NMR Structure Determinations of Small Proteins Using only One Fractionally 20% 13C- and Uniformly 100% 15N-Labeled Sample

Uniformly 13C- and 15N-labeled samples ensure fast and reliable nuclear magnetic resonance (NMR) assignments of proteins and are commonly used for structure elucidation by NMR. However, the preparation of uniformly labeled samples is a labor-intensive and expensive step. Reducing the portion of 13C-labeled glucose by a factor of five using a fractional 20% 13C- and 100% 15N-labeling scheme could lower the total chemical costs, yet retaining sufficient structural information of uniformly [13C, 15N]-labeled sample as a result of the improved sensitivity of NMR instruments. Moreover, fractional 13C-labeling can facilitate reliable resonance assignments of sidechains because of the biosynthetic pathways of each amino-acid. Preparation of only one [20% 13C, 100% 15N]-labeled sample for small proteins (<15 kDa) could also eliminate redundant sample preparations of 100% 15N-labeled and uniformly 100% [13C, 15N]-labeled samples of proteins. We determined the NMR structures of a small alpha-helical protein, the C domain of IgG-binding protein A from Staphylococcus aureus (SpaC), and a small beta-sheet protein, CBM64 module using [20% 13C, 100% 15N]-labeled sample and compared with the crystal structures and the NMR structures derived from the 100% [13C, 15N]-labeled sample. Our results suggest that one [20% 13C, 100% 15N]-labeled sample of small proteins could be routinely used as an alternative to conventional 100% [13C, 15N]-labeling for backbone resonance assignments, NMR structure determination, 15N-relaxation analysis, and ligand–protein interaction.

De Novo Design of Granulopoietic Proteins

Protein therapeutics are clinically developed and used as minorly engineered forms of their natural templates. This direct adoption of natural proteins in therapeutic contexts very frequently faces major challenges, including instability, poor solubility, and aggregation, which may result in undesired clinical outcomes. In contrast to classical protein engineering techniques, de novo protein design enables the introduction of radical sequence and structure manipulations, which can be used to address these challenges. In this work, we test the utility of two different design strategies to design novel granulopoietic proteins, using structural information from human granulocyte-colony stimulating factor (hG-CSF) as a template. The two strategies are: (1) An epitope rescaffolding where we migrate a tertiary structural epitope to simpler, idealised, proteins scaffolds (Fig. 1A-C), and (2) a topological refactoring strategy, where we change the protein fold by rearranging connections across the secondary structures and optimised the designed sequence of the new fold (Fig. 1A,D,E). Testing only eight designs, we obtained novel granulopoietic proteins that bind to the G-CSF receptor, have nanomolar activity in cell-based assays, and were highly thermostable and protease-resistant. NMR structure determination showed three designs to match their designed coordinates within less than 2.5 Å. While the designs possessed starkly different sequence and structure from the native G-CSF, they showed very specific activity in differentiating primary human haematopoietic stem cells into fully mature granulocytes. Morever, one design shows significant and specific activity in vivo in zebrafish and mice. These results are prospectively directing us to investigate the role of dimerisation geometry of G-GCSF receptor on activation magnitude and downstream signalling pathways. More broadly, the results also motivate our ongoing work on to design other heamatopoietic agents. In conclusion, our findings highlight the utility of computational protein design as a highly effective and guided means for discovering nover receptor modulators, and to obtain new mechanistic information about the target molecule. Figure 1. Two different strategies to generate superfolding G-CSF designs. (A) X-ray structure of G-CSF (orange) bound to its cognate receptor (red) through its binding epitope (blue). According to the epitope rescaffolding strategy, (B) the critical binding epitope residues were disembodied and used as a geometric search query against the entire Protein Data Bank (PDB) to retrieve structurally compatible scaffolds. The top six compatible scaffolds structures are shown in cartoon representation. (C) The top two templates chosen for sequence design, were a de novo designed coiled-coil and a four-helix bundle with unknown function. The binding epitopes were grafted, and the scaffolds were optimised to rigidly host the guest epitope. (D-E) According to the topological refactoring strategy (D) the topology of the native G-CSF was rewired from around the fixed binding epitope, and then was further mutated to idealise the core residues (blue volume (E)) and residues distal from the binding epitope (orange crust (E)). Both strategies aimed at simplifying the topology, reducing the size, and rigidifying the bound epitope conformation through alternate means. Figure 1 Disclosures No relevant conflicts of interest to declare.

NMR structure determination of Ixolaris and factor X(a) interaction reveals a noncanonical mechanism of Kunitz inhibition

Ixolaris is a potent tick salivary anticoagulant that binds coagulation factor Xa (FXa) and zymogen FX, with formation of a quaternary tissue factor (TF)/FVIIa/ FX(a)/Ixolaris inhibitory complex. Ixolaris blocks TF-induced coagulation and PAR2 signaling and prevents thrombosis, tumor growth, and immune activation. We present a high-resolution structure and dynamics of Ixolaris and describe the structural basis for recognition of FX. Ixolaris consists of 2 Kunitz domains (K1 and K2) in which K2 is strikingly dynamic and encompasses several residues involved in FX binding. This indicates that the backbone plasticity of K2 is critical for Ixolaris biological activity. Notably, a nuclear magnetic resonance–derived model reveals a mechanism for an electrostatically guided, high-affinity interaction between Ixolaris and FX heparin-binding (pro)exosite, resulting in an allosteric switch in the catalytic site. This is the first report revealing the structure-function relationship of an anticoagulant targeting a zymogen serving as a scaffold for TF inhibition.

Protein structure determination using Riemannian approach

The protein nuclear magnetic resonance (NMR) structure determination is one of the most extensively studied problems due to its increasing importance in biological function analysis. We adopt a novel method, based on one of the matrix completion (MC) techniques–the Riemannian approach, to solve the protein structure determination problem. We formulate the protein structure in terms of low-rank matrix which can be solved by an optimization problem in the Riemannian spectrahedron manifold whose objective function has been delimited with the derived boundary condition. Two efficient algorithms in Riemannian approach-the trust-region (Tr) algorithm and the conjugate gradient (Cg) algorithm are used to reconstruct protein structures. We first use the two algorithms in a toy model and show that the Tr algorithm is more robust. Afterwards, we rebuild the protein structure from the NOE distance information deposited in NMR Restraints Grid (http://restraintsgrid.bmrb.wisc.edu/NRG/MRGridServlet). A dataset with both X-ray crystallographic structure and NMR structure deposited in Protein Data Bank (PDB) is used to statistically evaluate the performance of our method. By comparing both our rebuilt structures and NMR counterparts with the “standard” X-ray structures, we conclude that our rebuilt structures have similar (sometimes even smaller) RMSDs relative to “standard” X-ray structures in contrast with the reference NMR structures. Besides, we also validate our method by comparing the Z-scores between our rebuilt structures with reference structures using Protein Structure Validation Software suit. All the validation scores indicate that the Riemannian approach in MC techniques is valid in reconstructing the protein structures from NOE distance information. The software based on Riemannian approach is freely available athttps://github.com/xubiaopeng/Protein_Recon_MCRiemman.Author summaryMatrix Completion is a technique widely used in many aspects, such as the global positioning in sensor networks, collaborative filtering in recommendation system for many companies and face recognition, etc. In biology, distance geometry used to be a popular method for reconstructing protein structures related to NMR experiment. However, due to the low quality of the reconstructed results, those methods were replaced by other dynamic methods such as ARIA, CYANA and UNIO. Recently, a new MC technique named Riemannian approach is introduced and proved mathematically, which promotes us to apply it in protein structure determination from NMR measurements. In this paper, by combining the Riemannian approach and some post-processing procedures together, we reconstruct the protein structures from the incomplete distance information measured by NMR. By evaluating our results and comparing with the corresponding PDB NMR deposits, we show that the current Riemannian approach method is valid and at least comparable with (if not better than) the state-of-art methods in NMR structure determination.

Integrative protein modeling in RosettaNMR from sparse paramagnetic restraints

Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals hold great potential to study larger proteins and complexes. Additionally, computational methods designed to work with sparse data add atomic detail that is missing in the experimental restraints, allowing application to systems that are difficult to investigate. While specific frameworks in the Rosetta macromolecular modeling suite support the use of certain NMR restraint types, use of all commonly measured restraint types together is precluded. Here, we introduce a comprehensive framework into Rosetta that reconciles CS-Rosetta, PCS-Rosetta and RosettaNMR into a single framework, that, in addition to backbone chemical shifts and nuclear Overhauser effect distance restraints, leverages NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements, measured at multiple tagging sites. We further showcase the generality of RosettaNMR for various modeling challenges and benchmark it on 28 structure prediction cases, eight symmetric assemblies, two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤ 2Å) in 50% of the cases.Significance StatementComputational methods such as Rosetta can assist NMR structure determination by employing efficient conformational search algorithms alongside physically realistic energy functions to model protein structure from sparse experimental data. We have developed a framework in Rosetta that leverages paramagnetic NMR data in addition to chemical shift and nuclear Overhauser effect restraints and extends RosettaNMR calculations to the prediction of symmetric assemblies, protein-protein and protein-ligand complexes. RosettaNMR generated high-accuracy models (≤ 2Å) in 50% of cases for a benchmark set of 28 monomeric and eight symmetric proteins and predicted protein-protein and protein-ligand interfaces with up to 1Å accuracy. The method expands Rosetta’s rich toolbox for integrative data-driven modeling and promises to be broadly useful in structural biology.

Integrated NMR and cryo-EM atomic-resolution structure determination of a half-megadalton enzyme complex

Atomic-resolution structure determination is the key requirement for understanding protein function. Cryo-EM and NMR spectroscopy both provide structural information, but currently cryo-EM does not routinely give access to atomic-level structural data, and, generally, NMR structure determination is restricted to small (<30 kDa) proteins. We introduce an integrated structure determination approach that simultaneously uses NMR and EM data to overcome the limits of each of these methods. The approach enabled determination of the high-resolution structure of the 468 kDa large dodecameric aminopeptidase TET2 to a precision and accuracy below 1 Angstrom by combining secondary-structure information obtained from near-complete magic-angle-spinning NMR assignments of the 39 kDa-large subunits, distance restraints from backbone amides and specifically labelled methyl groups, and a 4.1 Angstrom resolution EM map. The resulting structure exceeds current standards of NMR and EM structure determination in terms of molecular weight and precision. Importantly, the approach is successful even in cases where only medium-resolution (up to 8 Angstrom) cryo-EM data are available, thus paving avenues for the structure determination of challenging biological assemblies.

The design and NMR structure determination of yttrium-oligopeptide tags for recombinant proteins and antibodies

A strategy for the design of new yttrium(III) tags consisting of sequences of naturally occurring amino acids is described. These tags are 4–6 amino acids in length and consist of aspartic and glutamic acids. The use of natural amino acids would allow these oligopeptides to be incorporated into recombinant proteins at the DNA level, enabling the protein to be Y(III)-labelled after protein isolation. This allows a radionuclide or heavy atom to be associated with the protein without the necessity of further synthetic modification. Suitable peptides able to chelate Y(III) in stable complexes were designed based on quantum-chemical calculations. The stability of complexes formed by these peptides was tested by isothermal titration calorimetry, giving dissociation constants in the micromolar range. The likely structure of the most tightly bound complex was inferred from a combination of NMR experiments and quantum-chemical calculations. This structure will serve as the basis for future optimizations.