Enhancement of beta-sheet assembly by cooperative hydrogen bonds potential

Unraveling the Mechanical Property Decrease of Electrospun Spider Silk: A Molecular Dynamics Simulation Study

This study investigated the impact of electric fields on Nephila clavipes spider silk using molecular dynamics modeling. Electric fields with varying amplitudes and directions were observed to disrupt the β sheet structure of spider silk and reduce its mechanical properties. However, a notable exception was observed when a 0.1 V/nm electric field was applied in the antiparallel direction, resulting in improvements in Young's modulus and ultimate tensile strength. The antiparallel direction was observed to be particularly sensitive to electric fields, causing disruptions in beta sheets and hydrogen bonds, which significantly influence the mechanical properties. This study demonstrates that spider silk maintains its structural integrity at 0.1 V/nm. Possibly, lowering the power levels of typical electrospinning machines can prevent secondary structural disruption. These findings provide valuable insights for enhancing silk fiber production and applications using natural silk proteins while shedding light on the impact of electric fields on other silk proteins. Finally, this study opens up possibilities for optimizing electrospinning processes to enhance performance in various silk electrospinning applications.

Improving the gel properties of salted egg white/cooked soybean protein isolate composite gels by ultrasound treatment: Study on the gelling properties and structure

In this study, the effects of ultrasound treatment on the texture, physicochemical properties and protein structure of composite gels prepared by salted egg white (SEW) and cooked soybean protein isolate (CSPI) at different ratios were investigated. With the increased SEW addition, the ζ-potential absolute values, soluble protein content, surface hydrophobicity and swelling ratio of composite gels showed overall declining trends (P < 0.05), while the free sulfhydryl (SH) contents and hardness of exhibited overall increasing trends (P < 0.05). Microstructural results revealed that composite gels exhibited denser structure with the increased SEW addition. After ultrasound treatment, the particle size of composite protein solutions significantly decreased (P < 0.05), and the free SH contents of ultrasound-treated composite gels were lower than that of untreated composite gels. Moreover, ultrasound treatment enhanced the hardness of composite gels, and promoted the conversion of free water into non-flowable water. However, when ultrasonic power exceeded 150 W, the hardness of composite gels could not be further enhanced. FTIR results indicated that ultrasound treatment facilitated the composite protein aggregates to form a more stable gel structure. The improvement of ultrasound treatment on the properties of composite gels was mainly by promoting the dissociation of protein aggregates, and the dissociated protein particles further interacted to form denser aggregates through disulfide bond, thus facilitating the crosslinking and reaggregation of protein aggregates to form denser gel structure. Overall, ultrasound treatment is an effective approach to improve the properties of SEW-CSPI composite gels, which can improve the potential utilization of SEW and SPI in food processing.

Influence and effect mechanism of disulfide bonds linkages between protein-coated lipid droplets and the protein matrix on the physicochemical properties, microstructure, and protein structure of ovalbumin emulsion gels

In this study, disulfide bonds between the interfacial protein film formed on the lipid particles and the protein in ovalbumin emulsion gels were blocked with 0, 1, 3, 5 and 10 mM of the N-ethylmaleimide (NEM) to explore the influence and effect mechanism of disulfide bonds between the interfacial proteins on the physicochemical properties, microstructure, and protein structure of sunflower oil-ovalbumin emulsion gels. Ovalbumin emulsion gels with NEM-treated ovalbumin emulsion (N-OE) had lower hardness, free sulfhydryl content, water holding capacity (WHC), and surface hydrophobicity, but higher spin-spin relaxation time (T₂) than ovalbumin emulsion gels with NEM-treated ovalbumin substrate solution (N-OSS). In addition, N-OE and N-OSS had lower hardness, free sulfhydryl content, WHC and surface hydrophobicity, as well as a more coarse and disordered microstructure than non-NEM treated ovalbumin emulsion gel (control group). The free sulfhydryl content, hardness, WHC, and surface hydrophobicity of the ovalbumin emulsion gels all decreased as the NEM concentration rose (p < 0.05), whereas the amide A band changed to higher wave numbers. These results collectively indicated that the reduction of disulfide between the interfacial layer and the proteins inhibited the hydrophobic effect, the formation of hydrogen bonds, and prevented the formation of larger aggregates. Thus the disulfide bonds between the interfacial proteins contribute to the hardness enhancement and water stabilization of the ovalbumin gel.

Estimation of model accuracy by a unique set of features and tree-based regressor

Computationally generated models of protein structures bridge the gap between the practically negligible price tag of sequencing and the high cost of experimental structure determination. By providing a low-cost (and often free) partial alternative to experimentally determined structures, these models help biologists design and interpret their experiments. Obviously, the more accurate the models the more useful they are. However, methods for protein structure prediction generate many structural models of various qualities, necessitating means for the estimation of their accuracy. In this work we present MESHI_consensus, a new method for the estimation of model accuracy. The method uses a tree-based regressor and a set of structural, target-based, and consensus-based features. The new method achieved high performance in the EMA (Estimation of Model Accuracy) track of the recent CASP14 community-wide experiment (https://predictioncenter.org/casp14/index.cgi). The tertiary structure prediction track of that experiment revealed an unprecedented leap in prediction performance by a single prediction group/method, namely AlphaFold2. This achievement would inevitably have a profound impact on the field of protein structure prediction, including the accuracy estimation sub-task. We conclude this manuscript with some speculations regarding the future role of accuracy estimation in a new era of accurate protein structure prediction.

Interactions between wheat globulin and gluten under alkali or salt condition and its effects on noodle dough rheology and end quality

The influences of wheat globulin on dough and noodle quality under alkali or salt conditionwere investigated, and the protein interactions were revealed. Results indicated that dough viscoelasticity, noodle hardness, springiness and extensibility of samples with globulin added were remarkably increased under alkali condition. However, the corresponding enhancement was less significant under salt condition. In dough system, added globulin decreased the protein surface hydrophobicity by 38.71%, implying the enhancement of hydrophobic interactions. Under salt and alkali conditions, added globulin further increased the β-sheets structure by 1.68% and 3.17%, respectively, indicating the enhancement of hydrogen bonds interaction. In addition, disulfide bonds interactions between globulin and gluten have also been demonstrated induced by alkali. The results were accountable for protein network polymerization observed in micro-structures. This paper provides new insights into the structural properties of wheat globulin, and demonstrates the excellent potential to improve noodle processing quality under alkali condition significantly.

Mimetic Neural Networks: A Unified Framework for Protein Design and Folding

Recent advancements in machine learning techniques for protein structure prediction motivate better results in its inverse problem–protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein backbone design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be met and even improved, given recent architectures for protein folding.

Ions-regulated aggregation kinetics for egg white protein: A promising formulation with controlled gelation and rheological properties

This study aims to evaluate the structure of ions-regulated gelation of egg white protein (EWP) via aggregation kinetics model, which was built by monitoring turbidity. Results showed that compared with NaCl and KCl, the addition of Na2SO4 increased free sulfhydryl content, surface hydrophobicity and particle size of EWP significantly, while weakened the order of secondary structure. Hence, strengthened gel network structure was observed with higher porosity, which improved the texture profiles and rheological properties of EWP gels. Based on these phenomena above, the relationship between aggregation behavior and gelling properties with ions was further investigated by aggregation kinetics model and principal component analysis. Because of the enhancement of protein interactions, the aggregation growth rate with Na2SO4 was much faster than the samples with NaCl, which reflected over-aggregation due to the accelerated nucleation process and resulted in firmed gel network structure.

Fabrication and Characterization of Ultra-High-Pressure (UHP)-Induced Whey Protein Isolate/κ-Carrageenan Composite Emulsion Gels for the Delivery of Curcumin

The emulsion gels have attracted extensive interests due to their unique physical characters, remarkable stability, and control release properties of flavor and functional components compared to emulsions in liquid. In the current work, whey protein isolate (WPI)/κ-carrageenan (κ-CG) composite emulsion gels were fabricated based on the ultra-high-pressure (UHP) technology, in replacement of the traditional thermal, acid, or enzyme processing. Uniform composite emulsion gels could be fabricated by UHP above 400 MPa with minimum WPI and κ-CG concentrations of 8.0 and 1.0 wt%, respectively. The formation of UHP-induced emulsion gels is mostly attributed to the hydrophobic interaction and hydrogen bonding. The emulsion gels with different textures, rheology properties, and microstructures could be fabricated through adjusting the formulations (WPI concentration, κ-CG concentration, and oil phase fraction) as well as processing under different conditions (pressure and time). Afterward, curcumin-loaded emulsion gels were fabricated and subjected to an in vitro simulated gastrointestinal digestion in order to investigate the gastrointestinal fate of curcumin. In vitro simulated digestion results demonstrated that the UHP treatment significantly retarded the release of curcumin but had little impact on the bioaccessibility of curcumin. The results in this work provide useful information for the construction of emulsion gels through a non-thermal process, which showed great potential for the delivery of heat-sensitive bioactive components.

Predicting mutant outcome by combining deep mutational scanning and machine learning

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.

PathMolD-AB: Spatiotemporal pathways of protein folding using parallel molecular dynamics with a coarse-grained model

Solving the protein folding problem (PFP) is one of the grand challenges still open in computational biophysics. Globular proteins are believed to evolve from initial configurations through folding pathways connecting several thermodynamically accessible states in a free energy landscape until reaching its minimum, inhabited by the stable native structures. Despite its huge computational burden, molecular dynamics (MD) is the leading approach in the PFP studies by preserving the Newtonian temporal evolution in the canonical ensemble. Non-trivial improvements are provided by highly parallel implementations of MD in cost-effective GPUs, concomitant to multiscale descriptions of proteins by coarse-grained minimalist models. In this vein, we present the PathMolD-AB framework, a comprehensive software package for massively parallel MD simulations using the canonical ensemble, structural analysis, and visualization of the folding pathways using the minimalist AB-model. It has, also, a tool to compare the results with proteins re-scaled from the PDB. We simulate and analyze, as case studies, the folding of four proteins: 13FIBO, 2GB1, 1PLC and 5ANZ, with 13, 55, 99 and 223 amino acids, respectively. The datasets generated from simulations correspond to the MD evolution of 3500 folding pathways, encompassing 35×106 states, which contains the spatial amino acid positions, the protein free energies and radii of gyration at each time step. Results indicate that the speedup of our approach grows logarithmically with the protein length and, therefore, it is suited for most of the proteins in the PDB. The predicted structures simulated by PathMolD-AB were similar to the re-scaled biological structures, indicating that it is promising for the study of the PFP study.

Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning

The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a high price tag, computational predictions are highly desirable. Given a protein sequence, computational methods produce numerous 3D structures known as decoys. Selection of the best quality decoys is both challenging and essential as the end users can handle only a few ones. Therefore, scoring functions are central to decoy selection. They combine measurable features into a single number indicator of decoy quality. Unfortunately, current scoring functions do not consistently select the best decoys. Machine learning techniques offer great potential to improve decoy scoring. This paper presents two machine-learning based scoring functions to predict the quality of proteins structures, i.e., the similarity between the predicted structure and the experimental one without knowing the latter. We use different metrics to compare these scoring functions against three state-of-the-art scores. This is a first attempt at comparing different scoring functions using the same non-redundant dataset for training and testing and the same features. The results show that adding informative features may be more significant than the method used.

SURPASS Low-Resolution Coarse-Grained Protein Modeling

Coarse-grained modeling of biomolecules has a very important role in molecular biology. In this work we present a novel SURPASS (Single United Residue per Pre-Averaged Secondary Structure fragment) model of proteins that can be an interesting alternative for existing coarse-grained models. The design of the model is unique and strongly supported by the statistical analysis of structural regularities characteristic for protein systems. Coarse-graining of protein chain structures assumes a single center of interactions per residue and accounts for preaveraged effects of four adjacent residue fragments. Knowledge-based statistical potentials encode complex interaction patterns of these fragments. Using the Replica Exchange Monte Carlo sampling scheme and a generic version of the SURPASS force field we performed test simulations of a representative set of single-domain globular proteins. The method samples a significant part of conformational space and reproduces protein structures, including native-like, with surprisingly good accuracy. Future extension of the SURPASS model on large biomacromolecular systems is briefly discussed.

Ab initio folding of extended α-helix: a theoretical study about the role of electrostatic polarization in the folding of helical structures

In this work, we report the ab initio folding of three different extended helical peptides namely 2khk, N36, and C34 through conventional molecular dynamics simulation at room temperature using implicit solvation model. Employing adaptive hydrogen bond specific charge (AHBC) scheme to account for the polarization effect of hydrogen bonds established during the simulation, the effective folding of the three extended helices were observed with best backbone RMSDs in comparison to the experimental structures over the helical region determined to be 1.30 Å for 2khk, 0.73 Å for N36 and 0.72 Å for C34. In this study, 2khk will be used as a benchmark case serving as a means to compare the ability of polarized (AHBC) and nonpolarized force field in the folding of an extended helix. Analyses conducted revealed the ability of the AHBC scheme in effectively folding the extended helix by promoting helix growth through the stabilization of backbone hydrogen bonds upon formation during the folding process. Similar observations were also noted when AHBC scheme was employed during the folding of C34 and N36. However, under Amber03 force field, helical structures formed during the folding of 2khk was not accompanied by stabilization thus highlighting the importance of electrostatic polarization in the folding of helical structures.

Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement

I-TASSER is an automated pipeline for protein tertiary structure prediction using multiple threading alignments and iterative structure assembly simulations. In CASP9 experiments, two new algorithms, QUARK and fragment-guided molecular dynamics (FG-MD), were added to the I-TASSER pipeline for improving the structural modeling accuracy. QUARK is a de novo structure prediction algorithm used for structure modeling of proteins that lack detectable template structures. For distantly homologous targets, QUARK models are found useful as a reference structure for selecting good threading alignments and guiding the I-TASSER structure assembly simulations. FG-MD is an atomic-level structural refinement program that uses structural fragments collected from the PDB structures to guide molecular dynamics simulation and improve the local structure of predicted model, including hydrogen-bonding networks, torsion angles, and steric clashes. Despite considerable progress in both the template-based and template-free structure modeling, significant improvements on protein target classification, domain parsing, model selection, and ab initio folding of β-proteins are still needed to further improve the I-TASSER pipeline.

Hybrid approach to analysis of β-sheet structures based on signal processing and statistical consideration

A number of biotechnology applications are based on protein design. For this design, the relationship between a protein’s primary structure and its conformation is of vital importance. A β-sheet is a common feature of a protein’s two-dimensional structure; therefore, elucidating the principles governing β-sheet structure and its stability is critical for understanding the protein-folding process. In the three-dimensional representation of protein molecules, C α carbon coordinates (carbon atom immediately adjacent to the carboxylate group) have often been employed instead of the complete set of coordinates for the corresponding residues. Using the C α carbon coordinates, we showed that particular amino acids are not randomly distributed within a β-sheet structure. On the basis of a new statistical approach for the analysis of a spatial distribution of amino acids in a protein, presented by their physico-chemical parameters, the electron–ion interaction potential (EIIP) and hydrophobicity, are described here. The relationship between amino acid positions inside the β-sheet and the EIIP and hydrophobicity parameters was established. The correlation between amino acid propensities related to the β-sheet was examined using multiple cross-spectra analysis. We also applied the continuous wavelet transform for the analysis of selected β-sheet structures using the EIIP and hydrophobicity parameters. The findings provide new insight into conformational propensities of amino acids for the adaption of β-sheet structures.