New Views of Functionally Dynamic Proteins by Solution NMR Spectroscopy

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.

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.

From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.

Paramagnetic NMR restraints for the characterization of protein structural rearrangements

Mobility is a common feature of biomacromolecules, often fundamental for their function. Thus, in many cases, biomacromolecules cannot be described by a single conformation, but rather by a conformational ensemble. NMR paramagnetic data demonstrated quite informative to monitor this conformational variability, especially when used in conjunction with data from different sources. Due to their long-range nature, paramagnetic data can, for instance, i) clearly demonstrate the occurrence of conformational rearrangements, ii) reveal the presence of minor conformational states, sampled only for a short time, iii) indicate the most representative conformations within the conformational ensemble sampled by the molecule, iv) provide an upper limit to the weight of each conformation.

Metabolomics and NMR

The purpose of this manuscript will be to convince the reader to dive deeper into NMR spectroscopy and prevent the technique from being just another "black-box" in the lab. We will try to concisely highlight interesting topics and supply additional references for further exploration at each stage. The advantages of delving into the technique will be shown. The secondary objective, i.e., avoiding common problems before starting, will hopefully then become clear. Lastly, we will emphasize the spectrometer information needed for manuscript reporting to allow reproduction of results and confirm findings.

Combining cysteine scanning with chemical labeling to map protein-protein interactions and infer bound structure in an intrinsically disordered region

The Mycobacterium tuberculosis genome harbours nine toxin-antitoxin (TA) systems of the mazEF family. These consist of two proteins, a toxin and an antitoxin, encoded in an operon. While the toxin has a conserved fold, the antitoxins are structurally diverse and the toxin binding region is typically intrinsically disordered before binding. We describe high throughput methodology for accurate mapping of interfacial residues and apply it to three MazEF complexes. The method involves screening one partner protein against a panel of chemically masked single cysteine mutants of its interacting partner, displayed on the surface of yeast cells. Such libraries have much lower diversity than those generated by saturation mutagenesis, simplifying library generation and data analysis. Further, because of the steric bulk of the masking reagent, labeling of virtually all exposed epitope residues should result in loss of binding, and buried residues are inaccessible to the labeling reagent. The binding residues are deciphered by probing the loss of binding to the labeled cognate partner by flow cytometry. Using this methodology, we have identified the interfacial residues for MazEF3, MazEF6 and MazEF9 TA systems of M. tuberculosis. In the case of MazEF9, where a crystal structure was available, there was excellent agreement between our predictions and the crystal structure, superior to those with AlphaFold2. We also report detailed biophysical characterization of the MazEF3 and MazEF9 TA systems and measured the relative affinities between cognate and non-cognate toxin–antitoxin partners in order to probe possible cross-talk between these systems.

NMR spectroscopy, excited states and relevance to problems in cell biology - transient pre-nucleation tetramerization of huntingtin and insights into Huntington's disease

Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for analyzing three-dimensional structure and dynamics of macromolecules at atomic resolution. Recent advances have exploited the unique properties of NMR in exchanging systems to detect, characterize and visualize excited sparsely populated states of biological macromolecules and their complexes, which are only transient. These states are invisible to conventional biophysical techniques, and play a key role in many processes, including molecular recognition, protein folding, enzyme catalysis, assembly and fibril formation. All the NMR techniques make use of exchange between sparsely populated NMR-invisible and highly populated NMR-visible states to transfer a magnetization property from the invisible state to the visible one where it can be easily detected and quantified. There are three classes of NMR experiments that rely on differences in distance, chemical shift or transverse relaxation (molecular mass) between the NMR-visible and -invisible species. Here, I illustrate the application of these methods to unravel the complex mechanism of sub-millisecond pre-nucleation oligomerization of the N-terminal region of huntingtin, encoded by exon-1 of the huntingtin gene, where CAG expansion leads to Huntington's disease, a fatal autosomal-dominant neurodegenerative condition. I also discuss how inhibition of tetramerization blocks the much slower (by many orders of magnitude) process of fibril formation.

In-Cell Structural Biology by NMR: The Benefits of the Atomic Scale

In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.

Large Chaperone Complexes Through the Lens of Nuclear Magnetic Resonance Spectroscopy

Molecular chaperones are the guardians of the proteome inside the cell. Chaperones recognize and bind unfolded or misfolded substrates, thereby preventing further aggregation; promoting correct protein folding; and, in some instances, even disaggregating already formed aggregates. Chaperones perform their function by means of an array of weak protein-protein interactions that take place over a wide range of timescales and are therefore invisible to structural techniques dependent upon the availability of highly homogeneous samples. Nuclear magnetic resonance (NMR) spectroscopy, however, is ideally suited to study dynamic, rapidly interconverting conformational states and protein-protein interactions in solution, even if these involve a high-molecular-weight component. In this review, we give a brief overview of the principles used by chaperones to bind their client proteins and describe NMR methods that have emerged as valuable tools to probe chaperone-substrate and chaperone-chaperone interactions. We then focus on a few systems for which the application of these methods has greatly increased our understanding of the mechanisms underlying chaperone functions. Expected final online publication date for the Annual Review of Biophysics, Volume 51 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling

Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins. However traditional applications face several challenges, including a need for large datasets to offset the sparsity of restraints, the difficulty in accounting for the conformational heterogeneity of the spin-label, and noisy experimental data. Here we propose an integrative approach to structure determination combining sparse paramagnetic NMR with physical modelling to infer approximate protein structural ensembles. We use calmodulin in complex with the smooth muscle myosin light chain kinase peptide as a model system. Despite acquiring data from samples labeled only at the backbone amide positions, we are able to produce an ensemble with an average RMSD of ∼2.8 Å from a reference X-ray crystal structure. Our approach requires only backbone chemical shifts and measurements of the paramagnetic relaxation enhancement and residual dipolar couplings that can be obtained from sparsely labeled samples.

Making the invisible visible: Toward structural characterization of allosteric states, interaction networks, and allosteric regulatory mechanisms in protein kinases

Despite the established view of protein kinases as dynamic and versatile allosteric regulatory machines, our knowledge of allosteric functional states, allosteric interaction networks, and the intrinsic folding energy landscapes is surprisingly limited. We discuss the latest developments in structural characterization of allosteric molecular events underlying protein kinase dynamics and functions using structural, biophysical, and computational biology approaches. The recent studies highlighted progress in making the invisible aspects of protein kinase 'life' visible, including the determination of hidden allosteric states and mapping of allosteric energy landscapes, discovery of new mechanisms underlying ligand-induced modulation of allosteric activity, evolutionary adaptation of kinase allostery, and characterization of allosteric interaction networks as the intrinsic driver of kinase adaptability and signal transmission in the regulatory assemblies.

NMR refinement and peptide folding using the GROMACS software

Nuclear magnetic resonance spectroscopy is used routinely for studying the three-dimensional structures and dynamics of proteins and nucleic acids. Structure determination is usually done by adding restraints based upon NMR data to a classical energy function and performing restrained molecular simulations. Here we report on the implementation of a script to extract NMR restraints from a NMR-STAR file and export it to the GROMACS software. With this package it is possible to model distance restraints, dihedral restraints and orientation restraints. The output from the script is validated by performing simulations with and without restraints, including the ab initio refinement of one peptide.

Targeted DNP for biomolecular solid-state NMR

High-field dynamic nuclear polarization is revolutionizing the scope of solid-state NMR with new applications in surface chemistry, materials science and structural biology. In this perspective article, we focus on a specific DNP approach, called targeted DNP, in which the paramagnets introduced to polarize are not uniformly distributed in the sample but site-specifically located on the biomolecular system. After reviewing the various targeting strategies reported to date, including a bio-orthogonal chemistry-based approach, we discuss the potential of targeted DNP to improve the overall NMR sensitivity while avoiding the use of glass-forming DNP matrix. This is especially relevant to the study of diluted biomolecular systems such as, for instance, membrane proteins within their lipidic environment. We also discuss routes towards extracting structural information from paramagnetic relaxation enhancement (PRE) induced by targeted DNP at cryogenic temperature, and the possibility to recover site-specific information in the vicinity of the paramagnetic moieties using high-resolution selective DNP spectra. Finally, we review the potential of targeted DNP for in-cell NMR studies and how it can be used to extract a given protein NMR signal from a complex cellular background.

Simultaneous measurement of 1HC/N-R2's for rapid acquisition of backbone and sidechain paramagnetic relaxation enhancements (PREs) in proteins

Paramagnetic relaxation enhancements (PREs) are routinely used to provide long-range distance restraints for the determination of protein structures, to resolve protein dynamics, ligand-protein binding sites, and lowly populated species, using Nuclear Magnetic Resonance Spectroscopy (NMR). Here, we propose a simultaneous ¹H-¹⁵ N, ¹H-¹³C SESAME based pulse scheme for the rapid acquisition of ¹H^C/N-R₂ relaxation rates for the determination of backbone and sidechain PREs of proteins. The ¹H^N-R₂ rates from the traditional and our approach on Ubiquitin (UBQ) are well correlated (R² = 0.99), revealing their potential to be used quantitatively. Comparison of the S57C UBQ calculated and experimental PREs provided backbone and side chain Q factors of 0.23 and 0.24, respectively, well-fitted to the UBQ NMR structure, showing that our approach can be used to acquire accurate PRE rates from the functionally important sites of proteins but in at least half the time as traditional methods.

The precious fluorine on the ring: fluorine NMR for biological systems

The fluorine-19 nucleus was recognized early to harbor exceptional properties for NMR spectroscopy. With 100% natural abundance, a high gyromagnetic ratio (83% sensitivity compared to 1H), a chemical shift that is extremely sensitive to its surroundings and near total absence in biological systems, it was destined to become a favored NMR probe, decorating small and large molecules. However, after early excitement, where uptake of fluorinated aromatic amino acids was explored in a series of animal studies, 19F-NMR lost popularity, especially in large molecular weight systems, due to chemical shift anisotropy (CSA) induced line broadening at high magnetic fields. Recently, two orthogonal approaches, (i) CF3 labeling and (ii) aromatic 19F-13C labeling leveraging the TROSY (Transverse Relaxation Optimized Spectroscopy) effect have been successfully applied to study large biomolecular systems. In this perspective, we will discuss the fascinating early work with fluorinated aromatic amino acids, which reveals the enormous potential of these non-natural amino acids in biological NMR and the potential of 19F-NMR to characterize protein and nucleic acid structure, function and dynamics in the light of recent developments. Finally, we explore how fluorine NMR might be exploited to implement small molecule or fragment screens that resemble physiological conditions and discuss the opportunity to follow the fate of small molecules in living cells.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Conformational heterogeneity of Savinase from NMR, HDX-MS and X-ray diffraction analysis

Background

Several examples have emerged of enzymes where slow conformational changes are of key importance for function and where low populated conformations in the resting enzyme resemble the conformations of intermediate states in the catalytic process. Previous work on the subtilisin protease, Savinase, from Bacillus lentus by NMR spectroscopy suggested that this enzyme undergoes slow conformational dynamics around the substrate binding site. However, the functional importance of such dynamics is unknown.

Methods

Here we have probed the conformational heterogeneity in Savinase by following the temperature dependent chemical shift changes. In addition, we have measured changes in the local stability of the enzyme when the inhibitor phenylmethylsulfonyl fluoride is bound using hydrogen-deuterium exchange mass spectrometry (HDX-MS). Finally, we have used X-ray crystallography to compare electron densities collected at cryogenic and ambient temperatures and searched for possible low populated alternative conformations in the crystals.

Results

The NMR temperature titration shows that Savinase is most flexible around the active site, but no distinct alternative states could be identified. The HDX shows that modification of Savinase with inhibitor has very little impact on the stability of hydrogen bonds and solvent accessibility of the backbone. The most pronounced structural heterogeneities detected in the diffraction data are limited to alternative side-chain rotamers and a short peptide segment that has an alternative main-chain conformation in the crystal at cryo conditions. Collectively, our data show that there is very little structural heterogeneity in the resting state of Savinase and hence that Savinase does not rely on conformational selection to drive the catalytic process.

A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery

Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful tool to study structures and dynamics of biomolecules under physiological conditions. As there are numerous NMR-derived methods applicable to probe protein–ligand interactions, NMR has been widely utilized in drug discovery, especially in such steps as hit identification and lead optimization. NMR is frequently used to locate ligand-binding sites on a target protein and to determine ligand binding modes. NMR spectroscopy is also a unique tool in fragment-based drug design (FBDD), as it is able to investigate target-ligand interactions with diverse binding affinities. NMR spectroscopy is able to identify fragments that bind weakly to a target, making it valuable for identifying hits targeting undruggable sites. In this review, we summarize the roles of solution NMR spectroscopy in drug discovery. We describe some methods that are used in identifying fragments, understanding the mechanism of action for a ligand, and monitoring the conformational changes of a target induced by ligand binding. A number of studies have proven that ¹⁹F-NMR is very powerful in screening fragments and detecting protein conformational changes. In-cell NMR will also play important roles in drug discovery by elucidating protein-ligand interactions in living cells.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Linker Domains: Why ABC Transporters 'Live in Fragments no Longer'

ATP-binding cassette (ABC) transporters are membrane proteins present in all kingdoms of life. We have considered the disordered region that connects the N- and C-terminal halves in many eukaryotic ABC transporters, allowing all four consensus functional domains to be linked. The recent availability of structures of ABC transporters containing linker regions has allowed us to identify the start and end points of the connectors as well as hinting at their localisation. We address questions such as: Where did the linker regions come from? Why do some ABC transporters have connectors and others not? What are the rules and roles of the linker regions? What are the consequences of mutations in these connector regions for disease in humans?

Characterization of the dynamics and the conformational entropy in the binding between TAZ1 and CTAD-HIF-1α

The interaction between the C-terminal transactivation domain of HIF-1α (CTAD-HIF-1α) and the transcriptional adapter zinc binding 1 (TAZ1) domain of CREB binding protein participate in the initiation of gene transcription during hypoxia. Unbound CTAD-HIF-1α is disordered but undergoes a disorder-to-order transition upon binding to TAZ1. We have here performed NMR side chain and backbone relaxation studies on TAZ1 and side chain relaxation measurements on CTAD-HIF-1α in order to investigate the role of picosecond to nanosecond dynamics. We find that the internal motions are significantly affected upon binding, both on the side chain and the backbone level. The dynamic response corresponds to a conformational entropy change that contributes substantially to the binding thermodynamics for both binding partners. Furthermore, the conformational entropy change for the well-folded TAZ1 varies upon binding to different IDP targets. We further identify a cluster consisting of side chains in bound TAZ1 and CTAD-HIF-1α that experience extensive dynamics and are part of the binding region that involves the N-terminal end of the LPQL motif in CTAD-HIF-1α; a feature that might have an important role in the termination of the hypoxic response.

NMR Assessment of Therapeutic Peptides and Proteins: Correlations That Reveal Interactions and Motions

NMR measurements of rotational and translational diffusion are used to characterize the solution behavior of a wide variety of therapeutic proteins and peptides. The timescales of motions sampled in these experiments reveal complicated intrinsic solution behavior such as flexibility, that is central to function, as well as self-interactions, stress-induced conformational changes and other critical attributes that can be discovery and development liabilities. Trends from proton transverse relaxation (R₂ ) and hydrodynamic radius (R_h ) are correlated and used to identify and differentiate intermolecular from intramolecular interactions. In this study, peptide behavior is consistent with complicated multimer self-assembly, while multi-domain protein behavior is dominated by intramolecular interactions. These observations are supplemented by simulations that include effects from slow transient interactions and rapid internal motions. R₂ -R_h correlations provide a means to profile protein motions as well as interactions. The approach is completely general and can be applied to therapeutic and target protein characterization.

Molecular Graphics: Bridging Structural Biologists and Computer Scientists

Visualization of molecular structures is one of the most common tasks carried out by structural biologists, typically using software, such as Chimera, COOT, PyMOL, or VMD. In this Perspective article, we outline how past developments in computer graphics and data visualization have expanded the understanding of biomolecular function, and we summarize recent advances that promise to further transform structural biology. We also highlight how progress in molecular graphics has been impeded by communication barriers between two communities: the computer scientists driving these advances, and the structural and computational biologists who stand to benefit. By pointing to canonical papers and explaining technical progress underlying new graphical developments in simple terms, we aim to improve communication between these communities; this, in turn, would help shape future developments in molecular graphics.

Coarse-grain simulations on NMR conformational ensembles highlight functional residues in proteins

Dynamics are a key feature of protein function, and this is especially true of gating residues, which occupy cavity or tunnel lining positions in the protein structure, and will reversibly switch between open and closed conformations in order to control the diffusion of small molecules within a protein's internal matrix. Earlier work on globins and hydrogenases have shown that these gating residues can be detected using a multiscale scheme combining all-atom classic molecular dynamics simulations and coarse-grain calculations of the resulting conformational ensemble mechanical properties. Here, we show that the structural variations observed in the conformational ensembles produced by NMR spectroscopy experiments are sufficient to induce noticeable mechanical changes in a protein, which in turn can be used to identify residues important for function and forming a mechanical nucleus in the protein core. This new approach, which combines experimental data and rapid coarse-grain calculations and no longer needs to resort to time-consuming all-atom simulations, was successfully applied to five different protein families.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

A Conserved Allosteric Pathway in Tyrosine Kinase Regulation

An autoinhibitory network of hydrogen bonds located at the kinase hinge (referred to as the "molecular brake") regulates the activity of several receptor tyrosine kinases. The mechanism whereby mutational disengagement of the brake allosterically activates the kinase in human disease is incompletely understood. We used a combination of NMR, bioinformatics, and molecular dynamics simulation to show that mutational disruption of the molecular brake triggers localized conformational perturbations that propagate to the active site. This entails changes in interactions of an isoleucine, one of three hydrophobic residues that lock the phenylalanine of the DFG motif in an inactive conformation. Structural analysis of tyrosine kinases provides evidence that this allosteric control mechanism is shared across the tyrosine kinase family. We also show that highly activating mutations at the brake diminish the enzyme's thermostability, thereby explaining why these mutations cause milder skeletal syndromes compared with less-activating mutations in the activation loop.

An NMR View of Protein Dynamics in Health and Disease

Biological molecules are often highly dynamic, and this flexibility can be critical for function. The large range of sampled timescales and the fact that many of the conformers that are continually explored are only transiently formed and sparsely populated challenge current biophysical approaches. Solution nuclear magnetic resonance (NMR) spectroscopy has emerged as a powerful method for characterizing biomolecular dynamics in detail, even in cases where excursions involve short-lived states. Here, we briefly review a number of NMR experiments for studies of biomolecular dynamics on the microsecond-to-second timescale and focus on applications to protein and nucleic acid systems that clearly illustrate the functional relevance of motion in both health and disease.

Atomistic Modeling of the ABL Kinase Regulation by Allosteric Modulators Using Structural Perturbation Analysis and Community-Based Network Reconstruction of Allosteric Communications

In this work, we have examined the molecular mechanisms of allosteric regulation of the ABL tyrosine kinase at the atomic level. Atomistic modeling of the ABL complexes with a panel of allosteric modulators has been performed using a combination of molecular dynamics simulations, structural residue perturbation scanning, and a novel community analysis of the residue interaction networks. Our results have indicated that allosteric inhibitors and activators may exert a differential control on allosteric signaling between the kinase binding sites and functional regions. While the inhibitor binding can strengthen the closed ABL state and induce allosteric communications directed from the allosteric pocket to the ATP binding site, the DPH activator may induce a more dynamic open form and activate allosteric couplings between the ATP and substrate binding sites. By leveraging a network-centric theoretical framework, we have introduced a novel community analysis method and global topological parameters that have unveiled the hierarchical modularity and the intercommunity bridging sites in the residue interaction network. We have found that allosteric functional hotspots responsible for the kinase regulation may serve the intermodular bridges in the global interaction network. The central conclusion from this analysis is that the regulatory switch centers play a fundamental role in the modular network organization of ABL as the unique intercommunity bridges that connect the SH2 and SH3 domains with the catalytic core into a functional kinase assembly. The hierarchy of network organization in the ABL regulatory complexes may allow for the synergistic action of dense intercommunity links required for the robust signal transfer in the catalytic core and sparse network bridges acting as the regulatory control points that orchestrate allosteric transitions between the inhibited and active kinase forms.

NMR analysis of GPCR conformational landscapes and dynamics

Understanding the signal transduction mechanism mediated by the G Protein-Coupled Receptors (GPCRs) in eukaryote cells represents one of the main issues in modern biology. At the molecular level, various biophysical approaches have provided important insights on the functional plasticity of these complex allosteric machines. In this context, X-ray crystal structures published during the last decade represent a major breakthrough in GPCR structural biology, delivering important information on the activation process of these receptors through the description of the three-dimensional organization of their active and inactive states. In complement to crystals and cryo-electronic microscopy structures, information on the probability of existence of different GPCR conformations and the dynamic barriers separating those structural sub-states is required to better understand GPCR function. Among the panel of techniques available, nuclear magnetic resonance (NMR) spectroscopy represents a powerful tool to characterize both conformational landscapes and dynamics. Here, we will outline the potential of NMR to address such biological questions, and we will illustrate the functional insights that NMR has brought in the field of GPCRs in the recent years.

Applications of In-Cell NMR in Structural Biology and Drug Discovery

In-cell nuclear magnetic resonance (NMR) is a method to provide the structural information of a target at an atomic level under physiological conditions and a full view of the conformational changes of a protein caused by ligand binding, post-translational modifications or protein⁻protein interactions in living cells. Previous in-cell NMR studies have focused on proteins that were overexpressed in bacterial cells and isotopically labeled proteins injected into oocytes of Xenopus laevis or delivered into human cells. Applications of in-cell NMR in probing protein modifications, conformational changes and ligand bindings have been carried out in mammalian cells by monitoring isotopically labeled proteins overexpressed in living cells. The available protocols and successful examples encourage wide applications of this technique in different fields such as drug discovery. Despite the challenges in this method, progress has been made in recent years. In this review, applications of in-cell NMR are summarized. The successful applications of this method in mammalian and bacterial cells make it feasible to play important roles in drug discovery, especially in the step of target engagement.

Analyzing Biomolecular Ensembles

Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure-dynamics-function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.

Biomolecular Assemblies: Moving from Observation to Predictive Design

Biomolecular assembly is a key driving force in nearly all life processes, providing structure, information storage, and communication within cells and at the whole organism level. These assembly processes rely on precise interactions between functional groups on nucleic acids, proteins, carbohydrates, and small molecules, and can be fine-tuned to span a range of time, length, and complexity scales. Recognizing the power of these motifs, researchers have sought to emulate and engineer biomolecular assemblies in the laboratory, with goals ranging from modulating cellular function to the creation of new polymeric materials. In most cases, engineering efforts are inspired or informed by understanding the structure and properties of naturally occurring assemblies, which has in turn fueled the development of predictive models that enable computational design of novel assemblies. This Review will focus on selected examples of protein assemblies, highlighting the story arc from initial discovery of an assembly, through initial engineering attempts, toward the ultimate goal of predictive design. The aim of this Review is to highlight areas where significant progress has been made, as well as to outline remaining challenges, as solving these challenges will be the key that unlocks the full power of biomolecules for advances in technology and medicine.

Nucleosome structure and dynamics are coming of age

Since the first high-resolution structure of the nucleosome was reported in 1997, the available information on chromatin structure has increased very rapidly. Here, we review insights derived from cutting-edge biophysical and structural approaches applied to the study of nucleosome dynamics and nucleosome-binding factors, with a focus on the experimental advances driving the research. In addition, we highlight emerging challenges in nucleosome structural biology.

Monitoring alkaline transitions of yeast iso-1 cytochrome c at natural isotopic abundance using trimethyllysine as a native NMR probe

Spectral overlap makes it difficult to use NMR for mapping the conformational profile of heterogeneous conformational ensembles of macromolecules. Here, we apply a 1H-14N HSQC experiment to monitor the alkaline conformational transitions of yeast iso-1 cytochrome c (ycyt c) at natural isotopic abundance. Trimethylated Lys72 of ycyt c is selectively detected by a 1H-14N HSQC experiment, and used as a probe to trace conformational transitions of ycyt c under alkaline conditions. It was found that at least four different conformers of ycyt c coexisted under alkaline conditions. Besides the native structure, Lys73 or Lys79 coordinated conformers and a partially unfolded state with exposed heme were observed. These results indicate that the method is powerful at simplifying spectra of a trimethylated protein, which makes it possible to study complex conformational transitions of naturally extracted or chemically modified trimethylated protein at natural isotopic abundance.

Analysis of NMR Spin-Relaxation Data Using an Inverse Gaussian Distribution Function

Spin relaxation in solution-state NMR spectroscopy is a powerful approach to explore the conformational dynamics of biological macromolecules. Probability distribution functions for overall or internal correlation times have been used previously to model spectral density functions central to spin-relaxation theory. Applications to biological macromolecules rely on transverse relaxation rate constants, and when studying nanosecond timescale motions, sampling at ultralow frequencies is often necessary. Consequently, appropriate distribution functions necessitate spectral density functions that are accurate and convergent as frequencies approach zero. In this work, the inverse Gaussian probability distribution function is derived from general properties of spectral density functions at low and high frequencies for macromolecules in solution, using the principle of maximal entropy. This normalized distribution function is first used to calculate the correlation function, followed by the spectral density function. The resulting model-free spectral density functions are finite at a frequency of zero and can be used to describe distributions of either overall or internal correlation times using the model-free ansatz. To validate the approach, ¹⁵N spin-relaxation data for the bZip transcription factor domain of the Saccharomyces cerevisiae protein GCN4, in the absence of cognate DNA, were analyzed using the inverse Gaussian probability distribution for intramolecular correlation times. The results extend previous models for the conformational dynamics of the intrinsically disordered, DNA-binding region of the bZip transcription factor domain.

Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.

Singular value decomposition for the correlation of atomic fluctuations with arbitrary angle

Many proteins exhibit a critical property called allostery, which enables intra-molecular transmission of information between distal sites. Microscopically, allosteric response is closely related to correlated atomic fluctuations. Conventional correlation analysis correlates the atomic fluctuations at two sites by taking the dot product (DP) between the fluctuations, which accounts only for the parallel and antiparallel components. Here, we present a singular value decomposition (SVD) method that analyzes the correlation coefficient of fluctuation dynamics with an arbitrary angle between the correlated directions. In a model allosteric system, the second PDZ domain (PDZ2) in the human PTP1E protein, approximately one third of the strong correlations have near-perpendicular directions, which are underestimated in the conventional method. The discrimination becomes more prominent for residue pairs with larger separation. The results of the proposed SVD method are more consistent with the experimentally determined PDZ2 dynamics than those of conventional method. In addition, the SVD method improved the prediction accuracy of the allosteric sites in a dataset of 23 known allosteric monomer proteins. The proposed method may inspire extended investigation not only into allostery, but also into protein dynamics and drug design.

NMR probing of invisible excited states using selectively labeled RNAs

Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion NMR experiments are invaluable for probing sparsely and transiently populated biomolecular states that cannot be directly detected by traditional NMR experiments and that are invisible by other biophysical approaches. A notable gap for RNA is the absence of CPMG experiments for measurement of methine base ¹H and methylene C5' chemical shifts of ribose moieties in the excited state, partly because of complications from homonuclear ¹³C-¹³C scalar couplings. Here we present site-specific ¹³C labeling that makes possible the design of pulse sequences for recording accurate ¹H-¹³C MQ and SQ CPMG experiments for ribose methine H1'-C1' and H2'-C2', base and ribose ¹H CPMG, as well as a new ¹H-¹³C TROSY-detected methylene (CH₂) C5' CPMG relaxation pulse schemes. We demonstrate the utility of these experiments for two RNAs, the A-Site RNA known to undergo exchange and the IRE RNA suspected of undergoing exchange on microseconds to millisecond time-scale. We anticipate the new labeling approaches will facilitate obtaining structures of invisible states and provide insights into the relevance of such states for RNA-drug interactions.

Challenges and opportunities in connecting simulations with experiments via molecular dynamics of cellular environments

Computer simulations are widely used to study molecular systems, especially in biology. As simulations have greatly increased in scale reaching cellular levels there are now significant challenges in managing, analyzing, and interpreting such data in comparison with experiments that are being discussed. Management challenges revolve around storing and sharing terabyte to petabyte scale data sets whereas the analysis of simulations of highly complex systems will increasingly require automated machine learning and artificial intelligence approaches. The comparison between simulations and experiments is furthermore complicated not just by the complexity of the data but also by difficulties in interpreting experiments for highly heterogeneous systems. As an example, the interpretation of NMR relaxation measurements and comparison with simulations for highly crowded systems is discussed.

Magnesium Activates Microsecond Dynamics to Regulate Integrin-Collagen Recognition

Integrin receptors bind collagen via metal-mediated interactions that are modulated by magnesium (Mg²⁺) levels in the extracellular matrix. Nuclear magnetic resonance-based relaxation experiments, isothermal titration calorimetry, and adhesion assays reveal that Mg²⁺ functions as both a structural anchor and dynamic switch of the α₁β₁ integrin I domain (α₁I). Specifically, Mg²⁺ binding activates micro- to millisecond timescale motions of residues distal to the binding site, particularly those surrounding the salt bridge at helix 7 and near the metal ion-dependent adhesion site. Mutagenesis of these residues impacts α₁I functional activity, thereby suggesting that Mg-bound α₁I dynamics are important for collagen binding and consequent allosteric rearrangement of the low-affinity closed to high-affinity open conformation. We propose a multistep recognition mechanism for α₁I-Mg-collagen interactions involving both conformational selection and induced-fit processes. Our findings unravel the multifaceted role of Mg²⁺ in integrin-collagen recognition and assist in elucidating the molecular mechanisms by which metals regulate protein-protein interactions.

A gratuitous β-Lactamase inducer uncovers hidden active site dynamics of the Staphylococcus aureus BlaR1 sensor domain

Increasing evidence shows that active sites of proteins have non-trivial conformational dynamics. These dynamics include active site residues sampling different local conformations that allow for multiple, and possibly novel, inhibitor binding poses. Yet, active site dynamics garner only marginal attention in most inhibitor design efforts and exert little influence on synthesis strategies. This is partly because synthesis requires a level of atomic structural detail that is frequently missing in current characterizations of conformational dynamics. In particular, while the identity of the mobile protein residues may be clear, the specific conformations they sample remain obscure. Here, we show how an appropriate choice of ligand can significantly sharpen our abilities to describe the interconverting binding poses (conformations) of protein active sites. Specifically, we show how 2-(2'-carboxyphenyl)-benzoyl-6-aminopenicillanic acid (CBAP) exposes otherwise hidden dynamics of a protein active site that binds β-lactam antibiotics. When CBAP acylates (binds) the active site serine of the β-lactam sensor domain of BlaR1 (BlaRS), it shifts the time scale of the active site dynamics to the slow exchange regime. Slow exchange enables direct characterization of inter-converting protein and bound ligand conformations using NMR methods. These methods include chemical shift analysis, 2-d exchange spectroscopy, off-resonance ROESY of the bound ligand, and reduced spectral density mapping. The active site architecture of BlaRS is shared by many β-lactamases of therapeutic interest, suggesting CBAP could expose functional motions in other β-lactam binding proteins. More broadly, CBAP highlights the utility of identifying chemical probes common to structurally homologous proteins to better expose functional motions of active sites.

Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data

We present Farseer-NMR ( https://git.io/vAueU ), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems' responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension.

Capturing dynamic conformational shifts in protein–ligand recognition using integrative structural biology in solution

In recent years, a dynamic view of the structure and function of biological macromolecules is emerging, highlighting an essential role of dynamic conformational equilibria to understand molecular mechanisms of biological functions. The structure of a biomolecule, i.e. protein or nucleic acid in solution, is often best described as a dynamic ensemble of conformations, rather than a single structural state. Strikingly, the molecular interactions and functions of the biological macromolecule can then involve a shift between conformations that pre-exist in such an ensemble. Upon external cues, such population shifts of pre-existing conformations allow gradually relaying the signal to the downstream biological events. An inherent feature of this principle is conformational dynamics, where intrinsically disordered regions often play important roles to modulate the conformational ensemble. Unequivocally, solution-state NMR spectroscopy is a powerful technique to study the structure and dynamics of such biomolecules in solution. NMR is increasingly combined with complementary techniques, including fluorescence spectroscopy and small angle scattering. The combination of these techniques provides complementary information about the conformation and dynamics in solution and thus affords a comprehensive description of biomolecular functions and regulations. Here, we illustrate how an integrated approach combining complementary techniques can assess the structure and dynamics of proteins and protein complexes in solution.

Targeting Intrinsically Disordered Transcription Factors: Changing the Paradigm

Increased understanding of intrinsically disordered proteins (IDPs) and protein regions has revolutionized our view of the relationship between protein structure and function. Data now support that IDPs can be functional in the absence of a single, fixed, three-dimensional structure. Due to their dynamic morphology, IDPs have the ability to display a range of kinetics and affinity depending on what the system requires, as well as the potential for large-scale association. Although several studies have shed light on the functional properties of IDPs, the class of intrinsically disordered transcription factors (TFs) is still poorly characterized biophysically due to their combination of ordered and disordered sequences. In addition, TF modulation by small molecules has long been considered a difficult or even impossible task, limiting functional probe development. However, with evolving technology, it is becoming possible to characterize TF structure-function relationships in unprecedented detail and explore avenues not available or not considered in the past. Here we provide an introduction to the biophysical properties of intrinsically disordered TFs and we discuss recent computational and experimental efforts toward understanding the role of intrinsically disordered TFs in biology and disease. We describe a series of successful TF targeting strategies that have overcome the perception of the "undruggability" of TFs, providing new leads on drug development methodologies. Lastly, we discuss future challenges and opportunities to enhance our understanding of the structure-function relationship of intrinsically disordered TFs.

Rescue of conformational dynamics in enzyme catalysis by directed evolution

Rational design and directed evolution have proved to be successful approaches to increase catalytic efficiencies of both natural and artificial enzymes. Protein dynamics is recognized as important, but due to the inherent flexibility of biological macromolecules it is often difficult to distinguish which conformational changes are directly related to function. Here, we use directed evolution on an impaired mutant of the proline isomerase CypA and identify two second-shell mutations that partially restore its catalytic activity. We show both kinetically, using NMR spectroscopy, and structurally, by room-temperature X-ray crystallography, how local perturbations propagate through a large allosteric network to facilitate conformational dynamics. The increased catalysis selected for in the evolutionary screen is correlated with an accelerated interconversion between the two catalytically essential conformational sub-states, which are both captured in the high-resolution X-ray ensembles. Our data provide a glimpse of an evolutionary trajectory and show how subtle changes can fine-tune enzyme function.