Folding Stability Research Articles

Improved Estimates of Folding Stabilities and Kinetics with Multiensemble Markov Models.

Markov State Models (MSMs) have been widely applied to understand protein folding mechanisms by predicting long time scale dynamics from ensembles of short molecular simulations. Most MSM estimators enforce detailed balance, assuming that trajectory data are sampled at an equilibrium. This is rarely the case for ab initio folding studies, however, and as a result, MSMs can severely underestimate protein folding stabilities from such data. To remedy this problem, we have developed an enhanced-sampling protocol in which (1) unbiased folding simulations are performed and sparse tICA is used to obtain features that best capture the slowest events in folding, (2) umbrella sampling along this reaction coordinate is performed to observe folding and unfolding transitions, and (3) the thermodynamics and kinetics of folding are estimated using multiensemble Markov models (MEMMs). Using this protocol, folding pathways, rates, and stabilities of a designed α-helical hairpin, Z34C, can be predicted in good agreement with experimental measurements. These results indicate that accurate simulation-based estimates of absolute folding stabilities are within reach, with implications for the computational design of folded miniproteins and peptidomimetics.

Development of a Novel Label-Free Subunit HILIC-MS Method for Domain-Specific Free Thiol Identification and Quantitation in Therapeutic Monoclonal Antibodies.

Cysteine residues are crucial for the formation of conserved disulfide bonds in therapeutic monoclonal antibodies (mAbs), which are essential for their folding and structural stability. The presence of free thiols in mAbs can indicate incomplete disulfide bond formation, potentially impacting the molecule's conformational stability. Free thiol quantitation has been achieved using labeling-based strategies such as maleimide and haloalkyl derivatives at both intact and peptide levels. However, intact-level measurement only provides total free thiol levels, while peptide-level measurement is time-consuming and more prone to assay-induced artifacts. In this study, we present a novel label-free HILIC-MS method that separates free thiol species at the subunit level, followed by free thiol localization by the MS2 fragmentation pattern. This allows for facile identification and quantitation of intrachain free thiols at domain-specific resolution. Compared to bottom-up approaches, this subunit HILIC-MS method excels in simpler sample preparation and higher throughput and enables chain-specific free thiol analysis for bispecific mAbs. This method can be readily applied for screening mAb candidates with elevated levels of free thiols in early-stage developability assessment and facilitating an effective comparability evaluation of mAb samples during process development.

Leveraging neural networks to correct FoldX free energy estimates.

Proteins play a pivotal role in many biological processes, and changes in their amino acid sequences can lead to dysfunction and disease. These changes can affect protein folding or interaction with other biomolecules, such as preventing antibodies from inhibiting a viral infection or causing proteins to misfold. The ability to predict the effects of mutations in proteins is crucial. Although experimental techniques can accurately quantify the effect of mutations on protein folding free energies and protein-protein binding free energies, they are often time-consuming and costly. By contrast, computational techniques offer fast and cost-effective alternatives for estimating free energies, but they typically suffer from lower accuracy. Enhancing the accuracy of computational predictions is therefore of high importance, with the potential to greatly impact fields ranging from drug design to understanding disease mechanisms. One such widely used computational method, FoldX, is capable of rapidly predicting the relative folding stability ( ) for a protein as well as the relative binding affinity ( ) between proteins using a single protein structure as input. However, it can suffer from low accuracy, especially for antibody-antigen systems. In this work, we trained a neural network on FoldX output to enhance its prediction accuracy. We first performed FoldX calculations on the largest datasets available for mutations that affect binding (SKEMPIv2) and folding (ProTherm4) with experimentally measured . Features were then extracted from the FoldX output files including its prediction for . We then developed and optimized a neural network framework to predict the difference between FoldX's estimated and the experimental data, creating a model capable of producing a correction factor. Our approach showed significant improvements in Pearson correlation performance. For single mutations affecting folding, the correlation improved from a baseline of 0.3 to 0.66. In terms of binding, performance increased from 0.37 to 0.61 for single mutations and from 0.52 to 0.81 for double mutations. For epistasis, the correlation for binding affinity (both singles and doubles) improved from 0.19 to 0.59. Our results also indicated that models trained on double mutations enhanced accuracy when predicting higher-order mutations (such as triple or quadruple mutations), whereas models trained on singles did not. This suggests that interaction energy and epistasis effects present in the FoldX output are not fully utilized by FoldX itself. Once trained, these models add minimal computational time but provide a substantial increase in performance, especially for higher-order mutations and epistasis. This makes them a valuable addition to any free energy prediction pipeline using FoldX. Furthermore, we believe this technique can be further optimized and tested for predicting antibody escape, aiding in the efficient development of watch lists.

Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1.

De novo mutations in the synaptic GTPase activating protein (SynGAP) are associated with neurological disorders like intellectual disability, epilepsy, and autism. SynGAP is also implicated in Alzheimer's disease and cancer. Although pathogenic variants are highly penetrant in neurodevelopmental conditions, a substantial number of them are caused by missense mutations that are difficult to diagnose. Hence, in silico mutagenesis was performed for probing the missense effects within the N-terminal region of SynGAP structure. Through extensive molecular dynamics simulations, encompassing three 150-ns replicates for 211 variants, the impact of missense mutations on the protein fold was assessed. The effect of the mutations on the folding stability was also quantitatively assessed using free energy calculations. The mutations were categorized as potentially pathogenic or benign based on their structural impacts. Finally, the study introduces wild-type-SynGAP in complex with RasGTPase at the inner membrane, while considering the potential effects of mutations on these key interactions. This study provides structural perspective to the clinical assessment of SynGAP missense variants and lays the foundation for future structure-based drug discovery.

Analysis of Brain Protein Stability Changes in a Mouse Model of Alzheimer's Disease.

The stability of proteins from rates of oxidation (SPROX), thermal proteome profiling (TPP), and limited proteolysis (LiP) techniques were used to profile the stability of ∼2500 proteins in hippocampus tissue cell lysates from 2- and 8-months-old wild-type (C57BL/6J; n = 7) and transgenic (5XFAD; n = 7) mice with five Alzheimer's disease (AD)-linked mutations. Approximately 200-500 protein hits with AD-related stability changes were detected by each technique at each age point. The hit overlap from technique to technique was low, and all of the techniques generated protein hits that were more numerous and largely different from those identified in protein expression level analyses, which were also performed here. The hit proteins identified by each technique were enriched in a number of the same pathways and biological processes, many with known connections to AD. The protein stability hits included 25 high-value conformation biomarkers with AD-related stability changes detected using at least 2 techniques at both age points. Also discovered were subunit- and age-specific AD-related stability changes in the proteasome, which had reduced function at both age points. The different folding stability profiles of the proteasome at the two age points are consistent with a different mechanism for proteasome dysfunction at the early and late stages of AD.

Guiding Diffusion Models for Antibody Sequence and Structure Co-design with Developability Properties

Recent advances in deep generative methods have allowed antibody sequence and structure co-design. This study addresses the challenge of tailoring the highly variable complementarity-determining regions (CDRs) in antibodies to fulfill developability requirements. We introduce a guidance approach that integrates property information into the antibody design process using diffusion probabilistic models. This approach allows us to simultaneously design CDRs conditioned on antigen structures while considering critical properties like solubility and folding stability. Our property-guided diffusion model offers versatility by accommodating diverse property constraints, presenting a promising avenue for computational antibody design in therapeutic applications. Published by the American Physical Society 2024

ThermoLink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction.

Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we initially collected disulfide bond and protein thermostability data from extensive literature sources. Thereafter, we extracted various sequence- and structure-based features and constructed machine-learning models to predict whether disulfide bonds can improve protein thermostability. Among all models, the neighborhood context model based on the Adaboost-DT algorithm performed the best, yielding "area under the receiver operating characteristic curve" and accuracy scores of 0.773 and 0.714, respectively. Furthermore, we also found AlphaFold2 to exhibit high superiority in predicting disulfide bonds, and to some extent, the coevolutionary relationship between residue pairs potentially guided artificial disulfide bond design. Moreover, several mutants of imine reductase 89 (IR89) with artificially designed thermostable disulfide bonds were experimentally proven to be considerably efficient for substrate catalysis. The SS-bond data have been integrated into an online server, namely, ThermoLink, available at guolab.mpu.edu.mo/thermoLink.

Mitochondrial chaperon TNF-receptor- associated protein 1 as a novel apoptotic regulator conferring susceptibility to Pneumocystis jirovecii pneumonia.

Molecular chaperons stabilize protein folding and play a vital role in maintaining tissue homeostasis. To this intent, mitochondrial molecular chaperons may be involved in the regulation of oxidative phosphorylation and apoptosis during stress events such as infections. However, specific human infectious diseases relatable to defects in molecular chaperons have yet to be identified. To this end, we performed whole exome sequencing and functional immune assessment in a previously healthy Asian female, who experienced severe respiratory failure due to Pneumocystis jiroveci pneumonia and non-HIV-related CD4 lymphocytopenia. This revealed that a chaperon, the mitochondrial paralog of HSP90, TRAP1, may have been involved in the patient's susceptibility to an opportunistic infection. Two rare heterozygous variants in TRAP1, E93Q, and A64T were detected. The patient's peripheral blood mononuclear cells displayed diminished TRAP1 expression, but had increased active, cleaved caspase-3, caspase-7, and elevated IL-1β production. Transfection of A64T and E93Q variants in cell lines yielded decreased TRAP1 compared to transfected wildtype TRAP1 and re-capitulated the immunotypic phenotype of enhanced caspase-3 and caspase-7 activity. When infected with live P. jiroveci, the E93Q or A64T TRAP1 mutant expressing cells also exhibited reduced viability. Patient cells and cell lines transfected with the TRAP1 E93Q/A64T mutants had impaired respiration, glycolysis, and increased ROS production. Of note, co-expression of E93Q/A64T double mutants caused more functional aberration than either mutant singly. Taken together, our study uncovered a previously unrecognized role of TRAP1 in CD4+ lymphocytopenia, conferring susceptibility to opportunistic infections.

Yellow and oxidation-resistant derivatives of a monomeric superfolder GFP.

Fluorescent proteins (FPs) are essential tools in biology. The utility of FPs depends on their brightness, photostability, efficient folding, monomeric state, and compatibility with different cellular environments. Despite the proliferation of available FPs, derivatives of the originally identified Aequorea victoria green fluorescent protein often show superior behavior as fusion tags. We recently generated msGFP2, an optimized monomeric superfolder variant of A. victoria GFP. Here, we describe two derivatives of msGFP2. The monomeric variant msYFP2 is a yellow superfolder FP with high photostability. The monomeric variant moxGFP2 lacks cysteines but retains significant folding stability, so it works well in the lumen of the secretory pathway. These new FPs are useful for common imaging applications.

Stable and reusable calcium-responsive biopolymer for affinity precipitation of therapeutic antibodies.

Affinity precipitation is an attractive method for protein purification due to its many advantages, including the rapid capture of target proteins, simple processing, high specificity, and ease of scale-up. We previously reported a robust antibody purification method using Ca2+-dependent precipitation of ZZ-hCSQ2, a fusion protein of human calsequestrin 2, and the antibody-binding protein ZZ. However, the stability of this fusion protein was not sufficiently high for industrial use because the antibody recovery yield decreased to 60% after being reused 10 times. To identify a more stable calsequestrin (CSQ), we calculated Rosetta energy values for the folding stabilities of various CSQ homologs and selected human CSQ1 (hCSQ1) with lowest energy value (-992.6) as the new CSQ platform. We also identified that the linker sequence between ZZ and CSQ was vulnerable to proteases and alkaline pH by N-terminal protein sequencing. Therefore, we changed the linker to four asparagine (4N) sequences, which were shorter and less flexible than the previous glycine-rich linker. The new version of ZZ-CSQ, ZZ-4N-hCSQ1, was stable in a protease-containing conditioned medium obtained from the cultured Chinese hamster ovary cell or high pH condition (0.1M sodium hydroxide) for more than 5 days and could be reused at least 25 times for antibody purification without loss of recovery yield. The antibodies purified by ZZ-4N-hCSQ1 precipitation also showed greater purity (~33.6-fold lower host cell DNA and ~6.4-fold lower host cell protein) than those purified by protein A chromatography. These data suggest that ZZ-4N-hCSQ1 precipitation is more efficient and can achieve cost-effectiveness of up to 12.5-fold cheaper than previous antibody purification methods and can lower the production costs of therapeutic antibodies.

Electrochemical aptasensor based on RGO/PPy/Au*NPs for diagnostic biosensing of SARS-CoV-2 in clinical samples: Experimental and molecular dynamics simulations approaches

A highly sensitive and selective E-DNA biosensor was fabricated on a gold substrate using a reduced graphene oxide/polypyrrole/gold nanoparticle/oligonucleotide (RGO/PPy/Au*NPs/Apt) nanocomposite electrode to detect the nucleocapsid protein of SARS-CoV-2 in the patient blood plasma. The modified electrode was characterized by physicochemical techniques such as Fourier transform-infra red (FT-IR), Raman spectroscopy, X-ray diffraction (XRD), energy-dispersive X-ray (EDX) spectroscopy, X-ray photoelectron spectroscopy (XPS) analyses, Brunauer-Emmett-Teller (BET), and Barrett-Joyner-Halenda (BJH) analyses. Moreover, electrochemical analyses were employed to study the electrochemical performance of the electrodes including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and square wave voltammetry (SWV). Computational investigations of N protein bindings such as hydrogen bonding, Van der Waals binding, and Gibbs binding energies of the N-protein, and aptamer were studied by Molecular Dynamics Simulations (MDS). The unique synergistic effect of RGO, PPy, and the well-known effect of Au nanoparticles makes the DNA probe immobilized on the gold electrode surface. After optimizing the systems, the E-DNA biosensor exhibited a fast SWV response, higher sensitivity (33.77 μA.nM−1.cm−2) and selectivity, high efficiency, good storage stability, and acceptable repeatability for monitoring DNA. The results of real samples based on SWV indicated the correct functioning of the aptasensor in the presence of the SARS-CoV-2 virus. The limit of detection was 3.16×10−17 M and the limit of quantitation was 1.42×10−16 M. The MDS results indicated the stable dynamic folding of the aptamer for beneficial binding. The results indicated that the RGO/PPy/Au*NPs/Apt biosensor is promising for detecting of SARS-CoV-2 virus.

Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset.

Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss our model's limitations compared to other state-of-the-art methods in generalizing to larger protein scaffolds. Our results highlight the need for large-scale stability measurements on a diverse dataset that mirrors the distribution of sequence lengths commonly observed in nature.

Experimental Investigation Of The Flow-Acoustic Interaction In Human Phonation

In the human phonation process, acoustic standing waves in the vocal tract can influence the fluid flow through the glottis. To investigate the amount of two-way coupling between the flow field and the acoustics, the supraglottal flow field has been recorded via high-speed particle image velocimetry (PIV) in a synthetic larynx model for several configurations with different vocal tract lengths. Based on the obtained velocity fields, aeroacoustic source terms were computed. Furthermore, the hydrodynamic pressure in the vocal tract was recorded via wall pressure measurements. The recordings revealed that near a vocal tract resonance frequency, the focal fold oscillation frequency jumps onto the resonance. This is accompanied by a substantial relative increase in aeroacoustic sound generation efficiency. At the same time, the glottal volume flow needed for stable vocal fold oscillation decreases strongly. The pressure measurements showed that acoustic anti-resonances of the vocal tract directly dampen the harmonic content of the hydrodynamic pressure field when matching frequencies. This result shows that back-coupling of the acoustics onto the flow field does not only occur in the case where the vocal fold oscillation frequency and a resonance frequency of the vocal tract match, but also under more other conditions.

Structural and biochemical analyses of the nuclear IκBζ protein in complex with the NF-κB p50 homodimer.

As part of the efforts to understand nuclear IκB function in NF-κB-dependent gene expression, we report an X-ray crystal structure of the IκBζ ankyrin repeat domain in complex with the dimerization domain of the NF-κB p50 homodimer. IκBζ possesses an N-terminal α helix that conveys domain folding stability. Affinity and specificity of the complex depend on a small portion of p50 at the nuclear localization signal. The model suggests that only one p50 subunit supports binding with IκBζ, and biochemical experiments confirm that IκBζ associates with DNA-bound NF-κB p50:RelA heterodimers. Comparisons of IκBζ:p50 and p50:κB DNA complex crystallographic models indicate that structural rearrangement is necessary for ternary complex formation of IκBζ and p50 with DNA.

Phosphorylation by Protein Kinase C Weakens DNA-Binding Affinity and Folding Stability of the HMGB1 Protein.

The HMGB1 protein typically serves as a DNA chaperone that assists DNA-repair enzymes and transcription factors but can translocate from the nucleus to the cytoplasm or even to extracellular space upon some cellular stimuli. One of the factors that triggers the translocation of HMGB1 is its phosphorylation near a nuclear localization sequence by protein kinase C (PKC), although the exact modification sites on HMGB1 remain ambiguous. In this study, using spectroscopic methods, we investigated the HMGB1 phosphorylation and its impact on the molecular properties of the HMGB1 protein. Our nuclear magnetic resonance (NMR) data on the full-length HMGB1 protein showed that PKC specifically phosphorylates the A-box domain, one of the DNA binding domains of HMGB1. Phosphorylation of S46 and S53 was particularly efficient. Over a longer reaction time, PKC phosphorylated some additional residues within the HMGB1 A-box domain. Our fluorescence-based binding assays showed that the phosphorylation significantly reduces the binding affinity of HMGB1 for DNA. Based on the crystal structures of HMGB1-DNA complexes, this effect can be ascribed to electrostatic repulsion between the negatively charged phosphate groups at the S46 side chain and DNA backbone. Our data also showed that the phosphorylation destabilizes the folding of the A-box domain. Thus, phosphorylation by PKC weakens the DNA-binding affinity and folding stability of HMGB1.

Investigating Functional and Folding Stability of an Engineered E. coli L-asparaginase Harboring Y176F/S241C Mutations.

L-asparaginase has been widely recognized as a critical component in the treatment of various types of lymphoproliferative disorders, since its introduction in 1960s. However, its use in some cases leads to allergic reactions rendering the continuation of treatment unfeasible. Thus, the development of L-asparaginase from alternative sources or the production of engineered enzymes have always been considered. This study aimed to produce and evaluate a novel enzyme designed based on the sequence of L-asparaginase from Escherichia coli bacteria with Y176F/S241C mutations. The Y176F/S241C mutant L-asparaginase was successfully expressed as the GST-fusion protein in E. coli, and then was subjected to affinity and size exclusion chromatography. The activity of the purified enzyme was determined based on the released ammonia as the result of substrate hydrolysis using Nessler's reagent. Chemical denaturation experiment in the presence of increasing concentration of guanidinium chloride was applied to determine the folding stability of the purified enzyme. The mutant enzyme was purified with an efficiency of 77-fold but at a low recovery of 0.7%. The determined kinetic parameters Km, Vmax, kcat, specific activity and catalytic efficiency were 13.96 (mM), 2.218 (mM/min), 273.9 (min-1), 237.8 (IU/mg) and 19.62 (mM-1 min-1), respectively. Moreover, unfolding free energy determined by guanidinium chloride induced denaturation for mutated and commercial L-asparaginase enzymes were 8421 J/mol and 5274 J/mol, respectively. The mutant enzyme showed improved stability over the wild-type. Although the expression level and recovery were low, the mutant L-asparaginase demonstrated promising activity and stability, with potential clinical and industrial applications.

The Temperature Dependence of Hydrogen Bonds Is More Uniform in Stable Proteins: An Analysis of NMR h3JNC' Couplings in Four Different Protein Structures.

Long-range HNCO NMR spectra for proteins show crosspeaks due to 1JNC', 2JNC', 3JNCγ, and h3JNC' couplings. The h3JNC' couplings are transmitted through hydrogen bonds and their sizes are correlated to hydrogen bond lengths. We collected long-range HNCO data at a series of temperatures for four protein structures. P22i and CUS-3i are six-stranded beta-barrel I-domains from phages P22 and CUS-3 that share less than 40% sequence identity. The cis and trans states of the C-terminal domain from pore-forming toxin hemolysin ΙΙ (HlyIIC) arise from the isomerization of a single G404-P405 peptide bond. For P22i and CUS-3i, hydrogen bonds detected by NMR agree with those observed in the corresponding domains from cryoEM structures of the two phages. Hydrogen bond lengths derived from the h3JNC' couplings, however, are poorly conserved between the distantly related CUS-3i and P22i domains and show differences even between the closely related cis and trans state structures of HlyIIC. This is consistent with hydrogen bond lengths being determined by local differences in structure rather than the overall folding topology. With increasing temperature, hydrogen bonds typically show an apparent increase in length that has been attributed to protein thermal expansion. Some hydrogen bonds are invariant with temperature, however, while others show apparent decreases in length, suggesting they become stabilized with increasing temperature. Considering the data for the three proteins in this study and previously published data for ubiquitin and GB3, lowered protein folding stability and cooperativity corresponds with a larger range of temperature responses for hydrogen bonds. This suggests a partial uncoupling of hydrogen bond energetics from global unfolding cooperativity as protein stability decreases.

Role of N343 glycosylation on the SARS-CoV-2 S RBD structure and co-receptor binding across variants of concern.

Glycosylation of the SARS-CoV-2 spike (S) protein represents a key target for viral evolution because it affects both viral evasion and fitness. Successful variations in the glycan shield are difficult to achieve though, as protein glycosylation is also critical to folding and structural stability. Within this framework, the identification of glycosylation sites that are structurally dispensable can provide insight into the evolutionary mechanisms of the shield and inform immune surveillance. In this work, we show through over 45 μs of cumulative sampling from conventional and enhanced molecular dynamics (MD) simulations, how the structure of the immunodominant S receptor binding domain (RBD) is regulated by N-glycosylation at N343 and how this glycan's structural role changes from WHu-1, alpha (B.1.1.7), and beta (B.1.351), to the delta (B.1.617.2), and omicron (BA.1 and BA.2.86) variants. More specifically, we find that the amphipathic nature of the N-glycan is instrumental to preserve the structural integrity of the RBD hydrophobic core and that loss of glycosylation at N343 triggers a specific and consistent conformational change. We show how this change allosterically regulates the conformation of the receptor binding motif (RBM) in the WHu-1, alpha, and beta RBDs, but not in the delta and omicron variants, due to mutations that reinforce the RBD architecture. In support of these findings, we show that the binding of the RBD to monosialylated ganglioside co-receptors is highly dependent on N343 glycosylation in the WHu-1, but not in the delta RBD, and that affinity changes significantly across VoCs. Ultimately, the molecular and functional insight we provide in this work reinforces our understanding of the role of glycosylation in protein structure and function and it also allows us to identify the structural constraints within which the glycosylation site at N343 can become a hotspot for mutations in the SARS-CoV-2 S glycan shield.

Role of N343 glycosylation on the SARS-CoV-2 S RBD structure and co-receptor binding across variants of concern

Glycosylation of the SARS-CoV-2 spike (S) protein represents a key target for viral evolution because it affects both viral evasion and fitness. Successful variations in the glycan shield are difficult to achieve though, as protein glycosylation is also critical to folding and structural stability. Within this framework, the identification of glycosylation sites that are structurally dispensable can provide insight into the evolutionary mechanisms of the shield and inform immune surveillance. In this work, we show through over 45 μs of cumulative sampling from conventional and enhanced molecular dynamics (MD) simulations, how the structure of the immunodominant S receptor binding domain (RBD) is regulated by N-glycosylation at N343 and how this glycan’s structural role changes from WHu-1, alpha (B.1.1.7), and beta (B.1.351), to the delta (B.1.617.2), and omicron (BA.1 and BA.2.86) variants. More specifically, we find that the amphipathic nature of the N-glycan is instrumental to preserve the structural integrity of the RBD hydrophobic core and that loss of glycosylation at N343 triggers a specific and consistent conformational change. We show how this change allosterically regulates the conformation of the receptor binding motif (RBM) in the WHu-1, alpha, and beta RBDs, but not in the delta and omicron variants, due to mutations that reinforce the RBD architecture. In support of these findings, we show that the binding of the RBD to monosialylated ganglioside co-receptors is highly dependent on N343 glycosylation in the WHu-1, but not in the delta RBD, and that affinity changes significantly across VoCs. Ultimately, the molecular and functional insight we provide in this work reinforces our understanding of the role of glycosylation in protein structure and function and it also allows us to identify the structural constraints within which the glycosylation site at N343 can become a hotspot for mutations in the SARS-CoV-2 S glycan shield.

Targeting the protein folding transition state by mutation: Large scale (un)folding rate accelerations without altering native stability.

Proteins are constantly undergoing folding and unfolding transitions, with rates that determine their homeostasis in vivo and modulate their biological function. The ability to optimize these rates without affecting overall native stability is hence highly desirable for protein engineering and design. The great challenge is, however, that mutations generally affect folding and unfolding rates with inversely complementary fractions of the net free energy change they inflict on the native state. Here we address this challenge by targeting the folding transition state (FTS) of chymotrypsin inhibitor 2 (CI2), a very slow and stable two-state folding protein with an FTS known to be refractory to change by mutation. We first discovered that the CI2's FTS is energetically taxed by the desolvation of several, highly conserved, charges that form a buried salt bridge network in the native structure. Based on these findings, we designed a CI2 variant that bears just four mutations and aims to selectively stabilize the FTS. This variant has >250-fold faster rates in both directions and hence identical native stability, demonstrating the success of our FTS-centric design strategy. With an optimized FTS, CI2 also becomes 250-fold more sensitive to proteolytic degradation by its natural substrate chymotrypsin, and completely loses its activity as inhibitor. These results indicate that CI2 has been selected through evolution to have a very unstable FTS in order to attain the kinetic stability needed to effectively function as protease inhibitor. Moreover, the CI2 case showcases that protein (un)folding rates can critically pivot around a few key residues-interactions, which can strongly modify the general effects of known structural factors such as domain size and fold topology. From a practical standpoint, our results suggest that future efforts should perhaps focus on identifying such critical residues-interactions in proteins as best strategy to significantly improve our ability to predict and engineer protein (un)folding rates.