Relative Solvent Exposure of the Alpha-Helix and Beta-Sheet in Water Determines the Initial Stages of Urea and Guanidinium Chloride-Induced Denaturation of Alpha/Beta Proteins

Experimental and molecular dynamics simulation studies on the physical properties of three HBc-VLP derivatives as nanoparticle protein vaccine candidates

Self-assembling virus-like particles (VLPs) are promising platforms for vaccine development. However, the unpredictability of the physical properties, such as self-assembly capability, hydrophobicity, and overall stability in engineered protein particles fused with antigens, presents substantial challenges in their downstream processing. We envision that these challenges can be addressed by combining more precise computer-aided molecular dynamics (MD) simulations with experimental studies on the modified products, with more to-date forcefield descriptions and larger models closely resembling real assemblies, realized by rapid advancement in computing technology. In this study, three chimeric designs based on the hepatitis B core (HBc) protein as model vaccine candidates were constructed to study and compare the influence of inserted epitopes as well as insertion strategy on HBc modifications. Large partial VLP models containing 17 chains for the HBc chimeric model vaccines were constructed based on the wild-type (wt) HBc assembly template. The findings from our simulation analysis have demonstrated good consistency with experimental results, pertaining to the surface hydrophobicity and overall stability of the chimeric vaccine candidates. Furthermore, the different impact of foreign antigen insertions on the HBc scaffold was investigated through simulations. It was found that separately inserting two epitopes into the HBc platform at the N-terminal and the major immunogenic regions (MIR) yields better results compared to a serial insertion at MIR in terms of protein structural stability. This study substantiates that an MD-guided design approach can facilitate vaccine development and improve its manufacturing efficiency by predicting products with extreme surface hydrophobicity or structural instability.

Optimized Collective Variable for Collapse Transition in Linear Hydrophobic Polymers: Importance of Hydration Water and End-to-End Distance

Choosing an appropriate collective variable (CV) for any biomolecular process is a challenging task. Researchers are developing methods to solve this issue using a variety of methodologies, most recently using machine learning (ML) methods. In this work, we investigate the mechanism of collapse transition across various lengths of polymer systems through adaptively sampled multiple short trajectories utilizing the Time Lagged Independent Component Analysis (TICA) framework. From TICA analysis, it is revealed that the radius of gyration (Rg) and end-to-end distance serve as good order parameters (OPs) for these systems describing overall energy landscapes. Markov state model (MSM) and mean first passage time (MFPT) analysis suggest that hydration water (Nw) plays a determining role in dictating the time scale and barrier for the collapsed transition for the C40 system. P-fold analysis on identifying transition state ensembles (TSE) identified by committor analysis also strengthens the role of Nw in such a transition. TICA, MSM, and committor analyses on the collapse transition for C45 reveal similarities with C40 systems in different aspects. Furthermore, we propose a pipeline integrating XGBoost regression along with an interpretable ML model, Shapley Additive exPlanation (SHAP) to precisely elucidate the contribution of each OP locally at the TSE. Through this approach, we observe that the collapse transition is primarily driven by Nw for both polymer systems. A carefully designed protocol for the collapsed transition of C60 systems indirectly reiterates the above result. Overall, our results suggest that while the end-to-end distance should be considered for better resolution of metastable states in the landscape, Nw is the crucial coordinate to be used in enhanced sampling for the exploration of actual collapse transitions for linear hydrophobic polymer systems. The Python code for analyzing the contribution of different OPs in the TSE using an ML-aided protocol is available on GitHub (https://github.com/saikat-ai/linear_polymer_project).

Phosphoryl group wires stabilize pathological tau fibrils as revealed by multiple quantum spin counting NMR

Hyperphosphorylation of the protein tau is one of the biomarkers of neurodegenerative diseases in the category of tauopathies. However, the molecular level, mechanistic, role of this common post-translational modification (PTM) in enhancing or reducing the aggregation propensity of tau is unclear, especially considering that combinatorial phosphorylation of multiple sites can have complex, non-additive, effects on tau protein aggregation. Since tau proteins stack in register and parallel to elongate into pathological fibrils, phosphoryl groups from adjacent tau strands with 4.8 Å separation must find an energetically favorable spatial arrangement. At first glance, this appears to be an unfavorable configuration due to the proximity of negative charges between phosphate groups from adjacent neighboring tau fibrils. However, this study tests a counterhypothesis that phosphoryl groups within the fibril core-forming segments favorably assemble into highly ordered, hydrogen-bonded, one-dimensionally extended wires under biologically relevant conditions. We selected two phosphorylation sites associated with neurodegeneration, serine 305 (S305p) and tyrosine 310 (Y310p), on a model tau peptide jR2R3-P301L (tau295-313) spanning the R2/R3 splice junction of tau, that readily aggregate into a fibril with characteristics of a seed-competent mini prion. Using multiple quantum spin counting (MQ-SC) by 31P solid-state NMR of phosphorylated jR2R3-P301L tau peptide fibrils, enhanced by dynamic nuclear polarization, we find that at least six phosphorous spins must neatly arrange in 1D within fibrils or in 2D within a protofibril to yield the experimentally observed MQ-coherence orders of four. We found that S305p stabilizes the tau fibrils and leads to more seeding-competent fibrils compared to jR2R3 P301L or Y310p. This study introduces a new concept that phosphorylation of residues within a core forming tau segment can mechanically facilitate fibril registry and stability due a hitherto unrecognized role of phosphoryl groups to form highly ordered, extended, 1D wires that stabilize pathological tau fibrils.

Solvent effect on the detection of peptides and proteins by nanoelectrospray ionization mass spectrometry: Anomalous behavior of aqueous 2-propanol

To investigate the solvent effect on the detection of peptides and proteins, nanoelectrospray mass spectra were measured for mixtures of 1 % acetic acid and 5 × 10-6 M gramicidin S (G), ubiquitin (U), and cytochrome c (C) in water (W), methanol (MeOH), 1-propanol (1-PrOH), acetonitrile (AcN), and 2-propanol (2-PrOH). Although doubly protonated G (G2+) and multiply protonated U (Un+) and C (Cn+) were readily detected with a wide range of mixing ratios of W solutions for MeOH, 1-PrOH, and AcN, Cn+ was totally suppressed for the solutions with mixing ratios (v/v) of W/2-PrOH (50/50) and (70/30). However, denatured Cn+ started to be detected with W/2-PrOH (90/10) together with Gn+ (n = 1, 2) and native Un+ (n = 6-8). At the mixing ratio of W/2-PrOH (95/5), native Cn+ (n = 7-10) together with Gn+ (n = 1, 2) and native Un+ (n = 6-8) were detected with high ion intensities. The use of W/2-PrOH (95/5) is profitable because it enables the detection of native proteins with high detection sensitivities.

Computing Free Energies of Fold-Switching Proteins Using MELD x MD

Some proteins are conformational switches, able to transition between relatively different conformations. To understand what drives them requires computing the free-energy difference ΔGAB between their stable states, A and B. Molecular dynamics (MD) simulations alone are often slow because they require a reaction coordinate and must sample many transitions in between. Here, we show that modeling employing limited data (MELD) x MD on known endstates A and B is accurate and efficient because it does not require passing over barriers or knowing reaction coordinates. We validate this method on two problems: (1) it gives correct relative populations of α and β conformers for small designed chameleon sequences of protein G; and (2) it correctly predicts the conformations of the C-terminal domain (CTD) of RfaH. Free-energy methods like MELD x MD can often resolve structures that confuse machine-learning (ML) methods.

Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models

An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS.

Deciphering the Mechanistic Basis for Perfluoroalkyl-Protein Interactions

Although rarely used in nature, fluorine has emerged as an important elemental ingredient in the design of proteins with altered folding, stability, oligomerization propensities, and bioactivity. Adding to the molecular modification toolbox, here we report the ability of privileged perfluorinated amphiphiles to noncovalently decorate proteins to alter their conformational plasticity and potentiate their dispersion into fluorous phases. Employing a complementary suite of biophysical, in-silico and in-vitro approaches, we establish structure-activity relationships defining these phenomena and investigate their impact on protein structural dynamics and intracellular trafficking. Notably, we show that the lead compound, perfluorononanoic acid, is 106 times more potent in inducing non-native protein secondary structure in select proteins than is the well-known helix inducer trifluoroethanol, and also significantly enhances the cellular uptake of complexed proteins. These findings could advance the rational design of fluorinated proteins, inform on potential modes of toxicity for perfluoroalkyl substances, and guide the development of fluorine-modified biologics with desirable functional properties for drug discovery and delivery applications.

Effects of stirring speed ladder on the acid-promoted refolding of rice glutelin

The effects of stirring speed (0, 250, 500, 750, 1000, 1250, and 1500 rpm) on the rice glutelin hydrocolloids (1 %, w/v) during the acidified process were investigated. As the stirring speed was increased to 750 rpm, the hydration diameter of the rice glutelin was significantly decreased, but higher stirring speeds had no significant effect on size. The highest and lowest solubility were recorded for the samples treated at 750 and 0 rpm stirring speeds, respectively. The surface hydrophobicity and molecular weight increased first and then decreased, both the minimum value was recorded at 750 rpm sample. The principal component analysis (PCA) was employed to detect patterns between changes in various properties (solubility, particle size, β-sheet content, surface hydrophobicity, and ζ-potential) and stirring treatment. To conclude, the various properties of rice glutelin refold during acidification are drastically affected by employing different stirring speeds. Choosing a suitable stirring speed is important for quality control in protein hydrocolloid production.

Protein-Protein Interface Topology as a Predictor of Secondary Structure and Molecular Function Using Convolutional Deep Learning

To power the specific recognition and binding of protein partners into functional complexes, a wealth of information about the structure and function of the partners is necessarily encoded into the global shape of protein-protein interfaces and their local topological features. To identify whether this is the case, this study uses convolutional deep learning methods (typically leveraged for 2D image recognition) on 3D voxel representations of protein-protein interfaces colored by burial depth. A novel two-stage network fed with voxelizations of each interface at two distinct resolutions achieves balance between performance and computational cost. From the shape of the interfaces, the network tries to predict the presence of secondary structure motifs at the interface and the molecular function of the corresponding complex. Secondary structure and certain classes of function are found to be very well predicted, validating the hypothesis that interface shape is a conveyor of higher-level information. Interface patterns triggering the recognition of specific classes are also identified and described.

A computational method to simulate global conformational changes of proteins induced by cosolvent

A computational method to investigate the global conformational change of a protein is proposed by combining the linear response path following (LRPF) method and three-dimensional reference interaction site model (3D-RISM) theory, which is referred to as the LRPF/3D-RISM method. The proposed method makes it possible to efficiently simulate protein conformational changes caused by either solutions of varying concentrations or the presence of cosolvent species by taking advantage of the LRPF and 3D-RISM. The proposed method is applied to the urea-induced denaturation of ubiquitin. The LRPF/3D-RISM trajectories successfully simulate the early stage of the denaturation process within the simulation time of 300 ns, whereas no significant structural change is observed even in the 1 μs standard MD simulation. The obtained LRPF/3D-RISM trajectories reproduce the mechanism of the urea denaturation of ubiquitin reported in previous studies, and demonstrate the high efficiency of the method.