The PROSECCO server for chemical shift predictions in ordered and disordered proteins

Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering

By merging advanced dimensionality reduction (DR) and clustering algorithm (CA) techniques, our study advances the sampling procedure for predicting NMR chemical shifts (CS) in intrinsically disordered proteins (IDPs), making a significant leap forward in the field of protein analysis/modeling. We enhance NMR CS sampling by generating clustered ensembles that accurately reflect the different properties and phenomena encapsulated by the IDP trajectories. This investigation critically assessed different rapid CS predictors, both neural network (e.g., Sparta+ and ShiftX2) and database-driven (ProCS-15), and highlighted the need for more advanced quantum calculations and the subsequent need for more tractable-sized conformational ensembles. Although neural network CS predictors outperformed ProCS-15 for all atoms, all tools showed poor agreement with HN CSs, and the neural network CS predictors were unable to capture the influence of phosphorylated residues, highly relevant for IDPs. This study also addressed the limitations of using direct clustering with collective variables, such as the widespread implementation of the GROMOS algorithm. Clustered ensembles (CEs) produced by this algorithm showed poor performance with chemical shifts compared to sequential ensembles (SEs) of similar size. Instead, we implement a multiscale DR and CA approach and explore the challenges and limitations of applying these algorithms to obtain more robust and tractable CEs. The novel feature of this investigation is the use of solvent-accessible surface area (SASA) as one of the fingerprints for DR alongside previously investigated α carbon distance/angles or ϕ/ψ dihedral angles. The ensembles produced with SASA tSNE DR produced CEs better aligned with the experimental CS of between 0.17 and 0.36 r2 (0.18-0.26 ppm) depending on the system and replicate. Furthermore, this technique produced CEs with better agreement than traditional SEs in 85.7% of all ensemble sizes. This study investigates the quality of ensembles produced based on different input features, comparing latent spaces produced by linear vs nonlinear DR techniques and a novel integrated silhouette score scanning protocol for tSNE DR.

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models

Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing distinct NMR signals in different chemical environments. Apprehending chemical shifts from NMR signals can be challenging since having an NMR structure does not necessarily provide all the required chemical shift information, making predictive models essential for accurately deducing chemical shifts, either from protein structures or, more ideally, directly from amino acid sequences. Here, we present EFG-CS, a web server that specializes in chemical shift prediction. EFG-CS employs a machine learning-based transfer prediction model for backbone atom chemical shift prediction, using ESMFold-predicted protein structures. Additionally, ESG-CS incorporates a graph neural network-based model to provide comprehensive side-chain atom chemical shift predictions. Our method demonstrated reliable performance in backbone atom prediction, achieving comparable accuracy levels with root mean square errors (RMSE) of 0.30 ppm for H, 0.22 ppm for Hα, 0.89 ppm for C, 0.89 ppm for Cα, 0.84 ppm for Cβ, and 1.69 ppm for N. Moreover, our approach also showed predictive capabilities in side-chain atom chemical shift prediction achieving RMSE values of 0.71 ppm for Hβ, 0.74-1.15 ppm for Hδ, and 0.58-0.94 ppm for Hγ, solely utilizing amino acid sequences without homology or feature curation. This work shows for the first time that generative AI protein models can predict NMR shifts nearly comparable to experimental models. This web server is freely available at https://biosig.lab.uq.edu.au/efg_cs, and the chemical shift prediction results can be downloaded in tabular format and visualized in 3D format.

Automated Fragmentation Quantum Mechanical Calculation of 15N and 13C Chemical Shifts in a Membrane Protein

In this work, we developed an accurate and cost-effective automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) method to calculate the chemical shifts of 15N and 13C of membrane proteins. The convergence of the AF-QM/MM method was tested using Krokinobacter eikastus rhodopsin 2 as a test case. When the distance threshold of the QM region is equal to or larger than 4.0 Å, the results of the AF-QM/MM calculations are close to convergence. In addition, the effects of selected density functionals, basis sets, and local chemical environment of target atoms on the chemical shift calculations were systematically investigated. Our results demonstrate that the predicted chemical shifts are more accurate when important environmental factors including cross-protomer interactions, lipid molecules, and solvent molecules are taken into consideration, especially for the 15N chemical shift prediction. Furthermore, with the presence of sodium ions in the environment, the chemical shift of residues, retinal, and retinal Schiff base are affected, which is consistent with the results of the solid-state nuclear magnetic resonance (NMR) experiment. Upon comparing the performance of various density functionals (namely, B3LYP, B3PW91, M06-2X, M06-L, mPW1PW91, OB95, and OPBE), the results show that mPW1PW91 is a suitable functional for the 15N and 13C chemical shift prediction of the membrane proteins. Meanwhile, we find that the improved accuracy of the 13Cβ chemical shift calculations can be achieved by the employment of the triple-ζ basis set. However, the employment of the triple-ζ basis set does not improve the accuracy of the 15N and 13Cα chemical shift calculations nor does the addition of a diffuse function improve the overall prediction accuracy of the chemical shifts. Our study also underscores that the AF-QM/MM method has significant advantages in predicting the chemical shifts of key ligands and nonstandard residues in membrane proteins than most widely used empirical models; therefore, it could be an accurate computational tool for chemical shift calculations on various types of biological systems.

The influence of random-coil chemical shifts on the assessment of structural propensities in folded proteins and IDPs

In studying secondary structural propensities of proteins by nuclear magnetic resonance (NMR) spectroscopy, secondary chemical shifts (SCSs) serve as the primary atomic scale observables. For SCS calculation, the selection of an appropriate random coil chemical shift (RCCS) dataset is a crucial step, especially when investigating intrinsically disordered proteins (IDPs). The scientific literature is abundant in such datasets, however, the effect of choosing one over all the others in a concrete application has not yet been studied thoroughly and systematically. Hereby, we review the available RCCS prediction methods and to compare them, we conduct statistical inference by means of the nonparametric sum of ranking differences and comparison of ranks to random numbers (SRD-CRRN) method. We try to find the RCCS predictors best representing the general consensus regarding secondary structural propensities. The existence and the magnitude of resulting differences on secondary structure determination under varying sample conditions (temperature, pH) are demonstrated and discussed for globular proteins and especially IDPs.

Design, Optimization, and Structural Characterization of an Apoptosis-Inducing Factor Peptide Targeting Human Cyclophilin A to Inhibit Apoptosis Inducing Factor-Mediated Cell Death

Blocking the interaction between the apoptosis-inducing factor (AIF) and cyclophilin A (CypA) by the AIF fragment AIF(370-394) is protective against glutamate-induced neuronal cell death and brain injury in mice. Starting from AIF(370-394), we report the generation of the disulfide-bridged and shorter variant AIF(381-389) and its structural characterization by nuclear magnetic resonance (NMR) in the free and CypA-bound state. AIF(381-389) in both the free and bound states assumes a β-hairpin conformation similar to that of the fragment in the AIF protein and shows a highly reduced conformational flexibility. This peptide displays a similar in vitro affinity for CypA, an improved antiapoptotic activity in cells and an enhanced proteolytic stability compared to the parent peptide. The NMR-based 3D model of the AIF(381-389)/CypA complex provides a better understanding of the binding hot spots on both the peptide and the protein and can be exploited to design AIF/CypA inhibitors with improved pharmacokinetic and pharmacodynamics features.

Backbone NMR assignments of the C-terminal domain of the human prion protein and its disease-associated T183A variant

Transmissible spongiform encephalopathies (TSEs) are fatal neurodegenerative disorders associated with the misfolding and aggregation of the human prion protein (huPrP). Despite efforts into investigating the process of huPrP aggregation, the mechanisms triggering its misfolding remain elusive. A number of TSE-associated mutations of huPrP have been identified, but their role at the onset and progression of prion diseases is unclear. Here we report the NMR assignments of the C-terminal globular domain of the wild type huPrP and the pathological mutant T183A. The differences in chemical shifts between the two variants reveal conformational alterations in some structural elements of the mutant, whereas the analyses of secondary shifts and random coil index provide indications on the putative mechanisms of misfolding of T183A huPrP.

Mechanism of misfolding of the human prion protein revealed by a pathological mutation

The misfolding and aggregation of the human prion protein (PrP) is associated with transmissible spongiform encephalopathies (TSEs). Intermediate conformations forming during the conversion of the cellular form of PrP into its pathological scrapie conformation are key drivers of the misfolding process. Here, we analyzed the properties of the C-terminal domain of the human PrP (huPrP) and its T183A variant, which is associated with familial forms of TSEs. We show that the mutation significantly enhances the aggregation propensity of huPrP, such as to uniquely induce amyloid formation under physiological conditions by the sole C-terminal domain of the protein. Using NMR spectroscopy, biophysics, and metadynamics simulations, we identified the structural characteristics of the misfolded intermediate promoting the aggregation of T183A huPrP and the nature of the interactions that prevent this species to be populated in the wild-type protein. In support of these conclusions, POM antibodies targeting the regions that promote PrP misfolding were shown to potently suppress the aggregation of this amyloidogenic mutant.

The dynamics of free and phosphopeptide-bound Grb2-SH2 reveals two dynamically independent subdomains and an encounter complex with fuzzy interactions

The growth factor receptor-bound protein 2 (Grb2) is a key factor in the regulation of cell survival, proliferation, differentiation, and metabolism. In its structure, the central Src homology 2 (SH2) domain is flanked by two Src homology 3 (SH3). SH2 is the most important domain in the recognition of phosphotyrosines. Here, we present the first dynamical characterization of Grb2-SH2 domain in the free state and in the presence of phosphopeptide EpYINSQV at multiple timescales, which revealed valuable information to the understanding of phophotyrosine sensing mechanism. Grb2-SH2 presented two dynamically independent subdomains, subdomain I involved in pY recognition and subdomain II is the pY + 2 specificity pocket. Under semi-saturated concentrations of pY-pep we observed fuzzy interactions, which led to chemical exchange observed by NMR. This information was used to describe the encounter complex. The association with pY-pep is dynamic, involving fuzzy interactions and multiple conformations of pY-pep with negative and hydrophobic residues, creating an electrostatic-potential that drives the binding of pY-pep. The recognition face is wider than the binding site, with many residues beyond the central SH2 binding site participating in the association complex, which contribute to explain previously reported capability of Grb2 to recognize remote pY.

In vitro 0N4R tau fibrils contain a monomorphic β-sheet core enclosed by dynamically heterogeneous fuzzy coat segments

Misfolding of the microtubule-binding protein tau into filamentous aggregates is characteristic of many neurodegenerative diseases such as Alzheimer's disease and progressive supranuclear palsy. Determining the structures and dynamics of these tau fibrils is important for designing inhibitors against tau aggregation. Tau fibrils obtained from patient brains have been found by cryo-electron microscopy to adopt disease-specific molecular conformations. However, in vitro heparin-fibrillized 2N4R tau, which contains all four microtubule-binding repeats (4R), was recently found to adopt polymorphic structures. Here we use solid-state NMR spectroscopy to investigate the global fold and dynamics of heparin-fibrillized 0N4R tau. A single set of ¹³C and ¹⁵N chemical shifts was observed for residues in the four repeats, indicating a single β-sheet conformation for the fibril core. This rigid core spans the R2 and R3 repeats and adopts a hairpin-like fold that has similarities to but also clear differences from any of the polymorphic 2N4R folds. Obtaining a homogeneous fibril sample required careful purification of the protein and removal of any proteolytic fragments. A variety of experiments and polarization transfer from water and mobile side chains indicate that 0N4R tau fibrils exhibit heterogeneous dynamics: Outside the rigid R2-R3 core, the R1 and R4 repeats are semirigid even though they exhibit β-strand character and the proline-rich domains undergo large-amplitude anisotropic motions, whereas the two termini are nearly isotropically flexible. These results have significant implications for the structure and dynamics of 4R tau fibrils in vivo.

Chemical Shifts of the Carbohydrate Binding Domain of Galectin-3 from Magic Angle Spinning NMR and Hybrid Quantum Mechanics/Molecular Mechanics Calculations

Magic angle spinning NMR spectroscopy is uniquely suited to probe the structure and dynamics of insoluble proteins and protein assemblies at atomic resolution, with NMR chemical shifts containing rich information about biomolecular structure. Access to this information, however, is problematic, since accurate quantum mechanical calculation of chemical shifts in proteins remains challenging, particularly for ¹⁵N^H. Here we report on isotropic chemical shift predictions for the carbohydrate recognition domain of microcrystalline galectin-3, obtained from using hybrid quantum mechanics/molecular mechanics (QM/MM) calculations, implemented using an automated fragmentation approach, and using very high resolution (0.86 Å lactose-bound and 1.25 Å apo form) X-ray crystal structures. The resolution of the X-ray crystal structure used as an input into the AF-NMR program did not affect the accuracy of the chemical shift calculations to any significant extent. Excellent agreement between experimental and computed shifts is obtained for ¹³C^α, while larger scatter is observed for ¹⁵N^H chemical shifts, which are influenced to a greater extent by electrostatic interactions, hydrogen bonding, and solvation.