Unraveling the meaning of chemical shifts in protein NMR

Simple and Effective Conformational Sampling Strategy for Intrinsically Disordered Proteins Using the UNRES Web Server

Intrinsically disordered proteins (IDPs) contain more charged amino acids than folded proteins, resulting in a lack of hydrophobic core(s) and a tendency to adopt rapidly interconverting structures rather than well-defined structures. The structural heterogeneity of IDPs, encoded by the amino acid sequence, is closely related to their unique roles in biological pathways, which require them to interact with different binding partners. Recently, Robustelli and co-workers have demonstrated that a balanced all-atom force field can be used to sample heterogeneous structures of disordered proteins ( Proc. Natl. Acad. Sci. U.S.A. 2018, 115, E4758-E4766). However, such a solution requires extensive computational resources, such as Anton supercomputers. Here, we propose a simple and effective solution to sample the conformational space of IDPs using a publicly available web server, namely, the UNited-RESidue (UNRES) web server. Our proposed solution requires no investment in computational resources and no prior knowledge of UNRES. UNRES Replica Exchange Molecular Dynamics (REMD) simulations were carried out on a set of eight disordered proteins at temperatures spanning from 270 to 430 K. Utilizing the latest UNRES force field designed for structured proteins, with proper selections of temperatures, we were able to produce comparable results to all-atom force fields as reported in work done by Robustelli and co-workers. In addition, NMR observables and the radius of gyration calculated from UNRES ensembles were directly compared with the experimental data to further evaluate the accuracy of the UNRES model at all temperatures. Our results suggest that carrying out the UNRES simulations at optimal temperatures using the UNRES web server can be a good alternative to sample heterogeneous structures of IDPs.

Atomic Resolution Insights into pH Shift Induced Deprotonation Events in LS-Shaped Aβ(1-42) Amyloid Fibrils

Alzheimer's disease is a neurodegenerative disorder associated with the deposition of misfolded aggregates of the amyloid-β protein (Aβ). Aβ(1-42) is one of the most aggregation-prone components in senile plaques of AD patients. We demonstrated that relatively homogeneous Aβ(1-42) fibrils with one predominant fold visible in solid-state NMR spectra can be obtained at acidic pH. The structure of these fibrils differs remarkably from some other polymorphs obtained at neutral pH. In particular, the entire N-terminal region is part of the rigid fibril core. Here, we investigate the effects of a pH shift on the stability and the fold of these fibrils at higher pH values. Fibril bundling at neutral pH values renders cryo-EM studies impractical, but solid-state NMR spectroscopy, molecular dynamics simulations, and biophysical methods provide residue-specific structural information under these conditions. The LS-fold of the Aβ(1-42) fibrils does not change over the complete pH range from pH 2 to pH 7; in particular, the N-terminus remains part of the fibril core. We observe changes in the protonation state of charged residues starting from pH 5 on a residue-specific level. The deprotonation of the C-terminal carboxyl group of A42 in the intermolecular salt bridge with D1 and K28 is slow on the NMR time scale, with a local pK_a of 5.4, and local conformations of the involved residues are affected by deprotonation of A42. Thus, we demonstrate that this fibril form is stable at physiological pH values.

Structural insights on the selective interaction of the histidine-rich piscidin antimicrobial peptide Of-Pis1 with membranes

Of-Pis1 is a potent piscidin antimicrobial peptide (AMP), recently isolated from rock bream (Oplegnathus fasciatus). This rich in histidines and glycines 24-amino acid peptide displays high and broad antimicrobial activity and no significant hemolytic toxicity against human erythrocytes, suggesting low toxicity. To better understand the mechanism of action of Of-Pis1 and its potential selectivity, using NMR and CD spectroscopies, we studied the interaction with eukaryotic and procaryotic membranes and membrane models. Anionic sodium dodecyl sulfate (SDS) and lipopolysaccharide (LPS) micelles were used to mimic procaryotic membranes, while zwitterionic dodecyl phosphocholine (DPC) was used as eukaryotic membrane surrogate. In an aqueous environment, Of-Pis1 adopts a flexible random coil conformation. In DPC and SDS instead, the N-terminal region of Of-Pis1 forms an amphipathic α-helix with the non-polar face in close contact with the micelles. Slower solvent exchange and higher pK_as of the histidine residues in SDS than in DPC suggest that Of-Pis1 interacts more tightly with SDS. Of-Pis1 also binds tightly and structurally perturbs LPS micelles. Of-Pis1 interacts with both Escherichia coli and mammalian cell membranes, but only in the presence of Escherichia coli membranes it populates the helical conformation. Furthermore, ligand-based NMR experiments support a tighter and more specific interaction with bacterial than with eukaryotic membranes. Overall, these data clearly show the selective interaction of this broadly active AMP with bacterial over eukaryotic membranes. The conformational information is discussed in terms of Of-Pis1 amino acid sequence and composition to provide insights useful to design more potent and selective AMPs.

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.

Sortase-mediated segmental labeling: A method for segmental assignment of intrinsically disordered regions in proteins

A significant number of proteins possess sizable intrinsically disordered regions (IDRs). Due to the dynamic nature of IDRs, NMR spectroscopy is often the tool of choice for characterizing these segments. However, the application of NMR to IDRs is often hindered by their instability, spectral overlap and resonance assignment difficulties. Notably, these challenges increase considerably with the size of the IDR. In response to these issues, here we report the use of sortase-mediated ligation (SML) for segmental isotopic labeling of IDR-containing samples. Specifically, we have developed a ligation strategy involving a key segment of the large IDR and adjacent folded headpiece domain comprising the C-terminus of A. thaliana villin 4 (AtVLN4). This procedure significantly reduces the complexity of NMR spectra and enables group identification of signals arising from the labeled IDR fragment, a process we refer to as segmental assignment. The validity of our segmental assignment approach is corroborated by backbone residue-specific assignment of the IDR using a minimal set of standard heteronuclear NMR methods. Using segmental assignment, we further demonstrate that the IDR region adjacent to the headpiece exhibits nonuniform spectral alterations in response to temperature. Subsequent residue-specific characterization revealed two segments within the IDR that responded to temperature in markedly different ways. Overall, this study represents an important step toward the selective labeling and probing of target segments within much larger IDR contexts. Additionally, the approach described offers significant savings in NMR recording time, a valuable advantage for the study of unstable IDRs, their binding interfaces, and functional mechanisms.

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

Leiomodin creates a leaky cap at the pointed end of actin-thin filaments

Improper lengths of actin-thin filaments are associated with altered contractile activity and lethal myopathies. Leiomodin, a member of the tropomodulin family of proteins, is critical in thin filament assembly and maintenance; however, its role is under dispute. Using nuclear magnetic resonance data and molecular dynamics simulations, we generated the first atomic structural model of the binding interface between the tropomyosin-binding site of cardiac leiomodin and the N-terminus of striated muscle tropomyosin. Our structural data indicate that the leiomodin/tropomyosin complex only forms at the pointed end of thin filaments, where the tropomyosin N-terminus is not blocked by an adjacent tropomyosin protomer. This discovery provides evidence supporting the debated mechanism where leiomodin and tropomodulin regulate thin filament lengths by competing for thin filament binding. Data from experiments performed in cardiomyocytes provide additional support for the competition model; specifically, expression of a leiomodin mutant that is unable to interact with tropomyosin fails to displace tropomodulin at thin filament pointed ends and fails to elongate thin filaments. Together with previous structural and biochemical data, we now propose a molecular mechanism of actin polymerization at the pointed end in the presence of bound leiomodin. In the proposed model, the N-terminal actin-binding site of leiomodin can act as a "swinging gate" allowing limited actin polymerization, thus making leiomodin a leaky pointed-end cap. Results presented in this work answer long-standing questions about the role of leiomodin in thin filament length regulation and maintenance.

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.

Structures of N-terminally processed KRAS provide insight into the role of N-acetylation

Although post-translational modification of the C-terminus of RAS has been studied extensively, little is known about N-terminal processing. Mass spectrometric characterization of KRAS expressed in mammalian cells showed cleavage of the initiator methionine (iMet) and N-acetylation of the nascent N-terminus. Interestingly, structural studies on GDP- and GMPPNP-bound KRAS lacking the iMet and N-acetylation resulted in Mg2+-free structures of KRAS with flexible N-termini. In the Mg2+-free KRAS-GDP structure, the flexible N-terminus causes conformational changes in the interswitch region resulting in a fully open conformation of switch I. In the Mg2+-free KRAS-GMPPNP structure, the flexible N-terminus causes conformational changes around residue A59 resulting in the loss of Mg2+ and switch I in the inactive state 1 conformation. Structural studies on N-acetylated KRAS-GDP lacking the iMet revealed the presence of Mg2+ and a conformation of switch regions also observed in the structure of GDP-bound unprocessed KRAS with the iMet. In the absence of the iMet, the N-acetyl group interacts with the central beta-sheet and stabilizes the N-terminus and the switch regions. These results suggest there is crosstalk between the N-terminus and the Mg2+ binding site, and that N-acetylation plays an important role by stabilizing the N-terminus of RAS upon excision of the iMet.

Coordinated Network Changes across the Catalytic Cycle of Alpha Tryptophan Synthase

Networks of noncovalent interactions are important for protein structural dynamics. We used nuclear magnetic resonance chemical shift covariance analyses on an inactive variant of the alpha subunit of tryptophan synthase to map amino acid interaction networks across its catalytic cycle. Although some network connections were common to every enzyme state, many of the network connections strengthened or weakened over the catalytic cycle; these changes were highly coordinated. These results suggest a higher level of network organization. Our analyses identified periodic, second-order networks that show highly coordinated interaction changes across the catalytic cycle. These periodic networks may help synchronize the sequence of structural transitions necessary for enzyme function. Molecular dynamics simulations identified interaction changes across the catalytic cycle, including those involving the catalytic residue Glu49, which may help drive other interaction changes throughout the enzyme structure. Similar periodic networks may direct structural transitions and allosteric interactions in other proteins.

De novo design of a non-local β-sheet protein with high stability and accuracy

β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-β-sheet proteins from first principles lags far behind the design of all-α or mixed-αβ domains owing to their non-local nature and the tendency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local β-sheet protein structures.

Using NMR Chemical Shifts to Determine Residue-Specific Secondary Structure Populations for Intrinsically Disordered Proteins

Protein disorder is a pervasive phenomenon in biology and a natural consequence of polymer evolution that facilitates cell signaling by organizing sites for posttranslational modifications and protein-protein interactions into arrays of short linear motifs that can be rearranged by RNA splicing. Disordered proteins are missing the long-range nonpolar interactions that form tertiary structures, but they often contain regions with residual secondary structure that are stabilized by protein binding. NMR spectroscopy is uniquely suited to detect residual secondary structure in a disordered protein and it can provide atomic resolution data on the structure and dynamics of disordered protein interaction sites. Here we describe how backbone chemical shifts are used for assigning residual secondary structure in disordered proteins and discuss some of the tools available for estimating secondary structure populations with a focus on disordered proteins containing different levels of alpha helical secondary structure which are stabilized by protein binding.

CS-ROSETTA

Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

An efficient combination of BEST and NUS methods in multidimensional NMR spectroscopy for high throughput analysis of proteins

Application of Non Uniform Sampling (NUS) along with Band-selective Excitation Short-Transient (BEST) NMR experiments has been demonstrated for obtaining the important residue-specific atomic level backbone chemical shift values in short durations of time. This application has been demonstrated with both well-folded (ubiquitin) and unfolded (α-synuclein) proteins alike. With this strategy, the experiments required for determining backbone chemical shifts can be performed very rapidly, i.e., in ∼2 hours of spectrometer time, and this data can be used to calculate the backbone folds of proteins using well established algorithms. This will be of great value for structural proteomic investigations on one hand, where the speed of structure determination is a limiting factor and for application in the study of slow kinetic processes involving proteins, such as fibrillization, on the other hand.

Application of NUS along with BEST NMR experiments has been demonstrated for obtaining the important residue-specific atomic level backbone chemical shift values in short durations of time.

Conformational sampling of the intrinsically disordered dsRBD-1 domain from Arabidopsis thaliana DCL1

Partial folding and stability of DCL1-dsRBD1.