NMR Studies of Large Proteins

Probing Protein-Ligand Methyl-π Interaction Geometries through Chemical Shift Measurements of Selectively Labeled Methyl Groups

Fragment-based drug design is heavily dependent on the optimization of initial low-affinity binders. Herein we introduce an approach that uses selective labeling of methyl groups in leucine and isoleucine side chains to directly probe methyl-π contacts, one of the most prominent forms of interaction between proteins and small molecules. Using simple NMR chemical shift perturbation experiments with selected BRD4-BD1 binders, we find good agreement with a commonly used model of the ring-current effect as well as the overall interaction geometries extracted from the Protein Data Bank. By combining both interaction geometries and chemical shift calculations as fit quality criteria, we can position dummy aromatic rings into an AlphaFold model of the protein of interest. The proposed method can therefore provide medicinal chemists with important information about binding geometries of small molecules in fast and iterative matter, even in the absence of high-resolution experimental structures.

Insights into mechanisms of MALT1 allostery from NMR and AlphaFold dynamic analyses

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is an attractive target for the development of modulatory compounds in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained unexplored. We present here dynamic analyses of the apo MALT1 form along with the E549A mutation. This investigation used NMR 15N relaxation and NOE measurements between side-chain methyl groups. Our findings confirm that MALT1 exists as a monomer in solution, and demonstrate that the domains display semi-independent movements in relation to each other. Our dynamic study, covering multiple time scales, along with the assessment of conformational populations by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, put the side chain of residue W580 in an inward position, shedding light at potential mechanisms underlying the allosteric regulation of this enzyme.

Biomolecular NMR in the AI-assisted structural biology era: Old tricks and new opportunities

Over the last 40 years nuclear magnetic resonance (NMR) spectroscopy has established itself as one of the most versatile techniques for the characterization of biomolecules, especially proteins. Given the molecular size limitations of NMR together with recent advances in cryo-electron microscopy and artificial intelligence-assisted protein structure prediction, the bright future of NMR in structural biology has been put into question. In this mini review we argue the contrary. We discuss the unique opportunities solution NMR offers to the protein chemist that distinguish it from all other experimental or computational methods, and how it can benefit from machine learning.

Designing drugs and chemical probes with the dualsteric approach

Traditionally, drugs are monovalent, targeting only one site on the protein surface. This includes orthosteric and allosteric drugs, which bind the protein at orthosteric and allosteric sites, respectively. Orthosteric drugs are good in potency, whereas allosteric drugs have better selectivity and are solutions to classically undruggable targets. However, it would be difficult to simultaneously reach high potency and selectivity when targeting only one site. Also, both kinds of monovalent drugs suffer from mutation-caused drug resistance. To overcome these obstacles, dualsteric modulators have been proposed in the past twenty years. Compared to orthosteric or allosteric drugs, dualsteric modulators are bivalent (or bitopic) with two pharmacophores. Each of the two pharmacophores bind the protein at the orthosteric and an allosteric site, which could bring the modulator with special properties beyond monovalent drugs. In this study, we comprehensively review the current development of dualsteric modulators. Our main effort reason and illustrate the aims to apply the dualsteric approach, including a "double win" of potency and selectivity, overcoming mutation-caused drug resistance, developments of function-biased modulators, and design of partial agonists. Moreover, the strengths of the dualsteric technique also led to its application outside pharmacy, including the design of highly sensitive fluorescent tracers and usage as molecular rulers. Besides, we also introduced drug targets, designing strategies, and validation methods of dualsteric modulators. Finally, we detail the conclusions and perspectives.

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction

Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requires extensive measurement time. To address this limitation, we previously proposed ARTINA, a deep learning method for automatic assignment of two-dimensional (2D)-4D NMR spectra. Here, we present an integrative approach that combines ARTINA with AlphaFold and UCBShift, enabling chemical shift assignment with reduced experimental data, increased accuracy, and enhanced robustness for larger systems, as presented in a comprehensive study with more than 5000 automated assignment calculations on 89 proteins. We demonstrate that five 3D spectra yield more accurate assignments (92.59%) than pure ARTINA runs using all experimentally available NMR data (on average 10 3D spectra per protein, 91.37%), considerably reducing the required measurement time. We also showcase automated assignments of only 15N-labeled samples, and report improved assignment accuracy in larger synthetic systems of up to 500 residues.

Protein-Inspired Control over Synthetic Polymer Folding for Structured Functional Nanoparticles in Water

The folding of proteins into functional nanoparticles with defined 3D structures has inspired chemists to create simple synthetic systems mimicking protein properties. The folding of polymers into nanoparticles in water proceeds via different strategies, resulting in the global compaction of the polymer chain. Herein, we review the different methods available to control the conformation of synthetic polymers and collapse/fold them into structured, functional nanoparticles, such as hydrophobic collapse, supramolecular self-assembly, and covalent cross-linking. A comparison is made between the design principles of protein folding to synthetic polymer folding and the formation of structured nanocompartments in water, highlighting similarities and differences in design and function. We also focus on the importance of structure for functional stability and diverse applications in complex media and cellular environments.

Optically Enhanced Solid-State 1H NMR Spectroscopy

Low sensitivity is the primary limitation to extending nuclear magnetic resonance (NMR) techniques to more advanced chemical and structural studies. Photochemically induced dynamic nuclear polarization (photo-CIDNP) is an NMR hyperpolarization technique where light is used to excite a suitable donor-acceptor system, creating a spin-correlated radical pair whose evolution drives nuclear hyperpolarization. Systems that exhibit photo-CIDNP in solids are not common, and this effect has, up to now, only been observed for ¹³C and ¹⁵N nuclei. However, the low gyromagnetic ratio and natural abundance of these nuclei trap the local hyperpolarization in the vicinity of the chromophore and limit the utility for bulk hyperpolarization. Here, we report the first example of optically enhanced solid-state ¹H NMR spectroscopy in the high-field regime. This is achieved via photo-CIDNP of a donor-chromophore-acceptor molecule in a frozen solution at 0.3 T and 85 K, where spontaneous spin diffusion among the abundant strongly coupled ¹H nuclei relays polarization through the whole sample, yielding a 16-fold bulk ¹H signal enhancement under continuous laser irradiation at 450 nm. These findings enable a new strategy for hyperpolarized NMR beyond the current limits of conventional microwave-driven DNP.

Insights into the Structure of Invisible Conformations of Large Methyl Group Labeled Molecular Machines from High Pressure NMR

Most proteins are highly flexible and can adopt conformations that deviate from the energetically most favorable ground state. Structural information on these lowly populated, alternative conformations is often lacking, despite the functional importance of these states. Here, we study the pathway by which the Dcp1:Dcp2 mRNA decapping complex exchanges between an autoinhibited closed and an open conformation. We make use of methyl Carr-Purcell-Meiboom-Gill (CPMG) NMR relaxation dispersion (RD) experiments that report on the population of the sparsely populated open conformation as well as on the exchange rate between the two conformations. To obtain volumetric information on the open conformation as well as on the transition state structure we made use of RD measurements at elevated pressures. We found that the open Dcp1:Dcp2 conformation has a lower molecular volume than the closed conformation and that the transition state is close in volume to the closed state. In the presence of ATP the volume change upon opening of the complex increases and the volume of the transition state lies in-between the volumes of the closed and open state. These findings show that ATP has an effect on the volume changes that are associated with the opening-closing pathway of the complex. Our results highlight the strength of pressure dependent NMR methods to obtain insights into structural features of protein conformations that are not directly observable. As our work makes use of methyl groups as NMR probes we conclude that the applied methodology is also applicable to high molecular weight complexes.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

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.

Site-Specific Isotope Labeling of FliG for Studying Structural Dynamics Using Nuclear Magnetic Resonance Spectroscopy

To understand flagella-driven motility of bacteria, it is important to understand the structure and dynamics of the flagellar motor machinery. We have conducted structural dynamics analyses using solution nuclear magnetic resonance (NMR) to elucidate the detailed functions of flagellar motor proteins. Here, we introduce the analysis of the FliG protein, which is a flagellar motor protein, focusing on the preparation method of the original stable isotope-labeled protein.

Less is more: A simple methyl-TROSY based pulse scheme offers improved sensitivity in applications to high molecular weight complexes

The HMQC pulse sequence and variants thereof have been exploited in studies of high molecular weight protein complexes, taking advantage of the fact that fast and slow relaxing magnetization components are sequestered along two distinct magnetization transfer pathways. Despite the simplicity of the HMQC scheme an even shorter version can be designed, based on elimination of the terminal refocusing period, as a further means of increasing signal. Here we present such an experiment, and show that significant sensitivity gains, in some cases by factors of two or more, are realized in studies of proteins varying in molecular masses from 100 kDa to 1 MDa.

Atomic-Resolution Experimental Structural Biology and Molecular Dynamics Simulations of Hyaluronan and Its Complexes

This review summarizes the atomic-resolution structural biology of hyaluronan and its complexes available in the Protein Data Bank, as well as published studies of atomic-resolution explicit-solvent molecular dynamics simulations on these and other hyaluronan and hyaluronan-containing systems. Advances in accurate molecular mechanics force fields, simulation methods and software, and computer hardware have supported a recent flourish in such simulations, such that the simulation publications now outnumber the structural biology publications by an order of magnitude. In addition to supplementing the experimental structural biology with computed dynamic and thermodynamic information, the molecular dynamics studies provide a wealth of atomic-resolution information on hyaluronan-containing systems for which there is no atomic-resolution structural biology either available or possible. Examples of these summarized in this review include hyaluronan pairing with other hyaluronan molecules and glycosaminoglycans, with ions, with proteins and peptides, with lipids, and with drugs and drug-like molecules. Despite limitations imposed by present-day computing resources on system size and simulation timescale, atomic-resolution explicit-solvent molecular dynamics simulations have been able to contribute significant insight into hyaluronan's flexibility and capacity for intra- and intermolecular non-covalent interactions.

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.

CP-MAS and solution NMR studies of allosteric communication in CA-assemblies of HIV-1

Solution and solid-state NMR spectroscopy are highly complementary techniques for studying structure and dynamics in very high molecular weight systems. Here we have analysed the dynamics of HIV-1 capsid (CA) assemblies in presence of the cofactors IP6 and ATPγS and the host-factor CPSF6 using a combination of solution state and cross polarisation magic angle spinning (CP-MAS) solid-state NMR. In particular, dynamical effects on ns to µs and µs to ms timescales are observed revealing diverse motions in assembled CA. Using CP-MAS NMR, we exploited the sensitivity of the amide/Cα-Cβ backbone chemical shifts in DARR and NCA spectra to observe the plasticity of the HIV-1 CA tubular assemblies and also map the binding of cofactors and the dynamics of cofactor-CA complexes. In solution, we measured how the addition of host- and co-factors to CA -hexamers perturbed the chemical shifts and relaxation properties of CA-Ile and -Met methyl groups using transverse-relaxation-optimized NMR spectroscopy to exploit the sensitivity of methyl groups as probes in high-molecular weight proteins. These data show how dynamics of the CA protein assembly over a range of spatial and temporal scales play a critical role in CA function. Moreover, we show that binding of IP6, ATPγS and CPSF6 results in local chemical shift as well as dynamic changes for a significant, contiguous portion of CA, highlighting how allosteric pathways communicate ligand interactions between adjacent CA protomers.

Elucidating the mechanisms underlying protein conformational switching using NMR spectroscopy

How proteins switch between various ligand-free and ligand-bound structures has been a key biophysical question ever since the postulation of the Monod-Wyman-Changeux and Koshland-Nemethy-Filmer models over six decades ago. The ability of NMR spectroscopy to provide structural and kinetic information on biomolecular conformational exchange places it in a unique position as an analytical tool to interrogate the mechanisms of biological processes such as protein folding and biomolecular complex formation. In addition, recent methodological developments in the areas of saturation transfer and relaxation dispersion have expanded the scope of NMR for probing the mechanics of transitions in systems where one or more states constituting the exchange process are sparsely populated and 'invisible' in NMR spectra. In this review, we highlight some of the strategies available from NMR spectroscopy for examining the nature of multi-site conformational exchange, using five case studies that have employed NMR, either in isolation, or in conjunction with other biophysical tools.

Paired Simulations and Experimental Investigations into the Calcium-Dependent Conformation of Albumin

Serum albumin is the most abundant protein in blood plasma, and it is involved in multiple biological processes. Serum albumin has recently been adapted for improving biomaterial integration with bone tissue, and studies have shown the importance of this protein in bone repair and regeneration. However, the mechanism of action is not yet clear. In stark contrast, other studies have demonstrated that albumin blocks cell adhesion to surfaces, which is seen as a limitation to its bone healing role. These apparent contradictions suggest that the conformation of albumin facilitates its bioactivity, leading to enhanced bone repair. Serum albumin is known to play a major role in maintaining the calcium ion concentration in blood plasma. Due to the prevalence of calcium at bone repair and regeneration sites, it has been hypothesized that calcium binding to serum albumin triggers a conformational change, leading to bioactivity. In the current study, molecular modeling approaches including molecular docking, atomic molecular dynamics (MD) simulation, and coarse-grained MD simulation were used to test this hypothesis by investigating the conformational changes induced in bovine serum albumin by interaction with calcium ions. The computational results were qualitatively validated with experimental Fourier-transform infrared spectroscopy analysis. We find that free calcium ions in solution transiently bind with the three major loops in albumin, triggering a conformational change where N-terminal and C-terminal domains separate from each other in a partial unfolding process. The separation distance between these domains was found to correlate with the calcium ion concentration. The experimental data support the simulation results showing that albumin has enhanced conformational heterogeneity upon exposure to intermediate levels of calcium, without any significant secondary structure changes.

Allosteric binding on nuclear receptors: Insights on screening of non-competitive endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) can compete with endogenous hormones and bind to the orthosteric site of nuclear receptors (NRs), affecting normal endocrine system function and causing severe symptoms. Recently, a series of pharmaceuticals and personal care products (PPCPs) have been discovered to bind to the allosteric sites of NRs and induce similar effects. However, it remains unclear how diverse EDCs work in this new way. Therefore, we have systematically summarized the allosteric sites and underlying mechanisms based on existing studies, mainly regarding drugs belonging to the PPCP class. Advanced methods, classified as structural biology, biochemistry and computational simulation, together with their advantages and hurdles for allosteric site recognition and mechanism insight have also been described. Furthermore, we have highlighted two available strategies for virtual screening of numerous EDCs, relying on the structural features of allosteric sites and lead compounds, respectively. We aim to provide reliable theoretical and technical support for a broader view of various allosteric interactions between EDCs and NRs, and to drive high-throughput and accurate screening of potential EDCs with non-competitive effects.

Probing allosteric interactions in homo-oligomeric molecular machines using solution NMR spectroscopy

Developments in solution NMR spectroscopy have significantly impacted the biological questions that can now be addressed by this methodology. By means of illustration, we present here a perspective focusing on studies of a number of molecular machines that are critical for cellular homeostasis. The role of NMR in elucidating the structural dynamics of these important molecules is emphasized, focusing specifically on intersubunit allosteric communication in homo-oligomers. In many biophysical studies of oligomers, allostery is inferred by showing that models specifically including intersubunit communication best fit the data of interest. Ideally, however, experimental studies focusing on one subunit of a multisubunit system would be performed as an important complement to the more traditional bulk measurements in which signals from all components are measured simultaneously. Using an approach whereby asymmetric molecules are prepared in concert with NMR experiments focusing on the structural dynamics of individual protomers, we present examples of how intersubunit allostery can be directly observed in high-molecular-weight protein systems. These examples highlight some of the unique roles of solution NMR spectroscopy in studies of complex biomolecules and emphasize the important synergy between NMR and other atomic resolution biophysical methods.

Coming of Age: Cryo-Electron Tomography as a Versatile Tool to Generate High-Resolution Structures at Cellular/Biological Interfaces

Over the last few years, cryo electron microscopy has become the most important method in structural biology. While 80% of deposited maps are from single particle analysis, electron tomography has grown to become the second most important method. In particular sub-tomogram averaging has matured as a method, delivering structures between 2 and 5 Å from complexes in cells as well as in vitro complexes. While this resolution range is not standard, novel developments point toward a promising future. Here, we provide a guide for the workflow from sample to structure to gain insight into this emerging field.

Development and application of multicolor burst analysis spectroscopy

The formation and disassembly of macromolecular particles is a ubiquitous and essential feature of virtually all living organisms. Additionally, diseases are often associated with the accumulation and propagation of biologically active nanoparticles, like the formation of toxic protein aggregates in protein misfolding diseases and the growth of infectious viral particles. The heterogeneous and dynamic nature of biologically active particles can make them exceedingly challenging to study. The single-particle fluorescence technique known as burst analysis spectroscopy (BAS) was developed to facilitate real-time measurement of macromolecular particle distributions in the submicron range in a minimally perturbing, free-solution environment. Here, we develop a multicolor version of BAS and employ it to examine two problems in macromolecular assembly: 1) the extent of DNA packing heterogeneity in bacteriophage viral particles and 2) growth models of non-native protein aggregates. We show that multicolor BAS provides a powerful and flexible approach to studying hidden properties of important biological particles like viruses and protein aggregates.

Assessment of the Higher-Order Structure of Formulated Monoclonal Antibody Therapeutics by 2D Methyl Correlated NMR and Principal Component Analysis

Characterization of the higher-order structure (HOS) of protein therapeutics, and in particular of monoclonal antibodies, by 2D ¹ H-¹³ C methyl correlated NMR has been demonstrated as precise and robust. Such characterization can be greatly enhanced when collections of spectra are analyzed using multivariate approaches such as principal component analysis (PCA), allowing for the detection and identification of small structural differences in drug substance that may otherwise fall below the limit of detection of conventional spectral analysis. A major limitation to this approach is the presence of aliphatic signals from formulation or excipient components, which result in spectral interference with the protein signal of interest; however, the recently described Selective Excipient Reduction and Removal (SIERRA) filter greatly reduces this issue. Here we will outline how basic 2D ¹ H-¹³ C methyl-correlated NMR may be combined with the SIERRA approach to collect 'clean' NMR spectra of formulated monoclonal antibody therapeutics (i.e., drug substance spectra free of interfering component signals), and how series of such spectra may be used for HOS characterization by direct PCA of the series spectral matrix. © 2020 U.S. Government. Basic Protocol 1: NMR data acquisition Basic Protocol 2: Full spectral matrix data processing and analysis Support Protocol: Data visualization and cluster analysis.

Molecular Simulations and Network Modeling Reveal an Allosteric Signaling in the SARS-CoV-2 Spike Proteins

The development of computational strategies for the quantitative characterization of the functional mechanisms of SARS-CoV-2 spike proteins is of paramount importance in efforts to accelerate the discovery of novel therapeutic agents and vaccines combating the COVID-19 pandemic. Structural and biophysical studies have recently characterized the conformational landscapes of the SARS-CoV-2 spike glycoproteins in the prefusion form, revealing a spectrum of stable and more dynamic states. By employing molecular simulations and network modeling approaches, this study systematically examined functional dynamics and identified the regulatory centers of allosteric interactions for distinct functional states of the wild-type and mutant variants of the SARS-CoV-2 prefusion spike trimer. This study presents evidence that the SARS-CoV-2 spike protein can function as an allosteric regulatory engine that fluctuates between dynamically distinct functional states. Perturbation-based modeling of the interaction networks revealed a key role of the cross-talk between the effector hotspots in the receptor binding domain and the fusion peptide proximal region of the SARS-CoV-2 spike protein. The results have shown that the allosteric hotspots of the interaction networks in the SARS-CoV-2 spike protein can control the dynamic switching between functional conformational states that are associated with virus entry to the host receptor. This study offers a useful and novel perspective on the underlying mechanisms of the SARS-CoV-2 spike protein through the lens of allosteric signaling as a regulatory apparatus of virus transmission that could open up opportunities for targeted allosteric drug discovery against SARS-CoV-2 proteins and contribute to the rapid response to the current and potential future pandemic scenarios.

Coevolution, Dynamics and Allostery Conspire in Shaping Cooperative Binding and Signal Transmission of the SARS-CoV-2 Spike Protein with Human Angiotensin-Converting Enzyme 2

Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of the SARS-CoV and SARS-CoV-2 spike protein receptor binding domains with the ACE2 host receptor. Different from other computational studies, we systematically examine the molecular and energetic determinants of the binding mechanisms between SARS-CoV-2 and ACE2 proteins through the lens of coevolution, conformational dynamics, and allosteric interactions that conspire to drive binding interactions and signal transmission. Conformational dynamics analysis revealed the important differences in mobility of the binding interfaces for the SARS-CoV-2 spike protein that are not confined to several binding hotspots, but instead are broadly distributed across many interface residues. Through coevolutionary network analysis and dynamics-based alanine scanning, we established linkages between the binding energy hotspots and potential regulators and carriers of signal communication in the virus-host receptor complexes. The results of this study detailed a binding mechanism in which the energetics of the SARS-CoV-2 association with ACE2 may be determined by cumulative changes of a number of residues distributed across the entire binding interface. The central findings of this study are consistent with structural and biochemical data and highlight drug discovery challenges of inhibiting large and adaptive protein-protein interfaces responsible for virus entry and infection transmission.

An intrinsically disordered motif regulates the interaction between the p47 adaptor and the p97 AAA+ ATPase

VCP/p97, an enzyme critical to proteostasis, is regulated through interactions with protein adaptors targeting it to specific cellular tasks. One such adaptor, p47, forms a complex with p97 to direct lipid membrane remodeling. Here, we use NMR and other biophysical methods to study the structural dynamics of p47 and p47-p97 complexes. Disordered regions in p47 are shown to be critical in directing intra-p47 and p47-p97 interactions via a pair of previously unidentified linear motifs. One of these, an SHP domain, regulates p47 binding to p97 in a manner that depends on the nucleotide state of p97. NMR and electron cryomicroscopy data have been used as restraints in molecular dynamics trajectories to develop structural ensembles for p47-p97 complexes in adenosine diphosphate (ADP)- and adenosine triphosphate (ATP)-bound conformations, highlighting differences in interactions in the two states. Our study establishes the importance of intrinsically disordered regions in p47 for the formation of functional p47-p97 complexes.

Analyzing multi-step ligand binding reactions for oligomeric proteins by NMR: Theoretical and computational considerations

Solution NMR spectroscopy is widely used to investigate the thermodynamics and kinetics of the binding of ligands to their biological receptors, as it provides detailed, atomistic information, potentially leading to microscopic affinities for each binding event, and, to the development of allosteric pathways describing how the binding at one site affects distal sites in the molecule. Importantly, weak interactions that are often invisible to other biophysical methods can also be probed. Methodological advancements in NMR have enabled the investigation of high molecular weight, homo-oligomeric complexes that bind multiple ligand molecules, with increasing numbers of studies of the structural dynamics and binding properties of these systems. It therefore becomes of interest to consider how binding and kinetics parameters can be extracted from experiments on these more complicated molecules. Here we present the theoretical framework for analyzing binding reactions of homo-oligomeric complexes by NMR, taking into account all of the chemical species in solution and their corresponding NMR observables. A number of simulations are presented to illustrate the utility of the derived expressions.

Use of paramagnetic 19F NMR to monitor domain movement in a glutamate transporter homolog

In proteins where conformational changes are functionally important, the number of accessible states and their dynamics are often difficult to establish. Here we describe a novel 19F-NMR spectroscopy approach to probe dynamics of large membrane proteins. We labeled a glutamate transporter homolog with a 19F probe via cysteine chemistry and with a Ni2+ ion via chelation by a di-histidine motif. We used distance-dependent enhancement of the longitudinal relaxation of 19F nuclei by the paramagnetic metal to assign the observed resonances. We identified one inward- and two outward-facing states of the transporter, in which the substrate-binding site is near the extracellular and intracellular solutions, respectively. We then resolved the structure of the unanticipated second outward-facing state by cryo-EM. Finally, we showed that the rates of the conformational exchange are accessible from measurements of the metal-enhanced longitudinal relaxation of 19F nuclei.

Effect of daunorubicin on acute promyelocytic leukemia cells using nuclear magnetic resonance spectroscopy-based metabolomics

The aim of this study was to determine several key metabolites as potential biomarkers of daunorubicin (DNR) treatment of acute promyelocytic leukemia (APL) using APL blasts and NB4 cells. Samples which were obtained from 16 newly diagnosed APL patients and human APL NB4 cell lines were exposed to increasing concentrations of DNR (0 μM, 0.1 μM, 0.5 μM and 1.0 μM). Electron microscopy and Nuclear Magnetic Resonance (NMR) spectroscopy confirmed that there were clear differences between controls and DNR-treated groups, with the resultant models having excellent predictive and discriminative abilities. Four metabolites meeting the biomarker requirements were identified. KEGG analyses revealed that these biomarkers were associated with the metabolism of fat, choline, and glucose. These findings offered vital information about the effects of chemotherapies on the whole body biochemistry which might be important for monitoring apoptosis and injury to cells in order to reduce chemotherapies-induced side effects.

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.

A methyl-TROSY approach for NMR studies of high-molecular-weight DNA with application to the nucleosome core particle

The development of methyl-transverse relaxation-optimized spectroscopy (methyl-TROSY)-based NMR methods, in concert with robust strategies for incorporation of methyl-group probes of structure and dynamics into the protein of interest, has facilitated quantitative studies of high-molecular-weight protein complexes. Here we develop a one-pot in vitro reaction for producing NMR quantities of methyl-labeled DNA at the C5 and N6 positions of cytosine (5mC) and adenine (6mA) nucleobases, respectively, enabling the study of high-molecular-weight DNA molecules using TROSY approaches originally developed for protein applications. Our biosynthetic strategy exploits the large number of naturally available methyltransferases to specifically methylate DNA at a desired number of sites that serve as probes of structure and dynamics. We illustrate the methodology with studies of the 153-base pair Widom DNA molecule that is simultaneously methyl-labeled at five sites, showing that high-quality 13C-1H spectra can be recorded on 100 μM samples in a few minutes. NMR spin relaxation studies of labeled methyl groups in both DNA and the H2B histone protein component of the 200-kDa nucleosome core particle (NCP) establish that methyl groups at 5mC and 6mA positions are, in general, more rigid than Ile, Leu, and Val methyl probes in protein side chains. Studies focusing on histone H2B of NCPs wrapped with either wild-type DNA or DNA methylated at all 26 CpG sites highlight the utility of NMR in investigating the structural dynamics of the NCP and how its histone core is affected through DNA methylation, an important regulator of transcription.

Automated assignment of methyl NMR spectra from large proteins

As structural biology trends towards larger and more complex biomolecular targets, a detailed understanding of their interactions and underlying structures and dynamics is required. The development of methyl-TROSY has enabled NMR spectroscopy to provide atomic-resolution insight into the mechanisms of large molecular assemblies in solution. However, the applicability of methyl-TROSY has been hindered by the laborious and time-consuming resonance assignment process, typically performed with domain fragmentation, site-directed mutagenesis, and analysis of NOE data in the context of a crystal structure. In response, several structure-based automatic methyl assignment strategies have been developed over the past decade. Here, we present a comprehensive analysis of all available methods and compare their input data requirements, algorithmic strategies, and reported performance. In general, the methods fall into two categories: those that primarily rely on inter-methyl NOEs, and those that utilize methyl PRE- and PCS-based restraints. We discuss their advantages and limitations, and highlight the potential benefits from standardizing and combining different methods.

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.

Successes and challenges in simulating the folding of large proteins

Computational simulations of protein folding can be used to interpret experimental folding results, to design new folding experiments, and to test the effects of mutations and small molecules on folding. However, whereas major experimental and computational progress has been made in understanding how small proteins fold, research on larger, multidomain proteins, which comprise the majority of proteins, is less advanced. Specifically, large proteins often fold via long-lived partially folded intermediates, whose structures, potentially toxic oligomerization, and interactions with cellular chaperones remain poorly understood. Molecular dynamics based folding simulations that rely on knowledge of the native structure can provide critical, detailed information on folding free energy landscapes, intermediates, and pathways. Further, increases in computational power and methodological advances have made folding simulations of large proteins practical and valuable. Here, using serpins that inhibit proteases as an example, we review native-centric methods for simulating the folding of large proteins. These synergistic approaches range from Gō and related structure-based models that can predict the effects of the native structure on folding to all-atom-based methods that include side-chain chemistry and can predict how disease-associated mutations may impact folding. The application of these computational approaches to serpins and other large proteins highlights the successes and limitations of current computational methods and underscores how computational results can be used to inform experiments. These powerful simulation approaches in combination with experiments can provide unique insights into how large proteins fold and misfold, expanding our ability to predict and manipulate protein folding.

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier

Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current in silico methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the in silico, in vitro, and in vivo worlds, shedding new light onto complex biological problems inaccessible so far.

Structure Determination by Single-Particle Cryo-Electron Microscopy: Only the Sky (and Intrinsic Disorder) is the Limit

Traditionally, X-ray crystallography and NMR spectroscopy represent major workhorses of structural biologists, with the lion share of protein structures reported in protein data bank (PDB) being generated by these powerful techniques. Despite their wide utilization in protein structure determination, these two techniques have logical limitations, with X-ray crystallography being unsuitable for the analysis of highly dynamic structures and with NMR spectroscopy being restricted to the analysis of relatively small proteins. In recent years, we have witnessed an explosive development of the techniques based on Cryo-electron microscopy (Cryo-EM) for structural characterization of biological molecules. In fact, single-particle Cryo-EM is a special niche as it is a technique of choice for the structural analysis of large, structurally heterogeneous, and dynamic complexes. Here, sub-nanometer atomic resolution can be achieved (i.e., resolution below 10 Å) via single-particle imaging of non-crystalline specimens, with accurate 3D reconstruction being generated based on the computational averaging of multiple 2D projection images of the same particle that was frozen rapidly in solution. We provide here a brief overview of single-particle Cryo-EM and show how Cryo-EM has revolutionized structural investigations of membrane proteins. We also show that the presence of intrinsically disordered or flexible regions in a target protein represents one of the major limitations of this promising technique.

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.

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.

Recombinant Silk Fiber Properties Correlate to Prefibrillar Self-Assembly

Spider silks are desirable materials with mechanical properties superior to most synthetic materials coupled with biodegradability and biocompatibility. In order to replicate natural silk properties using recombinant spider silk proteins (spidroins) and wet-spinning methods, the focus to date has typically been on modifying protein sequence, protein size, and spinning conditions. Here, an alternative approach is demonstrated. Namely, using the same ≈57 kDa recombinant aciniform silk protein with a consistent wet-spinning protocol, fiber mechanical properties are shown to significantly differ as a function of the solvent used to dissolve the protein at high concentration (the "spinning dope" solution). A fluorinated acid/alcohol/water dope leads to drastic improvement in fibrillar extensibility and, correspondingly, toughness compared to fibers produced using a previously developed fluorinated alcohol/water dope. To understand the underlying cause for these mechanical differences, morphology and structure of the two classes of silk fiber are compared, with features tracing back to dope-state protein structuring and preassembly. Specifically, distinct classes of spidroin nanoparticles appear to form in each dope prior to fiber spinning and these preassembled states are, in turn, linked to fiber morphology, structure, and mechanical properties. Tailoring of dope-state spidroin nanoparticle assembly, thus, appears a promising strategy to modulate fibrillar silk properties.

Selective Methyl Labeling of Proteins: Enabling Structural and Mechanistic Studies As Well As Drug Discovery Applications by Solution-State NMR

Escherichia coli expression protocols for selective labeling of methyl groups in proteins have been essential in expanding the size range of targets that can be studied by biomolecular NMR. Based on the initial work achieving selective labeling of isoleucine, leucine, and valine residues, additional methods were developed over the past years which enabled the individual and/or simultaneous combinatorial labeling of all methyl containing amino acids. Together with the introduction of new methyl-optimized NMR experiments, this now allows the detailed characterization of protein-ligand interactions as well as mechanistic and dynamic processes of protein-protein complexes up to 1MDa in size. In this chapter, we provide a general introduction to selective labeling of proteins using E. coli-based expression systems, describe the considerations taken into account prior to the selective labeling of a protein, and include the protocols used to produce such proteins. An overview of applications using selectively labeled proteins with an emphasis on examples relevant to the drug discovery process is then presented.

Assigning methyl resonances for protein solution-state NMR studies

Solution-state NMR is an important tool for studying protein structure and function. The ability to probe methyl groups has substantially expanded the scope of proteins accessible by NMR spectroscopy, including facilitating study of proteins and complexes greater than 100 kDa in size. While the toolset for studying protein structure and dynamics by NMR continues to grow, a major rate-limiting step in these studies is the initial resonance assignments, especially for larger (>50 kDa) proteins. In this practical review, we present strategies to efficiently isotopically label proteins, delineate NMR pulse sequences that can be used to determine methyl resonance assignments in the presence and absence of backbone assignments, and outline computational methods for NMR data analysis. We use our experiences from assigning methyl resonances for the aromatic biosynthetic enzymes tryptophan synthase and chorismate mutase to provide advice for all stages of experimental set-up and data analysis.

Reversible inhibition of the ClpP protease via an N-terminal conformational switch

Protein homeostasis is critically important for cell viability. Key to this process is the refolding of misfolded or aggregated proteins by molecular chaperones or, alternatively, their degradation by proteases. In most prokaryotes and in chloroplasts and mitochondria, protein degradation is performed by the caseinolytic protease ClpP, a tetradecamer barrel-like proteolytic complex. Dysregulating ClpP function has shown promise in fighting antibiotic resistance and as a potential therapy for acute myeloid leukemia. Here we use methyl-transverse relaxation-optimized spectroscopy (TROSY)-based NMR, cryo-EM, biochemical assays, and molecular dynamics simulations to characterize the structural dynamics of ClpP from Staphylococcus aureus (SaClpP) in wild-type and mutant forms in an effort to discover conformational hotspots that regulate its function. Wild-type SaClpP was found exclusively in the active extended form, with the N-terminal domains of its component protomers in predominantly β-hairpin conformations that are less well-defined than other regions of the protein. A hydrophobic site was identified that, upon mutation, leads to unfolding of the N-terminal domains, loss of SaClpP activity, and formation of a previously unobserved split-ring conformation with a pair of 20-Å-wide pores in the side of the complex. The extended form of the structure and partial activity can be restored via binding of ADEP small-molecule activators. The observed structural plasticity of the N-terminal gates is shown to be a conserved feature through studies of Escherichia coli and Neisseria meningitidis ClpP, suggesting a potential avenue for the development of molecules to allosterically modulate the function of ClpP.

Cotranslocational processing of the protein substrate calmodulin by an AAA+ unfoldase occurs via unfolding and refolding intermediates

Protein remodeling by AAA+ enzymes is central for maintaining proteostasis in a living cell. However, a detailed structural description of how this is accomplished at the level of the substrate molecules that are acted upon is lacking. Here, we combine chemical cross-linking and methyl transverse relaxation-optimized NMR spectroscopy to study, at atomic resolution, the stepwise unfolding and subsequent refolding of the two-domain substrate calmodulin by the VAT AAA+ unfoldase from Thermoplasma acidophilum By engineering intermolecular disulphide bridges between the substrate and VAT we trap the substrate at different stages of translocation, allowing structural studies throughout the translocation process. Our results show that VAT initiates substrate translocation by pulling on intrinsically unstructured N or C termini of substrate molecules without showing specificity for a particular amino acid sequence. Although the B1 domain of protein G is shown to unfold cooperatively, translocation of calmodulin leads to the formation of intermediates, and these differ on an individual domain level in a manner that depends on whether pulling is from the N or C terminus. The approach presented generates an atomic resolution picture of substrate unfolding and subsequent refolding by unfoldases that can be quite different from results obtained via in vitro denaturation experiments.