Chemical shift-based methods in NMR structure determination

Solution NMR chemical shift assignment of apo and molybdate-bound ModA at two pHs

ModA is a soluble periplasmic molybdate-binding protein found in most gram-negative bacteria. It is part of the ABC transporter complex ModABC that moves molybdenum into the cytoplasm, to be used by enzymes that carry out various redox reactions. Since there is no clear analog for ModA in humans, this protein could be a good target for antibacterial drug design. Backbone 1H, 13C and 15N chemical shifts of apo and molybdate-bound ModA from E. coli were assigned at pHs 6.0 and 4.5. In addition, side chain atoms were assigned for apo ModA at pH 6.0. When comparing apo and molybdate-bound ModA at pH 6.0, large chemical shift perturbations are observed, not only in areas near the bound metal, but also in regions that are distant from the metal-binding site. Given the significant conformational change between apo and holo ModA, we might expect the large chemical shift changes to be more widespread; however, since they are limited to specific regions, the residues with large perturbations may reveal allosteric sites that could ultimately be important for the design of antibiotics that target ModA.

Conformational plasticity of RAS Q61 family of neoepitopes results in distinct features for targeted recognition

The conformational landscapes of peptide/human leucocyte antigen (pHLA) protein complexes encompassing tumor neoantigens provide a rationale for target selection towards autologous T cell, vaccine, and antibody-based therapeutic modalities. Here, using complementary biophysical and computational methods, we characterize recurrent RAS55-64 Q61 neoepitopes presented by the common HLA-A*01:01 allotype. We integrate sparse NMR restraints with Rosetta docking to determine the solution structure of NRASQ61K/HLA-A*01:01, which enables modeling of other common RAS55-64 neoepitopes. Hydrogen/deuterium exchange mass spectrometry experiments alongside molecular dynamics simulations reveal differences in solvent accessibility and conformational plasticity across a panel of common Q61 neoepitopes that are relevant for recognition by immunoreceptors. Finally, we predict binding and provide structural models of NRASQ61K antigens spanning the entire HLA allelic landscape, together with in vitro validation for HLA-A*01:191, HLA-B*15:01, and HLA-C*08:02. Our work provides a basis to delineate the solution surface features and immunogenicity of clinically relevant neoepitope/HLA targets for cancer therapy.

NMR-Chemical-Shift-Driven Protocol Reveals the Cofactor-Bound, Complete Structure of Dynamic Intermediates of the Catalytic Cycle of Oncogenic KRAS G12C Protein and the Significance of the Mg2+ Ion

In this work, catalytically significant states of the oncogenic G12C variant of KRAS, those of Mg2+-free and Mg2+-bound GDP-loaded forms, have been determined using CS-Rosetta software and NMR-data-driven molecular dynamics simulations. There are several Mg2+-bound G12C KRAS/GDP structures deposited in the Protein Data Bank (PDB), so this system was used as a reference, while the structure of the Mg2+-free but GDP-bound state of the RAS cycle has not been determined previously. Due to the high flexibility of the Switch-I and Switch-II regions, which also happen to be the catalytically most significant segments, only chemical shift information could be collected for the most important regions of both systems. CS-Rosetta was used to derive an "NMR ensemble" based on the measured chemical shifts, which, however, did not contain the nonprotein components of the complex. We developed a torsional restraint set for backbone torsions based on the CS-Rosetta ensembles for MD simulations, overriding the force-field-based parametrization in the presence of the reinserted cofactors. This protocol (csdMD) resulted in complete models for both systems that also retained the structural features and heterogeneity defined by the measured chemical shifts and allowed a detailed comparison of the Mg2+-bound and Mg2+-free states of G12C KRAS/GDP.

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.

Chemical Shifts of Artificial Monomers Used to Construct Heterogeneous-Backbone Protein Mimetics in Random Coil and Folded States

The construction of protein-sized synthetic chains that blend natural amino acids with artificial monomers to create so-called heterogeneous-backbones is a powerful approach to generate complex folds and functions from bio-inspired agents. A variety of techniques from structural biology commonly used to study natural proteins have been adapted to investigate folding in these entities. In NMR characterization of proteins, proton chemical shift is a straightforward to acquire, information-rich metric that bears directly on a variety of properties related to folding. Leveraging chemical shift to gain insight into folding requires a set of reference chemical shift values corresponding to each building block type (i.e., the 20 canonical amino acids in the case of natural proteins) in a random coil state and knowledge of systematic changes in chemical shift associated with particular folded conformations. Although well documented for natural proteins, these issues remain unexplored in the context of protein mimetics. Here, we report random coil chemical shift values for a library of artificial amino acid monomers frequently used to construct heterogeneous-backbone protein analogues as well as a spectroscopic signature associated with one monomer class, β3-residues bearing proteinogenic side chains, adopting a helical folded conformation. Collectively, these results will facilitate the continued utilization of NMR for the study of structure and dynamics in protein-like artificial backbones.

Quantum Computational, Spectroscopic (FT-IR, FT-Raman, NMR, and UV-Vis) Hirshfeld Surface and Molecular Docking-Dynamics Studies on 5-Hydroxymethyluracil (Monomer and Trimer)

For many decades, uracil has been an antineoplastic agent used in combination with tegafur to treat various human cancers, including breast, prostate, and liver cancer. Therefore, it is necessary to explore the molecular features of uracil and its derivatives. Herein, the molecule's 5-hydroxymethyluracil has been thoroughly characterized by NMR, UV-Vis, and FT-IR spectroscopy by means of experimental and theoretical analysis. Density functional theory (DFT) using the B3LYP method at 6-311++G(d,p) was computed to achieve the optimized geometric parameters of the molecule in the ground state. For further investigation and computation of the NLO, NBO, NHO analysis, and FMO, the improved geometrical parameters were utilized. The potential energy distribution was used to allocate the vibrational frequencies using the VEDA 4 program. The NBO study determined the relationship between the donor and acceptor. The molecule's charge distribution and reactive regions were highlighted using the MEP and Fukui functions. Maps of the hole and electron density distribution in the excited state were generated using the TD-DFT method and PCM solvent model in order to reveal electronic characteristics. The energies and diagrams for the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO) were also provided. The HOMO-LUMO band gap estimated the charge transport within the molecule. When examining the intermolecular interactions in 5-HMU, Hirshfeld surface analysis was used, and fingerprint plots were also produced. The molecular docking investigation involved docking 5-HMU with six different protein receptors. Molecular dynamic simulation has given a better idea of the binding of the ligand with protein.

Insight into the Structural Basis for Dual Nucleic Acid-Recognition by the Scaffold Attachment Factor B2 Protein

The family of scaffold attachment factor B (SAFB) proteins comprises three members and was first identified as binders of the nuclear matrix/scaffold. Over the past two decades, SAFBs were shown to act in DNA repair, mRNA/(l)ncRNA processing and as part of protein complexes with chromatin-modifying enzymes. SAFB proteins are approximately 100 kDa-sized dual nucleic acid-binding proteins with dedicated domains in an otherwise largely unstructured context, but whether and how they discriminate DNA and RNA binding has remained enigmatic. We here provide the SAFB2 DNA- and RNA-binding SAP and RRM domains in their functional boundaries and use solution NMR spectroscopy to ascribe DNA- and RNA-binding functions. We give insight into their target nucleic acid preferences and map the interfaces with respective nucleic acids on sparse data-derived SAP and RRM domain structures. Further, we provide evidence that the SAP domain exhibits intra-domain dynamics and a potential tendency to dimerize, which may expand its specifically targeted DNA sequence range. Our data provide a first molecular basis of and a starting point towards deciphering DNA- and RNA-binding functions of SAFB2 on the molecular level and serve a basis for understanding its localization to specific regions of chromatin and its involvement in the processing of specific RNA species.

Benchmarking AlphaFold2 on peptide structure prediction

Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.

Using Unassigned NMR Chemical Shifts to Model RNA Secondary Structure

NMR-derived chemical shifts are sensitive probes of RNA structure. However, the need to assign NMR spectra hampers their utility as a direct source of structural information. In this report, we describe a simple method that uses unassigned 2D NMR spectra to model the secondary structure of RNAs. As in the case of assigned chemical shifts, we could use unassigned chemical shift data to reweight conformational libraries such that the highest weighted structure closely resembles their reference NMR structure. Furthermore, the application of our approach to the 3'- and 5'-UTR of the SARS-CoV-2 genome yields structures that are, for the most part, consistent with the secondary structure models derived from chemical probing data. Therefore, we expect the framework we describe here will be useful as a general strategy for rapidly generating preliminary structural RNA models directly from unassigned 2D NMR spectra. As we demonstrated for the 337-nt and 472-nt UTRs of SARS-CoV-2, our approach could be especially valuable for modeling the secondary structures of large RNA.

Epitope-focused immunogen design based on the ebolavirus glycoprotein HR2-MPER region

The three human pathogenic ebolaviruses: Zaire (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) virus, cause severe disease with high fatality rates. Epitopes of ebolavirus glycoprotein (GP) recognized by antibodies with binding breadth for all three ebolaviruses are of major interest for rational vaccine design. In particular, the heptad repeat 2 -membrane-proximal external region (HR2-MPER) epitope is relatively conserved between EBOV, BDBV, and SUDV GP and targeted by human broadly-neutralizing antibodies. To study whether this epitope can serve as an immunogen for the elicitation of broadly-reactive antibody responses, protein design in Rosetta was employed to transplant the HR2-MPER epitope identified from a co-crystal structure with the known broadly-reactive monoclonal antibody (mAb) BDBV223 onto smaller scaffold proteins. From computational analysis, selected immunogen designs were produced as recombinant proteins and functionally validated, leading to the identification of a sterile alpha motif (SAM) domain displaying the BDBV-HR2-MPER epitope near its C terminus as a promising candidate. The immunogen was fused to one component of a self-assembling, two-component nanoparticle and tested for immunogenicity in rabbits. Robust titers of cross-reactive serum antibodies to BDBV and EBOV GPs and moderate titers to SUDV GP were induced following immunization. To confirm the structural composition of the immunogens, solution NMR studies were conducted and revealed structural flexibility in the C-terminal residues of the epitope. Overall, our study represents the first report on an epitope-focused immunogen design based on the structurally challenging BDBV-HR2-MPER epitope.

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.

Structure determination of high-energy states in a dynamic protein ensemble

Macromolecular function frequently requires that proteins change conformation into high-energy states^1-4. However, methods for solving the structures of these functionally essential, lowly populated states are lacking. Here we develop a method for high-resolution structure determination of minorly populated states by coupling NMR spectroscopy-derived pseudocontact shifts⁵ (PCSs) with Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion⁶ (PCS-CPMG). Our approach additionally defines the corresponding kinetics and thermodynamics of high-energy excursions, thereby characterizing the entire free-energy landscape. Using a large set of simulated data for adenylate kinase (Adk), calmodulin and Src kinase, we find that high-energy PCSs accurately determine high-energy structures (with a root mean squared deviation of less than 3.5 angström). Applying our methodology to Adk during catalysis, we find that the high-energy excursion involves surprisingly small openings of the AMP and ATP lids. This previously unresolved high-energy structure solves a longstanding controversy about conformational interconversions that are rate-limiting for catalysis. Primed for either substrate binding or product release, the high-energy structure of Adk suggests a two-step mechanism combining conformational selection to this state, followed by an induced-fit step into a fully closed state for catalysis of the phosphoryl-transfer reaction. Unlike other methods for resolving high-energy states, such as cryo-electron microscopy and X-ray crystallography, our solution PCS-CPMG approach excels in cases involving domain rearrangements of smaller systems (less than 60 kDa) and populations as low as 0.5%, and enables the simultaneous determination of protein structure, kinetics and thermodynamics while proteins perform their function.

Structure and Carbohydrate Recognition by the Nonmitogenic Lectin Horcolin

Lectins are sugar-binding proteins that have shown considerable promise as antiviral agents because of their ability to interact with envelope glycoproteins present on the surface of viruses such as HIV-1. However, their therapeutic potential has been compromised by their mitogenicity that stimulates uncontrolled division of T-lymphocytes. Horcolin, a member of the jacalin family of lectins, tightly binds the HIV-1 envelope glycoprotein gp120 and neutralizes HIV-1 particles but is nonmitogenic. In this report, we combine X-ray crystallography and NMR spectroscopy to obtain atomic-resolution insights into the structure of horcolin and the molecular basis for its carbohydrate recognition. Each protomer of the horcolin dimer adopts a canonical β-prism I fold with three Greek key motifs and carries two carbohydrate-binding sites. The carbohydrate molecule binds in a negatively charged pocket and is stabilized by backbone and side chain hydrogen bonds to conserved residues in the ligand-binding loop. NMR titrations reveal a two-site binding mode and equilibrium dissociation constants for the two binding sites determined from two-dimensional (2D) lineshape modeling are 4-fold different. Single-binding-site variants of horcolin confirm the dichotomy in binding sites and suggest that there is allosteric communication between the two sites. An analysis of the horcolin structure shows a network of hydrogen bonds linking the two carbohydrate-binding sites directly and through a secondary binding site, and this coupling between the two sites is expected to assume importance in the interaction of horcolin with high-mannose glycans found on viral envelope glycoproteins.

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

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

Aggregation-Prone Structural Ensembles of Transthyretin Collected With Regression Analysis for NMR Chemical Shift

Monomer dissociation and subsequent misfolding of the transthyretin (TTR) is one of the most critical causative factors of TTR amyloidosis. TTR amyloidosis causes several human diseases, such as senile systemic amyloidosis and familial amyloid cardiomyopathy/polyneuropathy; therefore, it is important to understand the molecular details of the structural deformation and aggregation mechanisms of TTR. However, such molecular characteristics are still elusive because of the complicated structural heterogeneity of TTR and its highly sensitive nature to various environmental factors. Several nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) studies of TTR variants have recently reported evidence of transient aggregation-prone structural states of TTR. According to these studies, the stability of the DAGH β-sheet, one of the two main β-sheets in TTR, is a crucial determinant of the TTR amyloidosis mechanism. In addition, its conformational perturbation and possible involvement of nearby structural motifs facilitates TTR aggregation. This study proposes aggregation-prone structural ensembles of TTR obtained by MD simulation with enhanced sampling and a multiple linear regression approach. This method provides plausible structural models that are composed of ensemble structures consistent with NMR chemical shift data. This study validated the ensemble models with experimental data obtained from circular dichroism (CD) spectroscopy and NMR order parameter analysis. In addition, our results suggest that the structural deformation of the DAGH β-sheet and the AB loop regions may correlate with the manifestation of the aggregation-prone conformational states of TTR. In summary, our method employing MD techniques to extend the structural ensembles from NMR experimental data analysis may provide new opportunities to investigate various transient yet important structural states of amyloidogenic proteins.

Effects of polyethylene glycol on the surface of nanoparticles for targeted drug delivery

The rapid development of drug nanocarriers has benefited from the surface hydrophilic polymers of particles, which has improved the pharmacokinetics of the drugs. Polyethylene glycol (PEG) is a kind of polymeric material with unique hydrophilicity and electrical neutrality. PEG coating is a crucial factor to improve the biophysical and chemical properties of nanoparticles and is widely studied. Protein adherence and macrophage removal are effectively relieved due to the existence of PEG on the particles. This review discusses the PEGylation methods of nanoparticles and related techniques that have been used to detect the PEG coverage density and thickness on the surface of the nanoparticles in recent years. The molecular weight (MW) and coverage density of the PEG coating on the surface of nanoparticles are then described to explain the effects on the biophysical and chemical properties of nanoparticles.

Modeling of Hidden Structures Using Sparse Chemical Shift Data from NMR Relaxation Dispersion

NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here, we use a "sample and select" method to generate relevant structural models of alternative conformations of the C-terminal-associated region of Escherichia coli dihydrofolate reductase (DHFR), using only unsigned chemical shift changes for backbone amides and carbonyls (¹H, ¹⁵N, and ¹³C'). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces and validated by the x-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution.

Characterization of DNA G-quadruplex Topologies with NMR Chemical Shifts

G-quadruplexes are nucleic acid motifs formed by stacking of guanosine-tetrad pseudoplanes. They perform varied biological roles, and their distinctive structural features enable diverse applications. High-resolution structural characterization of G-quadruplexes is often time-consuming and expensive, calling for effective methods. Herein, we develop NMR chemical shifts and machine learning-based methodology that allows direct, rapid, and reliable analysis of canonical three-plane DNA G-quadruplexes sans isotopic enrichment. We show, for the first time, that each unique topology enforces a specific distribution of glycosidic torsion angles. Newly acquired carbon chemical shifts are exquisite probes for the dihedral angle distribution and provide immediate and unambiguous backbone topology assignment. The support vector machine learning methodology aids resonance assignment by providing plane indices for tetrad-forming guanosines. We further demonstrate the robustness by successful application of the methodology to a sequence that folds in two dissimilar topologies under different ionic conditions, providing its first atomic-level characterization.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

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.

Better together: Elements of successful scientific software development in a distributed collaborative community

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Monitoring the Site-Specific Solid-State NMR Data in Oligopeptides

Reliable values of the solid-state NMR (SSNMR) parameters together with precise structural data specific for a given amino acid site in an oligopeptide are needed for the proper interpretation of measurements aiming at an understanding of oligopeptides' function. The periodic density functional theory (DFT)-based computations of geometries and SSNMR chemical shielding tensors (CSTs) of solids are shown to be accurate enough to support the SSNMR investigations of suitably chosen models of oriented samples of oligopeptides. This finding is based on a thorough comparison between the DFT and experimental data for a set of tripeptides with both ¹³C_α and ¹⁵N_amid CSTs available from the single-crystal SSNMR measurements and covering the three most common secondary structural elements of polypeptides. Thus, the ground is laid for a quantitative description of local spectral parameters of crystalline oligopeptides, as demonstrated for the backbone ¹⁵N_amid nuclei of samarosporin I, which is a pentadecapeptide (composed of five classical and ten nonproteinogenic amino acids) featuring a strong antimicrobial activity.

Computational design of closely related proteins that adopt two well-defined but structurally divergent folds

The plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used for de novo design of proteins that fold to a single state with a deep free-energy minimum [P.-S. Huang, S. E. Boyken, D. Baker, Nature 537, 320-327 (2016)], and to reengineer natural proteins to alter their dynamics [J. A. Davey, A. M. Damry, N. K. Goto, R. A. Chica, Nat. Chem. Biol. 13, 1280-1285 (2017)] or fold [P. A. Alexander, Y. He, Y. Chen, J. Orban, P. N. Bryan, Proc. Natl. Acad. Sci. U.S.A. 106, 21149-21154 (2009)], the de novo design of closely related sequences which adopt well-defined but structurally divergent structures remains an outstanding challenge. We designed closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations-one short (∼66 Å height) and the other long (∼100 Å height)-reminiscent of the conformational transition of viral fusion proteins. Crystallographic and NMR spectroscopic characterization shows that both the short- and long-state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large-scale conformational switches between structurally divergent forms.

Mass spectrometry assisted arginine side chains assignment of NMR resonances in natural abundance proteins

Arginine side chains play critical roles in many protein-ligand interactions and enzyme catalysis. Unambiguous resonance assignment is a prerequisite for the nuclear magnetic resonance (NMR) spectroscopy studies of arginine side chains dynamics and hydrogen exchange properties from which one can expect to elucidate in more detail the roles of arginine residues in protein structure and function. Here we present a new mass spectrometry (MS)-based method for assigning the side-chain resonances of arginine residues in 2D ¹H-¹⁵N NMR spectra. The method requires no additional isotopic labeling, and relies on knowledge of the amino acid sequence, the modification of the guanidino groups and liquid chromatography-mass spectrometry rather than the protein's structure or properties. Correlating the modification rates can connect cross-peak positions from NMR data with MS data to support resonances assignments. In the present work, we have extended our original application to natural abundance human ubiquitin to provide ε-NH assessments of three arginine for this well-studied protein.

Determining methyl sidechain conformations in a CS-ROSETTA model using methyl 1H-13C residual dipolar couplings

Modelling of protein structures based on backbone chemical shifts, using programs such as CS-ROSETTA, is becoming increasingly popular, especially for systems where few restraints are available or where homologous structures are already known. While the reliability of CS-ROSETTA calculations can be improved by incorporation of some additional backbone NMR data such as those afforded by residual dipolar couplings or minimal NOE data sets involving backbone amide protons, the sidechain conformations are largely modelled by statistical energy terms. Here, we present a simple method based on methyl residual dipolar couplings that can be used to determine the rotameric state of the threefold symmetry axis of methyl groups that occupy a single rotamer, determine rotameric distributions, and identify regions of high flexibility. The method is demonstrated for methyl side chains of a deletion variant of the human chaperone DNAJB6b.

NMR Methods for Structural Characterization of Protein-Protein Complexes

Protein-protein interactions and the complexes thus formed are critical elements in a wide variety of cellular events that require an atomic-level description to understand them in detail. Such complexes typically constitute challenging systems to characterize and drive the development of innovative biophysical methods. NMR spectroscopy techniques can be applied to extract atomic resolution information on the binding interfaces, intermolecular affinity, and binding-induced conformational changes in protein-protein complexes formed in solution, in the cell membrane, and in large macromolecular assemblies. Here we discuss experimental techniques for the characterization of protein-protein complexes in both solution NMR and solid-state NMR spectroscopy. The approaches include solvent paramagnetic relaxation enhancement and chemical shift perturbations (CSPs) for the identification of binding interfaces, and the application of intermolecular nuclear Overhauser effect spectroscopy and residual dipolar couplings to obtain structural constraints of protein-protein complexes in solution. Complementary methods in solid-state NMR are described, with emphasis on the versatility provided by heteronuclear dipolar recoupling to extract intermolecular constraints in differentially labeled protein complexes. The methods described are of particular relevance to the analysis of membrane proteins, such as those involved in signal transduction pathways, since they can potentially be characterized by both solution and solid-state NMR techniques, and thus outline key developments in this frontier of structural biology.

An order-to-disorder structural switch activates the FoxM1 transcription factor

Intrinsically disordered transcription factor transactivation domains (TADs) function through structural plasticity, adopting ordered conformations when bound to transcriptional co-regulators. Many transcription factors contain a negative regulatory domain (NRD) that suppresses recruitment of transcriptional machinery through autoregulation of the TAD. We report the solution structure of an autoinhibited NRD-TAD complex within FoxM1, a critical activator of mitotic gene expression. We observe that while both the FoxM1 NRD and TAD are primarily intrinsically disordered domains, they associate and adopt a structured conformation. We identify how Plk1 and Cdk kinases cooperate to phosphorylate FoxM1, which releases the TAD into a disordered conformation that then associates with the TAZ2 or KIX domains of the transcriptional co-activator CBP. Our results support a mechanism of FoxM1 regulation in which the TAD undergoes switching between disordered and different ordered structures.

Toward Structure Determination of Disulfide-Rich Peptides Using Chemical Shift-Based Methods

Disulfide-rich peptides are a class of molecules for which NMR spectroscopy has been the primary tool for structural characterization. Here, we explore whether the process can be achieved by using structural information encoded in chemical shifts. We examine (i) a representative set of five cyclic disulfide-rich peptides that have high-resolution NMR and X-ray structures and (ii) a larger set of 100 disulfide-rich peptides from the PDB. Accuracy of the calculated structures was dependent on the methods used for searching through conformational space and for identifying native conformations. Although Hα chemical shifts could be predicted reasonably well using SHIFTX, agreement between predicted and experimental chemical shifts was sufficient for identifying native conformations for only some peptides in the representative set. Combining chemical shift data with the secondary structure information and potential energy calculations improved the ability to identify native conformations. Additional use of sparse distance restraints or homology information to restrict the search space also improved the resolution of the calculated structures. This study demonstrates that abbreviated methods have potential for elucidation of peptide structures to high resolution and further optimization of these methods, e.g., improvement in chemical shift prediction accuracy, will likely help transition these methods into the mainstream of disulfide-rich peptide structural biology.

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.

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.