The role of cysteine in the formation of domain structures of papain and legumin in peas, involved in limited proteolysis

The limited use of plant proteins for food is explained by their low bioavailability and poor digestibility by enzymes of the gastrointestinal tract. Partially reproduced enzymatic processes of limited proteolysis that occur during seed germination are used to modify and improve the edibility characteristics of seed proteins. The present work discusses the possibility of reducing the duration of seed germination processes by optimising the conditions and parameters of limited proteolysis. To optimise manufacturing high-quality final product, enzymes (additional to the natural enzymes in the seed) and proteolysis conditions (in this case, temperature), as well as added substances (hydrolysis activators), were selected. The influence of cysteine on the formation of domain structures of proteins (enzymes and globulins) was evaluated. The proposed expressions can be used to determine those fragments of protein molecules that form stable domains and become unstructured when exposed to enzymes. Optimal conditions for limited proteolysis were identified based on the physical mechanism of action of papain-like proteolytic enzymes on pea legumin LegA (3KSC, CAA10722). It is shown that the decomposition of protein secondary structures takes 6–8 times longer, since the formed hydrogen bonds limit the access of enzymes to the corresponding amino-acid residues. It is also demonstrated that the decomposition of hydrogen bonds, e.g. by preliminary heat treatment of proteins, will broaden the prospects for limited proteolysis.

In Silico Study of Secondary Structure of Hemoglobin Protein

Protein structure prediction is one of the important goals in the area of bioinformatics and biotechnology. Prediction methods include structure prediction of both secondary and tertiary structures of protein. Protein secondary structure prediction infers knowledge related to presence of helixes, sheets and coils in a polypeptide chain whereas protein tertiary structure prediction infers knowledge related to three dimensional structures of proteins. Protein secondary structures represent the possible motifs or regular expressions represented as patterns that are predicted from primary protein sequence in the form of alpha helix, betastr and and coils. The secondary structure prediction is useful as it infers information related to the structure and function of unknown protein sequence. There are various secondary structure prediction methods used to predict about helixes, sheets and coils. Based on these methods there are various prediction tools under study. This study includes prediction of hemoglobin using various tools. The results produced inferred knowledge with reference to percentage of amino acids participating to produce helices, sheets and coils. PHD and DSC produced the best of the results out of all the tools used.

Moisture Distribution and Structural Properties of Frozen Cooked Noodles with NaCl and Kansui

The effects of NaCl (1–3%) and kansui (0.5–1.5%) on the quality of frozen cooked noodles (FCNs) were investigated, which provided a reference for alleviating the quality deterioration of FCNs. Textural testing illustrated that the optimal tensile properties were observed in 2% NaCl (N-2) and the maximum hardness and chewiness were reached at 1% kansui (K-1). Compared to NaCl, the water absorption and cooking loss of recooked FCNs increased significantly with increasing kansui levels (p < 0.05). Rheological results confirmed NaCl and kansui improved the resistance to deformation and recovery ability of thawed dough; K-1 especially had the highest dough strength. SEM showed N-2 induced a more elongated fibrous protein network that contributed to the extensibility, while excessive levels of kansui formed a deformed membrane-like gluten network that increased the solid loss. Moisture analysis revealed that N-2 reduced the free water content, while K-1 had the lowest freezable water content and highest binding capacity for deeply adsorbed water. The N-2 and K-1 induced more ordered protein secondary structures with stronger intermolecular disulfide bonds, which were maximally improved in K-1. This study provides more comprehensive theories for the strengthening effect of NaCl and kansui on FCNs quality.

Quantitative Assessment of Chirality of Protein Secondary Structures and Phenylalanine Peptide Nanotubes

In this study we consider the features of spatial-structure formation in proteins and their application in bioengineering. Methods for the quantitative assessment of the chirality of regular helical and irregular structures of proteins are presented. The features of self-assembly of phenylalanine (F) into peptide nanotubes (PNT), which form helices of different chirality, are also analyzed. A method is proposed for calculating the magnitude and sign of the chirality of helix-like peptide nanotubes using a sequence of vectors for the dipole moments of individual peptides.

Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction

Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem—driven by the recent results obtained by computational methods in this task—i) template-free classifiers, based on machine learning techniques; and ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers—six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.

A Generic Force Field for Simulating Native Protein Structures Using Dissipative Particle Dynamics

A coarse-grained force field for molecular dynamics simulations of native structures of proteins in a dissipative particle dynamics (DPD) framework is developed. The parameters for bonded interactions are derived by mapping the bonds and angles for 20 amino acids onto target distributions obtained from fully atomistic simulations in explicit solvent. A dual-basin potential is introduced for stabilizing backbone angles, to cover a wide spectrum of protein secondary structures. The backbone dihedral potential enables folding of the protein from an unfolded initial state to the folded native structure. The proposed force field is validated by evaluating structural properties of several model peptides and proteins including the SARS-CoV-2 fusion peptide, consisting of α-helices, β-sheets, loops and turns. Detailed comparisons with fully atomistic simulations are carried out to assess the ability of the proposed force field to stabilize the different secondary structures present in proteins. The compact conformations of the native states were evident from the radius of gyration as well as the high intensity peaks of the root mean square deviation histograms, which were found to lie below 0.4 nm. The Ramachandran-like energy landscape on the phase space of backbone angles (θ) and dihedrals (ϕ) effectively captured the conformational phase space of α-helices at ~(ϕ=50°, θ=90°) and β-strands at ~(ϕ=±180°, θ=90°-120°). Furthermore, the residue-residue native contacts are also well reproduced by the proposed DPD model. The applicability of the model to multidomain complexes is assessed using lysozyme as well as a large α helical bacterial pore-forming toxin, cytolysin A. Our studies illustrate that the proposed force field is generic, and can potentially be extended for efficient in-silico investigations of membrane bound polypeptides and proteins using DPD simulations.

The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction

The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.

Comprehensive Evaluation of PAXgene Fixation on Oral Cancer Tissues Using Routine Histology, Immunohistochemistry, and FTIR Microspectroscopy

The choice of tissue fixation is critical for preserving the morphology and biochemical information of tissues. Fragile oral tissues with lower tensile strength are challenging to process for histological applications as they are prone to processing damage, such as tissue tear, wrinkling, and tissue fall-off from slides. This leads to loss of morphological information and unnecessary delay in experimentation. In this study, we have characterized the new PAXgene tissue fixation system on oral buccal mucosal tissue of cancerous and normal pathology for routine histological and immunohistochemical applications. We aimed to minimize the processing damage of tissues and improve the quality of histological experiments. We also examined the preservation of biomolecules by PAXgene fixation using FTIR microspectroscopy. Our results demonstrate that the PAXgene-fixed tissues showed significantly less tissue fall-off from slides. Hematoxylin and Eosin staining showed comparable morphology between formalin-fixed and PAXgene-fixed tissues. Good quality and slightly superior immunostaining for cancer-associated proteins p53 and CK5/6 were observed in PAXgene-fixed tissues without antigen retrieval than formalin-fixed tissues. Further, FTIR measurements revealed superior preservation of glycogen, fatty acids, and amide III protein secondary structures in PAXgene-fixed tissues. Overall, we present the first comprehensive evaluation of the PAXgene tissue fixation system in oral tissues. This study concludes that the PAXgene tissue fixation system can be applied to oral tissues to perform diagnostic molecular pathology experiments without compromising the quality of the morphology or biochemistry of biomolecules.

Protein Secondary Structure Prediction With a Reductive Deep Learning Method

Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments.