotlight on the Ballet of Proteins: The Structural Dynamic Properties of Proteins Illuminated by Solution NMR.

Solution NMR spectroscopy is a unique and powerful technique that has the ability to directly connect the structural dynamics of proteins in physiological conditions to their activity and function. Here, we summarize recent studies in which solution NMR contributed to the discovery of relationships between key dynamic properties of proteins and functional mechanisms in important biological systems. The capacity of NMR to quantify the dynamics of proteins over a range of time scales and to detect lowly populated protein conformations plays a critical role in its power to unveil functional protein dynamics. This analysis of dynamics is not only important for the understanding of biological function, but also in the design of specific ligands for pharmacologically important proteins. Thus, the dynamic view of structure provided by NMR is of importance in both basic and applied biology.