Protein dynamics and function from solution state NMR spectroscopy

Cryo-EM and Molecular Dynamics Simulations Reveal Hidden Conformational Dynamics Controlling Ammonia Transport in Human Asparagine Synthetase

How motions in enzymes might be linked to catalytic function is of considerable general interest. Recent advances in X-ray crystallography and cryogenic electron microscopy offer the promise of elucidating functionally relevant motions in proteins that are not easily amenable to study by other biophysical methods. Here we use 3D variability analysis (3DVA) on cryo-EM maps for wild type (WT) human asparagine synthetase (ASNS) and the R142I ASNS variant to identify conformational changes in the Arg-142 side chain, which mediates the formation of a catalytically relevant intramolecular tunnel. Our 3DVA results for WT ASNS are consistent with independent molecular dynamics (MD) simulations on a model generated from the X-ray structure of human ASNS. Moreover, MD simulations of computational models for the ASNS/β-aspartyl-AMP/MgPPi and R142I/β-aspartyl-AMP/MgPPi ternary complexes, suggest that the structural integrity of the tunnel is impaired in the R142I variant when β-aspartyl-AMP is present in the synthetase active site. The kinetic properties of the R142I ASNS variant support the proposed function of Arg-142. These studies illustrate the power of cryo-EM to identify localized motions and dissect the conformational landscape of large proteins. When combined with MD simulations, 3DVA is a powerful approach to understanding how conformational dynamics might regulate function in multi-domain enzymes possessing multiple active sites.

Including the Ensemble of Unstructured Conformations in the Analysis of Protein's Native State by High-Pressure NMR Spectroscopy

The analysis of pressure induced changes in the chemical shift of proteins allows statements on structural fluctuations proteins exhibit at ambient pressure. The inherent issue of separating general pressure effects from structural related effects on the pressure dependence of chemical shifts has so far been addressed by considering the characteristics of random coil peptides on increasing pressure. In this work, chemically and pressure denatured states of the cold shock protein B from Bacillus subtilis (BsCspB) have been assigned in 2D 1H-15N HSQC NMR spectra and their dependence on increasing hydrostatic pressure has been evaluated. The pressure denatured polypeptide chain has been used to separate general from structural related effects on 1H and 15N chemical shifts of native BsCspB and the implications on the interpretation of pressure induced changes in the chemical shift regarding the structure of BsCspB are discussed. It has been found that the ensemble of unstructured conformations of BsCspB shows different responses to increasing pressure than random coil peptides do. Thus, the approach used for considering the general effects that arise when hydrostatic pressure increases changes the structural conclusions that are drawn from high pressure NMR spectroscopic experiments that rely on the analysis of chemical shifts.

Systematic QM/MM Study for Predicting 31P NMR Chemical Shifts of Adenosine Nucleotides in Solution and Stages of ATP Hydrolysis in a Protein Environment

NMR (nuclear magnetic resonance) spectroscopy allows for important atomistic insights into the structure and dynamics of biological macromolecules; however, reliable assignments of experimental spectra are often difficult. Herein, quantum mechanical/molecular mechanical (QM/MM) calculations can provide crucial support. A major problem for the simulations is that experimental NMR signals are time-averaged over much longer time scales, and since computed chemical shifts are highly sensitive to local changes in the electronic and structural environment, sufficiently large averages over representative structural ensembles are essential. This entails high computational demands for reliable simulations. For NMR measurements in biological systems, a nucleus of major interest is 31P since it is both highly present (e.g., in nucleic acids) and easily observable. The focus of our present study is to develop a robust and computationally cost-efficient framework for simulating 31P NMR chemical shifts of nucleotides. We apply this scheme to study the different stages of the ATP hydrolysis reaction catalyzed by p97. Our methodology is based on MM molecular dynamics (MM-MD) sampling, followed by QM/MM structure optimizations and NMR calculations. Overall, our study is one of the most comprehensive QM-based 31P studies in a protein environment and the first to provide computed NMR chemical shifts for multiple nucleotide states in a protein environment. This study sheds light on a process that is challenging to probe experimentally and aims to bridge the gap between measured and calculated NMR spectroscopic properties.

Prediction of order parameters based on protein NMR structure ensemble and machine learning

The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. The most used NMR approach to study fast protein dynamics is the model free method, which uses order parameter S2 to describe the amplitude of the internal motion of local group. However, to obtain order parameter through NMR experiments is quite complex and lengthy. In this paper, we present a machine learning approach for predicting backbone 1H-15N order parameters based on protein NMR structure ensemble. A random forest model is used to learn the relationship between order parameters and structural features. Our method achieves high accuracy in predicting backbone 1H-15N order parameters for a test dataset of 10 proteins, with a Pearson correlation coefficient of 0.817 and a root-mean-square error of 0.131.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Fluorescence-Based Protein Stability Monitoring-A Review

Proteins are large biomolecules with a specific structure that is composed of one or more long amino acid chains. Correct protein structures are directly linked to their correct function, and many environmental factors can have either positive or negative effects on this structure. Thus, there is a clear need for methods enabling the study of proteins, their correct folding, and components affecting protein stability. There is a significant number of label-free methods to study protein stability. In this review, we provide a general overview of these methods, but the main focus is on fluorescence-based low-instrument and -expertise-demand techniques. Different aspects related to thermal shift assays (TSAs), also called differential scanning fluorimetry (DSF) or ThermoFluor, are introduced and compared to isothermal chemical denaturation (ICD). Finally, we discuss the challenges and comparative aspects related to these methods, as well as future opportunities and assay development directions.

Elucidating Dynamics of Adenylate Kinase from Enzyme Opening to Ligand Release

This study explores ligand-driven conformational changes in adenylate kinase (AK), which is known for its open-to-close conformational transitions upon ligand binding and release. By utilizing string free energy simulations, we determine the free energy profiles for both enzyme opening and ligand release and compare them with profiles from the apoenzyme. Results reveal a three-step ligand release process, which initiates with the opening of the adenosine triphosphate-binding subdomain (ATP lid), followed by ligand release and concomitant opening of the adenosine monophosphate-binding subdomain (AMP lid). The ligands then transition to nonspecific positions before complete dissociation. In these processes, the first step is energetically driven by ATP lid opening, whereas the second step is driven by ATP release. In contrast, the AMP lid opening and its ligand release make minor contributions to the total free energy for enzyme opening. Regarding the ligand binding mechanism, our results suggest that AMP lid closure occurs via an induced-fit mechanism triggered by AMP binding, whereas ATP lid closure follows conformational selection. This difference in the closure mechanisms provides an explanation with implications for the debate on ligand-driven conformational changes of AK. Additionally, we determine an X-ray structure of an AK variant that exhibits significant rearrangements in the stacking of catalytic arginines, explaining its reduced catalytic activity. In the context of apoenzyme opening, the sequence of events is different. Here, the AMP lid opens first while the ATP lid remains closed, and the free energy associated with ATP lid opening varies with orientation, aligning with the reported AK opening and closing rate heterogeneity. Finally, this study, in conjunction with our previous research, provides a comprehensive view of the intricate interplay between various structural elements, ligands, and catalytic residues that collectively contribute to the robust catalytic power of the enzyme.

Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts.

Experimental NOE, Chemical Shift, and Proline Isomerization Data Provide Detailed Insights into Amelotin Oligomerization

Amelotin is an intrinsically disordered protein (IDP) rich in Pro residues and is involved in hydroxyapatite mineralization. It rapidly oligomerizes under physiological conditions of pH and pressure but reverts to its monomeric IDP state at elevated pressure. We identified a 105-residue segment of the protein that becomes ordered upon oligomerization, and we used pressure-jump NMR spectroscopy to measure long-range NOE contacts that exist exclusively in the oligomeric NMR-invisible state. The kinetics of oligomerization and dissociation were probed at the residue-specific level, revealing that the oligomerization process is initiated in the C-terminal half of the segment. Using pressure-jump NMR, the degree of order in the oligomer at the sites of Pro residues was probed by monitoring changes in cis/trans equilibria relative to the IDP state after long-term equilibration under oligomerizing conditions. Whereas most Pro residues revert to trans in the oligomeric state, Pro-49 favors a cis configuration and three Pro residues retain an unchanged cis fraction, pointing to their local lack of order in the oligomeric state. NOE contacts and secondary 13C chemical shifts in the oligomeric state indicate the presence of an 11-residue α-helix, preceded by a small intramolecular antiparallel β-sheet, with slower formation of long-range intermolecular interactions to N-terminal residues. Although none of the models generated by AlphaFold2 for the amelotin monomer was consistent with experimental data, subunits of a hexamer generated by AlphaFold-Multimer satisfied intramolecular NOE and chemical shift data and may provide a starting point for developing atomic models for the oligomeric state.

Protein aggregation and neurodegenerative disease: Structural outlook for the novel therapeutics

Before the controversial approval of humanized monoclonal antibody lecanemab, which binds to the soluble amyloid-β protofibrils, all the treatments available earlier, for Alzheimer's disease (AD) were symptomatic. The researchers are still struggling to find a breakthrough in AD therapeutic medicine, which is partially attributable to lack in understanding of the structural information associated with the intrinsically disordered proteins and amyloids. One of the major challenges in this area of research is to understand the structural diversity of intrinsically disordered proteins under in vitro conditions. Therefore, in this review, we have summarized the in vitro applications of biophysical methods, which are aimed to shed some light on the heterogeneity, pathogenicity, structures and mechanisms of the intrinsically disordered protein aggregates associated with proteinopathies including AD. This review will also rationalize some of the strategies in modulating disease-relevant pathogenic protein entities by small molecules using structural biology approaches and biophysical characterization. We have also highlighted tools and techniques to simulate the in vivo conditions for native and cytotoxic tau/amyloids assemblies, urge new chemical approaches to replicate tau/amyloids assemblies similar to those in vivo conditions, in addition to designing novel potential drugs.

NMR Relaxation Experiments Probe Monomer-Fibril Interaction and Identify Critical Interacting Residues Responsible for Distinct Tau Fibril Morphologies

Tau aggregation is governed by secondary processes, a major pathological pathway for tau protein fibril propagation, yet its molecular mechanism remains unknown. This work uses saturation transfer and lifetime line-broadening experiments to identify the critical residues involved in these secondary processes. Distinct residue-specific NMR relaxation parameters were obtained for the truncated three repeat tau construct (K19) in equilibrium with structurally different, self-aggregated (saK19) or heparin-induced (hK19) fibrils. The interacting residues are restricted to R3 repeat for hK19 and to R3, R4, and R' repeats for saK19 fibrils. Furthermore, the relaxation profiles of tau monomers in equilibrium with the structurally comparable, in vitro pathological fibrils (tauAD and tauCTE) were similar but distinct from hK19 or saK19 fibrils. Thus, residue-specific relaxation identifies the important residues involved in the binding of monomers to the fibrils. The relaxation profile of the monomers in equilibrium with the NMR invisible fibril seeds potentially distinguishes the distinct structures of tau fibrils.

Refinement of multiconformer ensemble models from multi-temperature X-ray diffraction data

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363 K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Refinement of Multiconformer Ensemble Models from Multi-temperature X-ray Diffraction Data

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Engineering the Active Site Lid Dynamics to Improve the Catalytic Efficiency of Yeast Cytosine Deaminase

Conformational dynamics is important for enzyme catalysis. However, engineering dynamics to achieve a higher catalytic efficiency is still challenging. In this work, we develop a new strategy to improve the activity of yeast cytosine deaminase (yCD) by engineering its conformational dynamics. Specifically, we increase the dynamics of the yCD C-terminal helix, an active site lid that controls the product release. The C-terminal is extended by a dynamical single α-helix (SAH), which improves the product release rate by up to ~8-fold, and the overall catalytic rate kcat by up to ~2-fold. It is also shown that the kcat increase is due to the favorable activation entropy change. The NMR H/D exchange data indicate that the conformational dynamics of the transition state analog complex increases as the helix is extended, elucidating the origin of the enhanced catalytic entropy. This study highlights a novel dynamics engineering strategy that can accelerate the overall catalysis through the entropy-driven mechanism.

NMR Methods to Study the Dynamics of SH2 Domain-Phosphopeptide Complexes

Nuclear magnetic resonance (NMR) spectroscopy is the method of choice for studying the dynamics of biological macromolecules in solution. By exploiting the intricate interplay between the effects of protein motion (both overall rotational diffusion and internal mobility) and nuclear spin relaxation, NMR allows molecular motion to be probed at atomic resolution over a wide range of timescales, including picosecond (bond vibrations and methyl-group rotations), nanosecond (loop motions and rotational diffusion), and microsecond-millisecond (ligand binding, allostery). In this chapter, we describe different NMR pulse schemes (R1, R1ρ, heteronuclear NOE, and CPMG relaxation dispersion) to characterize the dynamics of SH2 domains. As an example, we use the N-SH2 domain of protein tyrosine phosphatase SHP2 in complex with two phosphopeptides derived from immune checkpoint receptor PD-1 (ITIM and ITSM).

Protein dynamics: The future is bright and complicated!

Biological life depends on motion, and this manifests itself in proteins that display motion over a formidable range of time scales spanning from femtoseconds vibrations of atoms at enzymatic transition states, all the way to slow domain motions occurring on micro to milliseconds. An outstanding challenge in contemporary biophysics and structural biology is a quantitative understanding of the linkages among protein structure, dynamics, and function. These linkages are becoming increasingly explorable due to conceptual and methodological advances. In this Perspective article, we will point toward future directions of the field of protein dynamics with an emphasis on enzymes. Research questions in the field are becoming increasingly complex such as the mechanistic understanding of high-order interaction networks in allosteric signal propagation through a protein matrix, or the connection between local and collective motions. In analogy to the solution to the "protein folding problem," we argue that the way forward to understanding these and other important questions lies in the successful integration of experiment and computation, while utilizing the present rapid expansion of sequence and structure space. Looking forward, the future is bright, and we are in a period where we are on the doorstep to, at least in part, comprehend the importance of dynamics for biological function.

Bottom-up Coarse-Graining: Principles and Perspectives

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

Dynamic regulation of Zn(II) sequestration by calgranulin C

Calgranulin C performs antimicrobial activity in the human immune response by sequestering Zn(II). This biological function is afforded with the aid of two structurally distinct Ca(II)-binding EF hand motifs, wherein one of which bears an unusual amino acid sequence. Here, we utilize solution state NMR relaxation measurements to investigate the mechanism of Ca(II)-modulated enhancement of Zn(II) sequestration by calgranulin C. Using C13 /N15 CPMG dispersion experiments we have measured pH-dependent major and minor state populations exchanging on micro-to-millisecond timescale. This conformational exchange takes place exclusively in the Ca(II)-bound state and can be mapped to residues located in the EF-I loop and the linker between the tandem EF hands. Molecular dynamics (MD) simulations spanning nano-to-microsecond timescale offer insights into the role of pH-dependent electrostatic interactions in EF-hand dynamics. Our results suggest a pH-regulated dynamic equilibrium of conformations that explore a range of "closed" and partially "open" sidechain configurations within the Zn(II) binding site. We propose a novel mechanism by which Ca(II) binding to a non-canonical EF loop regulates its flexibility and tunes the antimicrobial activity of calgranulin C.

ssPINE: Probabilistic Algorithm for Automated Chemical Shift Assignment of Solid-State NMR Data from Complex Protein Systems

The heightened dipolar interactions in solids render solid-state NMR (ssNMR) spectra more difficult to interpret than solution NMR spectra. On the other hand, ssNMR does not suffer from severe molecular weight limitations like solution NMR. In recent years, ssNMR has undergone rapid technological developments that have enabled structure-function studies of increasingly larger biomolecules, including membrane proteins. Current methodology includes stable isotope labeling schemes, non-uniform sampling with spectral reconstruction, faster magic angle spinning, and innovative pulse sequences that capture different types of interactions among spins. However, computational tools for the analysis of complex ssNMR data from membrane proteins and other challenging protein systems have lagged behind those for solution NMR. Before a structure can be determined, thousands of signals from individual types of multidimensional ssNMR spectra of samples, which may have differing isotopic composition, must be recognized, correlated, categorized, and eventually assigned to atoms in the chemical structure. To address these tedious steps, we have developed an automated algorithm for ssNMR spectra called "ssPINE". The ssPINE software accepts the sequence of the protein plus peak lists from a variety of ssNMR experiments as inputs and offers automated backbone and side-chain assignments. The alpha version of ssPINE, which we describe here, is freely available through a web submission form.

Fundamental Redesign of the TIGER2hs Kernel to Address Severe Parameter Sensitivity

Replica exchange molecular dynamics simulations are one of the most popular approaches to enhance conformational sampling of molecular systems. Applications range from protein folding to protein-protein or other host-guest interactions, as well as binding free energy calculations. While these methods are computationally expensive, highly accurate results can be obtained. We recently developed TIGER2hs, an improved version of the temperature intervals with global exchange of replicas (TIGER2) algorithm. This method combines the replica-based enhanced sampling in an explicit solvent with a hybrid solvent energy evaluation. During the exchange attempts, bulk water is replaced by an implicit solvent model, allowing sampling with significantly less replicas than parallel tempering (REMD). This enables accurate enhanced sampling calculations with only a fraction of computational resources compared to REMD. Our latest results highlight several issues with sampling imbalance and parameter sensitivity within the original TIGER2 exchange algorithms that affect the overall state populations. A high sensitivity on replica number and maximum temperature is eliminated by changing to a pairwise exchange kernel (PE) without additional sorting. Simulations are controlled by adjusting the average temperature change per exchange ⟨ΔT/χ⟩ to below 30 K to mimic a controlled temperature mixing of replicas similar to REMD. Thus, this parameter provides an applicable property for selecting combinations of replica number and maximum temperature to adjust simulations for best accuracy, with flexible resource investment. This increases the robustness of the method and ensures results in excellent agreement with REMD, as demonstrated for three different peptides.

Solution structure of recombinant Pvfp-5β reveals insights into mussel adhesion

Some marine organisms can resist to aqueous tidal environments and adhere tightly on wet surface. This behavior has raised increasing attention for potential applications in medicine, biomaterials, and tissue engineering. In mussels, adhesive forces to the rock are the resultant of proteinic fibrous formations called byssus. We present the solution structure of Pvfp-5β, one of the three byssal plaque proteins secreted by the Asian green mussel Perna viridis, and the component responsible for initiating interactions with the substrate. We demonstrate that Pvfp-5β has a stably folded structure in agreement with the presence in the sequence of two EGF motifs. The structure is highly rigid except for a few residues affected by slow local motions in the µs-ms time scale, and differs from the model calculated by artificial intelligence methods for the relative orientation of the EGF modules, which is something where computational methods still underperform. We also show that Pvfp-5β is able to coacervate even with no DOPA modification, giving thus insights both for understanding the adhesion mechanism of adhesive mussel proteins, and developing of biomaterials.

A Single-Molecule Strategy to Capture Non-native Intramolecular and Intermolecular Protein Disulfide Bridges

Non-native disulfide bonds are dynamic covalent bridges that form post-translationally between two cysteines within the same protein (intramolecular) or with a neighboring protein (intermolecular), frequently due to changes in the cellular redox potential. The reversible formation of non-native disulfides is intimately linked to alterations in protein function; while they can provide a mechanism to protect against cysteine overoxidation, they are also involved in the early stages of protein multimerization, a hallmark of several protein aggregation diseases. Yet their identification using current protein chemistry technology remains challenging, mainly because of their fleeting reactivity. Here, we use single-molecule spectroscopy AFM and molecular dynamics simulations to capture both intra- and intermolecular disulfide bonds in γD-crystallin, a cysteine-rich, structural human lens protein involved in age-related eye cataracts. Our approach showcases the power of mechanical force as a conformational probe in dynamically evolving proteins and presents a platform to detect non-native disulfide bridges with single-molecule resolution.

Recent Advances in Investigating Functional Dynamics of Chromatin

Dynamics spanning the picosecond-minute time domain and the atomic-subcellular spatial window have been observed for chromatin in vitro and in vivo. The condensed organization of chromatin in eukaryotic cells prevents regulatory factors from accessing genomic DNA, which requires dynamic stabilization and destabilization of structure to initiate downstream DNA activities. Those processes are achieved through altering conformational and dynamic properties of nucleosomes and nucleosome–protein complexes, of which delineating the atomistic pictures is essential to understand the mechanisms of chromatin regulation. In this review, we summarize recent progress in determining chromatin dynamics and their modulations by a number of factors including post-translational modifications (PTMs), incorporation of histone variants, and binding of effector proteins. We focus on experimental observations obtained using high-resolution techniques, primarily including nuclear magnetic resonance (NMR) spectroscopy, Förster (or fluorescence) resonance energy transfer (FRET) microscopy, and molecular dynamics (MD) simulations, and discuss the elucidated dynamics in the context of functional response and relevance.

Integrating dynamics into enzyme engineering

Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.

Atomic-resolution chemical characterization of (2x)72-kDa tryptophan synthase via four- and five-dimensional 1H-detected solid-state NMR

NMR chemical shifts provide detailed information on the chemical properties of molecules, thereby complementing structural data from techniques like X-ray crystallography and electron microscopy. Detailed analysis of protein NMR data, however, often hinges on comprehensive, site-specific assignment of backbone resonances, which becomes a bottleneck for molecular weights beyond 40 to 45 kDa. Here, we show that assignments for the (2x)72-kDa protein tryptophan synthase (665 amino acids per asymmetric unit) can be achieved via higher-dimensional, proton-detected, solid-state NMR using a single, 1-mg, uniformly labeled, microcrystalline sample. This framework grants access to atom-specific characterization of chemical properties and relaxation for the backbone and side chains, including those residues important for the catalytic turnover. Combined with first-principles calculations, the chemical shifts in the β-subunit active site suggest a connection between active-site chemistry, the electrostatic environment, and catalytically important dynamics of the portal to the β-subunit from solution.

Double Mutant of Chymotrypsin Inhibitor 2 Stabilized through Increased Conformational Entropy

The conformational heterogeneity of a folded protein can affect not only its function but also stability and folding. We recently discovered and characterized a stabilized double mutant (L49I/I57V) of the protein CI2 and showed that state-of-the-art prediction methods could not predict the increased stability relative to the wild-type protein. Here, we have examined whether changed native-state dynamics, and resulting entropy changes, can explain the stability changes in the double mutant protein, as well as the two single mutant forms. We have combined NMR relaxation measurements of the ps-ns dynamics of amide groups in the backbone and the methyl groups in the side chains with molecular dynamics simulations to quantify the native-state dynamics. The NMR experiments reveal that the mutations have different effects on the conformational flexibility of CI2: a reduction in conformational dynamics (and entropy estimated from this) of the native state of the L49I variant correlates with its decreased stability, while increased dynamics of the I57V and L49I/I57V variants correlates with their increased stability. These findings suggest that explicitly accounting for changes in native-state entropy might be needed to improve the predictions of the effect of mutations on protein stability.

Dipolar Order Parameters in Large Systems With Fast Spinning

Order parameters are a useful tool for quantifying amplitudes of molecular motions. Here we measure dipolar order parameters by recoupling heteronuclear dipole-dipole couplings under fast spinning. We apply symmetry based recoupling methods to samples spinning under magic angle at 60 kHz by employing a variable flip angle compound inversion pulse. We validate the methods by measuring site-specific ¹⁵N-¹H order parameters of a microcrystalline protein over a small temperature range and the same protein in a large, precipitated complex with antibody. The measurements of the order parameters in the complex are consistent with the observed protein undergoing overall motion within the assembly.

Toward a Computational NMR Procedure for Modeling Dipeptide Side-Chain Conformation

Theoretical relationships between the vicinal spin–spin coupling constants (SSCCs) and the χ₁ torsion angles have been studied to predict the conformations of protein side chains. An efficient computational procedure is developed to obtain the conformation of dipeptides through theoretical and experimental SSCCs, Karplus equations, and quantum chemistry methods, and it is applied to three aliphatic hydrophobic residues (Val, Leu, and Ile). Three models are proposed: unimodal-static, trimodal-static-stepped, and trimodal-static-trigonal, where the most important factors are incorporated (coupled nuclei, nature and orientation of the substituents, and local geometric properties). Our results are validated by comparison with NMR and X-ray empirical data described in the literature, obtaining successful results on the 29 residues considered. Using out trimodal residue treatment, it is possible to detect and resolve residues with a simple conformation and those with two or three staggered conformers. In four residues, a deeper analysis explains that they do not have a unique conformation and that the population of each conformation plays an important role.

Characterization of backbone dynamics using solution NMR spectroscopy to discern the functional plasticity of structurally analogous proteins

The comprehensive delineation of inherent dynamic motions embedded in proteins, which can be crucial for their functional repertoire, is often essential yet remains poorly understood in the majority of cases. In this protocol, we outline detailed descriptions of the necessary steps for employing solution NMR spectroscopy for the in-depth amino acid level understanding of backbone dynamics of proteins. We describe the application of the protocol on the structurally analogous Tudor domains with disparate functionalities as a model system. For complete details on the use and execution of this protocol, please refer to Kawale and Burmann (2021).

A drug discovery toolbox for Nuclear Magnetic Resonance (NMR) characterization of ligands and their targets

Information about the structure, dynamics, and ligand-binding properties of biomolecules can be derived from Nuclear Magnetic Resonance (NMR) spectroscopy and provides valuable information for drug discovery. A multitude of experimental approaches provides a wealth of information that can be tailored to the system of interest. Methods to study the behavior of ligands upon target binding enable the identification of weak binders in a robust manner that is critical for the identification of truly novel binding interactions. This is particularly important for challenging targets. Observing the solution behavior of biomolecules yields information about their structure, dynamics, and interactions. This review describes the breadth of approaches that are available, many of which are under-utilized in a drug-discovery environment, and focuses on recent advances that continue to emerge.

Lipid Nanodiscs for High-Resolution NMR Studies of Membrane Proteins

Membrane proteins (MPs) play essential roles in numerous cellular processes. Because around 70% of the currently marketed drugs target MPs, a detailed understanding of their structure, binding properties, and functional dynamics in a physiologically relevant environment is crucial for a more detailed understanding of this important protein class. We here summarize the benefits of using lipid nanodiscs for NMR structural investigations and provide a detailed overview of the currently used lipid nanodisc systems as well as their applications in solution-state NMR. Despite the increasing use of other structural methods for the structure determination of MPs in lipid nanodiscs, solution NMR turns out to be a versatile tool to probe a wide range of MP features, ranging from the structure determination of small to medium-sized MPs to probing ligand and partner protein binding as well as functionally relevant dynamical signatures in a lipid nanodisc setting. We will expand on these topics by discussing recent NMR studies with lipid nanodiscs and work out a key workflow for optimizing the nanodisc incorporation of an MP for subsequent NMR investigations. With this, we hope to provide a comprehensive background to enable an informed assessment of the applicability of lipid nanodiscs for NMR studies of a particular MP of interest.

Observation of Sub-Microsecond Protein Methyl-Side Chain Dynamics by Nanoparticle-Assisted NMR Spin Relaxation

Amino-acid side-chain properties in proteins are key determinants of protein function. NMR spin relaxation of side chains is an important source of information about local protein dynamics and flexibility. However, traditional solution NMR relaxation methods are most sensitive to sub-nanosecond dynamics lacking information on slower ns-μs time-scale motions. Nanoparticle-assisted NMR spin relaxation (NASR) of methyl-side chains is introduced here as a window into these ns-μs dynamics. NASR utilizes the transient and nonspecific interactions between folded proteins and slowly tumbling spherical nanoparticles (NPs), whereby the increase of the relaxation rates reflects motions on time scales from ps all the way to the overall tumbling correlation time of the NPs ranging from hundreds of ns to μs. The observed motional amplitude of each methyl group can then be expressed by a model-free NASR S² order parameter. The method is demonstrated for ²H-relaxation of CH₂D methyl moieties and cross-correlated relaxation of CH₃ groups for proteins Im7 and ubiquitin in the presence of anionic silica-nanoparticles. Both types of relaxation experiments, dominated by either quadrupolar or dipolar interactions, yield highly consistent results. Im7 shows additional dynamics on the intermediate time scales taking place in a functionally important loop, whereas ubiquitin visits the majority of its conformational substates on the sub-ns time scale. These experimental observations are in good agreement with 4-10 μs all-atom molecular dynamics trajectories. NASR probes side-chain dynamics on a much wider range of motional time scales than previously possible, thereby providing new insights into the interplay between protein structure, dynamics, and molecular interactions that govern protein function.

DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.

Correlated Motions in Structural Biology

Correlated motions in proteins arising from the collective movements of residues have long been proposed to be fundamentally important to key properties of proteins, from allostery and catalysis to evolvability. Recent breakthroughs in structural biology have made it possible to capture proteins undergoing complex conformational changes, yet intrinsic correlated motions within a conformation remain one of the least understood facets of protein structure. For many decades, the analysis of total X-ray scattering held the promise of animating crystal structures with correlated motions. With recent advances in both X-ray detectors and data interpretation methods, this long-held promise can now be met. In this Perspective, we will introduce how correlated motions are captured in total scattering and provide guidelines for the collection, interpretation, and validation of data. As structural biology continues to push the boundaries, we see an opportunity to gain atomistic insight into correlated motions using total scattering as a bridge between theory and experiment.

Protein Footprinting, Conformational Dynamics, and Core Interface-Adjacent Neutralization "Hotspots" in the SARS-CoV-2 Spike Protein Receptor Binding Domain/Human ACE2 Interaction

The novel severe respiratory syndrome-like coronavirus (SARS-CoV-2) causes COVID-19 in humans and is responsible for one of the most destructive pandemics of the last century. At the root of SARS-CoV infection is the interaction between the viral spike protein and the human angiotensin converting enzyme 2 protein, which allows the virus to gain entry into host cells through endocytosis. In this work, we apply hydrogen-deuterium exchange mass spectrometry (HDX-MS) to provide a detailed view of the functional footprint and conformational dynamics associated with this interaction. Our results broadly agree with the binding interface derived from high resolution X-ray crystal structure data but also provide insights into shifts in structure and dynamics that accompany complexation, including some that occur immediately outside of the core binding interface. We propose that dampening of these "binding-site adjacent" dynamic shifts could represent a mechanism for neutralizing activity in a multitude of spike protein-targeted mAbs that have been found to specifically bind these "peripheral" sites. Our results highlight the unique capacity of HDX-MS to detect potential neutralization "hotspots" outside of the core binding interfaces defined by high resolution structural data.

Inherent backbone dynamics fine-tune the functional plasticity of Tudor domains

Tudor domains are crucial for mediating a diversity of protein-protein or protein-DNA interactions involved in nucleic acid metabolism. Using solution NMR spectroscopy, we assess the comprehensive understanding of the dynamical properties of the respective Tudor domains from four different bacterial (Escherichia coli) proteins UvrD, Mfd, RfaH, and NusG involved in different aspects of bacterial transcription regulation and associated processes. These proteins are benchmarked to the canonical Tudor domain fold from the human SMN protein. The detailed analysis of protein backbone dynamics and subsequent analysis by the Lipari-Szabo model-free approach revealed subtle differences in motions of the amide-bond vector on both pico- to nanosecond and micro- to millisecond timescales. On these timescales, our comparative approach reveals the usefulness of discrete amplitudes of dynamics to discern the different functionalities for Tudor domains exhibiting promiscuous binding, including the metamorphic Tudor domain included in the study.

Milligram scale expression, refolding, and purification of Bombyx mori cocoonase using a recombinant E. coli system

Silk is one of the most versatile biomaterials with signature properties of outstanding mechanical strength and flexibility. A potential avenue for developing more environmentally friendly silk production is to make use of the silk moth (Bombyx mori) cocoonase, this will at the same time increase the possibility for using the byproduct, sericin, as a raw material for other applications. Cocoonase is a serine protease utilized by the silk moth to soften the cocoon to enable its escape after completed metamorphosis. Cocoonase selectively degrades the glue protein of the cocoon, sericin, without affecting the silk-fiber made of the protein fibroin. Cocoonase can be recombinantly produced in E. coli, however, it is exclusively found as insoluble inclusion bodies. To solve this problem and to be able to utilize the benefits associated with an E. coli based expression system, we have developed a protocol that enables the production of soluble and functional protease in the milligram/liter scale. The core of the protocol is refolding of the protein in a buffer with a redox potential that is optimized for formation of native and intramolecular di-sulfide bridges. The redox potential was balanced with defined concentrations of reduced and oxidized glutathione. This E.coli based production protocol will, in addition to structure determination, also enable modification of cocoonase both in terms of catalytic function and stability. These factors will be valuable components in the development of alternate silk production methodology.

Viewing SARS-CoV-2 Nucleocapsid Protein in Terms of Molecular Flexibility

The latest coronavirus SARS-CoV-2, which causes coronavirus disease 2019 (COVID-19) pneumonia leading to the pandemic, contains 29 proteins. Among them, nucleocapsid protein (NCoV2) is one of the abundant proteins and shows multiple functions including packaging the RNA genome during the infection cycle. It has also emerged as a potential drug target. In this review, the current status of the research of NCoV2 is described in terms of molecular structure and dynamics. NCoV2 consists of two domains, i.e., the N-terminal domain (NTD) and the C-terminal domain (CTD) with a disordered region between them. Recent simulation studies have identified several potential drugs that can bind to NTD or CTD with high affinity. Moreover, it was shown that the degree of flexibility in the disordered region has a large effect on drug binding rate, suggesting the importance of molecular flexibility for the NCoV2 function. Molecular flexibility has also been shown to be integral to the formation of droplets, where NCoV2, RNA and/or other viral proteins gather through liquid-liquid phase separation and considered important for viral replication. Finally, as one of the future research directions, a strategy for obtaining the structural and dynamical information on the proteins contained in droplets is presented.

Molecular Dynamics-From Small Molecules to Macromolecules

All molecular systems, from small molecules to macromolecules, exhibit specific characteristics for a specific environment and time. In order to gain an accurate understanding of the functions of all types of molecules, studies of their structure and dynamics are essential. Through dynamic studies, using techniques such as spectroscopy, structure determination, and computer analysis, it is possible to collect functional information on molecules at specific times and in specific environments. Such information not only reveals the properties and mechanisms of action of molecules but also provides insights that can be applied to various industries, such as the development of new materials and drugs. Herein, I discuss the importance of molecular dynamics studies, present the time scale of molecular motion, and review techniques for analyzing molecular dynamics.

Backbone chemical shift assignments for the SARS-CoV-2 non-structural protein Nsp9: intermediate (ms - μs) dynamics in the C-terminal helix at the dimer interface

The Betacoronavirus SARS-CoV-2 non-structural protein Nsp9 is a 113-residue protein that is essential for viral replication, and consequently, a potential target for the development of therapeutics against COVID19 infections. To capture insights into the dynamics of the protein's backbone in solution and accelerate the identification and mapping of ligand-binding surfaces through chemical shift perturbation studies, the backbone ¹H, ¹³C, and ¹⁵N NMR chemical shifts for Nsp9 have been extensively assigned. These assignments were assisted by the preparation of an ~ 70% deuterated sample and residue-specific, ¹⁵N-labelled samples (V, L, M, F, and K). A major feature of the assignments was the "missing" amide resonances for N96-L106 in the ¹H-¹⁵N HSQC spectrum, a region that comprises almost the complete C-terminal α-helix that forms a major part of the homodimer interface in the crystal structure of SARS-CoV-2 Nsp9, suggesting this region either undergoes intermediate motion in the ms to μs timescale and/or is heterogenous. These "missing" amide resonances do not unambiguously appear in the ¹H-¹⁵N HSQC spectrum of SARS-CoV-2 Nsp9 collected at a concentration of 0.0007 mM. At this concentration, at the detection limit, native mass spectrometry indicates the protein is exclusively in the monomeric state, suggesting the intermediate motion in the C-terminal of Nsp9 may be due to intramolecular dynamics. Perhaps this intermediate ms to μs timescale dynamics is the physical basis for a previously suggested "fluidity" of the C-terminal helix that may be responsible for homophilic (Nsp9-Nsp9) and postulated heterophilic (Nsp9-Unknown) protein-protein interactions.

Deciphering the Binding Interactions between Acinetobacter baumannii ACP and β-ketoacyl ACP Synthase III to Improve Antibiotic Targeting Using NMR Spectroscopy

Fatty acid synthesis is essential for bacterial viability. Thus, fatty acid synthases (FASs) represent effective targets for antibiotics. Nevertheless, multidrug-resistant bacteria, including the human opportunistic bacteria, Acinetobacter baumannii, are emerging threats. Meanwhile, the FAS pathway of A. baumannii is relatively unexplored. Considering that acyl carrier protein (ACP) has an important role in the delivery of fatty acyl intermediates to other FAS enzymes, we elucidated the solution structure of A. baumannii ACP (AbACP) and, using NMR spectroscopy, investigated its interactions with β-ketoacyl ACP synthase III (AbKAS III), which initiates fatty acid elongation. The results show that AbACP comprises four helices, while Ca²⁺ reduces the electrostatic repulsion between acid residues, and the unconserved F47 plays a key role in thermal stability. Moreover, AbACP exhibits flexibility near the hydrophobic cavity entrance from D59 to T65, as well as in the α₁α₂ loop region. Further, F29 and A69 participate in slow exchanges, which may be related to shuttling of the growing acyl chain. Additionally, electrostatic interactions occur between the α₂ and α₃-helix of ACP and AbKAS III, while the hydrophobic interactions through the ACP α₂-helix are seemingly important. Our study provides insights for development of potent antibiotics capable of inhibiting A. baumannii FAS protein–protein interactions.

The Picornavirus Precursor 3CD Has Different Conformational Dynamics Compared to 3Cpro and 3Dpol in Functionally Relevant Regions

Viruses have evolved numerous strategies to maximize the use of their limited genetic material, including proteolytic cleavage of polyproteins to yield products with different functions. The poliovirus polyprotein 3CD is involved in important protein-protein, protein-RNA and protein-lipid interactions in viral replication and infection. It is a precursor to the 3C protease and 3D RNA-dependent RNA polymerase, but has different protease specificity, is not an active polymerase, and participates in other interactions differently than its processed products. These functional differences are poorly explained by the known X-ray crystal structures. It has been proposed that functional differences might be due to differences in conformational dynamics between 3C, 3D and 3CD. To address this possibility, we conducted nuclear magnetic resonance spectroscopy experiments, including multiple quantum relaxation dispersion, chemical exchange saturation transfer and methyl spin-spin relaxation, to probe conformational dynamics across multiple timescales. Indeed, these studies identified differences in conformational dynamics in functionally important regions, including enzyme active sites, and RNA and lipid binding sites. Expansion of the conformational ensemble available to 3CD may allow it to perform additional functions not observed in 3C and 3D alone despite having nearly identical lowest-energy structures.

A theoretical study on the effects of interdomain flexibility on drug encounter rate for coronavirus nucleocapsid-type proteins

To study the effects of the interdomain flexibility on the encounter rate of nucleocapsid-type protein with drug molecules, where two domains (NTD) are connected by a flexible linker and each NTD has a drug binding site, two-dimensional random walk simulation was carried out as a function of the interdomain flexibility and the drug concentration. NTDs represented as circles undergo random motions constrained by the interdomain flexibility while drug molecules are represented by lattice points. It was found that as the interdomain flexibility increases, the time interval between the drug bindings to the 1st and 2nd NTDs decreases, suggesting that the 2nd drug binding is accelerated. Furthermore, this effect was more significant at lower drug concentrations. These results suggest that the interdomain linker plays a key role in the drug binding process and thus emphasize the importance of characterization of their physicochemical properties to better evaluate the efficacy of potential drugs.

Exploring interactions between lipids and amyloid-forming proteins: A review on applying fluorescence and NMR techniques

A hallmark of Alzheimer's, Parkinson's, and other amyloid diseases is the assembly of amyloid proteins into amyloid aggregates or fibrils. In many cases, the formation and cytotoxicity of amyloid assemblies are associated with their interaction with cell membranes. Despite studied for many years, the characterization of the interaction is challenged for reasons on the multiple aggregation states of amyloid-forming proteins, transient and weak interactions in the complex system. Although several strategies such as computation biology, spectroscopy, and imaging methods have been performed, there is an urgent need to detail the molecular mechanism in different time scales and high resolutions. This review highlighted the recent applications of fluorescence, solution and solid-state NMR in exploring the interactions between amyloid protein and membranes attributing to their advantages of high sensitivity and atomic resolution.

Broadband Dynamics of Ubiquitin by Anionic and Cationic Nanoparticle Assisted NMR Spin Relaxation

The quantitative and comprehensive description of the internal dynamics of proteins is critical for understanding their function. Nanoparticle-assisted ¹⁵ N NMR spin relaxation spectroscopy is a new method for the observation of picosecond to microsecond dynamics of proteins when transiently interacting with the surface of the nanoparticles (NPs). The method is applied here to the protein ubiquitin in the presence of anionic and cationic silica NPs (SNPs) of different sizes. The backbone dynamics profiles are reproducible and strikingly similar to each other, indicating that specific protein-SNP interactions are unimportant. The dynamics profiles closely match the sub-nanosecond dynamics S² values observed by model-free analysis of standard ¹⁵ N relaxation of ubiquitin in free solution, indicating that the bulk of the ubiquitin backbone dynamics in solution is confined to sub-nanosecond timescales and, hence, it is dynamically more restrained than previous NMR studies have suggested.

The Role of Conformational Dynamics in the Recognition and Regulation of Ubiquitination

The ubiquitination pathway is central to many cell signaling and regulatory events. One of the intriguing aspects of the pathway is the combinatorial sophistication of substrate recognition and ubiquitin chain building determinations. The abundant structural and biological data portray several characteristic protein folds among E2 and E3 proteins, and the understanding of the combinatorial complexity that enables interaction with much of the human proteome is a major goal to developing targeted and selective manipulation of the pathway. With the commonality of some folds, there are likely other aspects that can provide differentiation and recognition. These aspects involve allosteric effects and conformational dynamics that can direct recognition and chain building processes. In this review, we will describe the current state of the knowledge for conformational dynamics across a wide timescale, address the limitations of present approaches, and illustrate the potential to make new advances in connecting dynamics with ubiquitination regulation.

John PC, Bomble YJ. Advances in integrative structural biology: Towards understanding protein complexes in their cellular context

Microorganisms rely on protein interactions to transmit signals, react to stimuli, and grow. One of the best ways to understand these protein interactions is through structural characterization. However, in the past, structural knowledge was limited to stable, high-affinity complexes that could be crystallized. Recent developments in structural biology have revolutionized how protein interactions are characterized. The combination of multiple techniques, known as integrative structural biology, has provided insight into how large protein complexes interact in their native environment. In this mini-review, we describe the past, present, and potential future of integrative structural biology as a tool for characterizing protein interactions in their cellular context.

The role of NMR in leveraging dynamics and entropy in drug design

Nuclear magnetic resonance (NMR) spectroscopy has contributed to structure-based drug development (SBDD) in a unique way compared to the other biophysical methods. The potency of a ligand binding to a protein is dictated by the binding free energy, which is an intricate interplay between entropy and enthalpy. In addition to providing the atomic resolution structural information, NMR can help to identify protein-ligand interactions that potentially contribute to the enthalpic component of the free energy. NMR can also illuminate dynamic aspects of the interaction, which correspond to the entropic term of the free energy. The ability of NMR to access both terms in the free energy equation stems from the suite of experiments developed to shed light on various aspects that contribute to both entropy and enthalpy, deepening our understanding of the biological function of macromolecules and assisting to target them in physiological conditions. Here we provide a brief account of the contribution of NMR to SBDD, highlighting hallmark examples and discussing the challenges that demand further method development. In the era of integrated biology, the unique ability of NMR to directly ascertain structural and dynamical aspects of macromolecule and monitor changes in these properties upon engaging a ligand can be combined with computational and other structural and biophysical methods to provide a more complete picture of the energetics of drug engagement with the target. Such efforts can be used to engineer better drugs.