Structural Features Of Proteins Research Articles

In silico design of a TLR4-mediating multiepitope chimeric vaccine against amyotrophic lateral sclerosis via advanced immunoinformatics.

Amyotrophic lateral sclerosis (ALS) is the most prevalent motor neuron disorder worldwide. In ALS, progressing disease can result from misfolding and aggregation of superoxide dismutase-1 (SOD1) or TAR DNA-binding protein 43kDa (TDP43). An efficient immunotherapy for ALS should spare intact SOD1 while eliminating its dysfunctional variant. We utilized advanced immunoinformatics to suggest a potential vaccine candidate against ALS by proposing a model of dynamic TLR4 mediation and induction of a specific Th2-biased shift against mutant SOD1, TDP43, and TRAF6, a protein that specifically interacts with dysfunctional SOD1. SOD1, TDP43, and TRAF6 were retrieved in FASTA. Immune Epitopes Database and CTLpred suggested T/B-cell epitopes from disease-specific regions of selected antigens. A TLR4-mediating adjuvant, RS01, was used. Sequences were assembled via suitable linkers. Tertiary structure of the protein was calculated. Refined protein structure and physicochemical features of the 3D structure were verified in silico. Differential immune induction was assessed via C-ImmSim. GROningen MAchine for Chemical Simulation was used to assess evolution of the docked vaccine-TLR4 complex in blood. Our protein showed high structural quality and was nonallergenic and immune inducing. Also, the vaccine-TLR4 complex stability was verified by RMSD, RMSF, gyration, and visual analyses of the molecular dynamic trajectory. Contact residues in the vaccine-TLR4 complex showed favorable binding energies. Immune stimulation analyses of the proposed candidate demonstrated a sustained memory cell response and a strong adaptive immune reaction. We proposed a potential vaccine candidate against ALS and verified its physicochemical and immune inducing features. Future studies should assess this vaccine in animal studies.

NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.

Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields of biology and biotechnology at large, such methods have the downside of high demands in terms of computing power and runtime, hampering their applicability to large datasets. Here, we present NetSurfP-3.0, a tool for predicting solvent accessibility, secondary structure, structural disorder and backbone dihedral angles for each residue of an amino acid sequence. This NetSurfP update exploits recent advances in pre-trained protein language models to drastically improve the runtime of its predecessor by two orders of magnitude, while displaying similar prediction performance. We assessed the accuracy of NetSurfP-3.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features, with a runtime that is up to to 600 times faster than the most commonly available methods performing the same tasks. The tool is freely available as a web server with a user-friendly interface to navigate the results, as well as a standalone downloadable package.

Linking the thermostability of FIP-nha (Nectria haematococca) to its structural properties

Fungal immunomodulatory proteins (FIPs) have been investigated for their use as potential natural derived anti-tumor molecules. However, the stability of FIPs is critical for their preparation and storage. In this study, the correlation between thermal stability and protein structural features of rFIP-nha, with significant anti-tumor activity, has been evaluated. For comprehensive analysis, FIP-nha and its homologues FIP-gmi, FIP-fve, and LZ-8 were all recombinantly expressed in E. coli. In solution, rFIP-nha and rFIP-gmi formed tetramers; rFIP-fve and rLZ-8 appeared as dimers. Their melting temperatures were 85.1 °C, 77.8 °C, 66.5 °C, and 64.4 °C, respectively. Accordingly, their cytotoxicity was also temperature dependent. To investigate the underlying mechanism of their thermostability, we solved the crystal structure of FIP-nha. Detailed structure analysis, molecular dynamic simulation and mutagenesis studies indicated that a higher thermostability was correlated to higher oligomerization states, larger interface area, and more interactions. The structure property studies indicate that Y12, D61 and Y108 were critical for oligomerization and high thermostability of rFIP-nha, but the dimeric and tetrameric states of rFIP-nha exert similar cytotoxicity on A549 cells. Taken together, these findings reveal that thermostability of FIPs was dependent on their oligomerization state, and correlated with their cytotoxicity.

Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction

Advanced protein structure prediction requires evolutionary information from multiple sequence alignments (MSAs) from evolutionary couplings that are not always available. Artificial intelligence (AI)-based predictions inputting only single sequences are faster but so inaccurate as to render speed irrelevant. Here, we described a competitive prediction of inter-residue distances (2D structure) exclusively inputting embeddings from pre-trained protein language models (pLMs), namely ProtT5, from single sequences into a convolutional neural network (CNN) with relatively few layers. The major advance used the ProtT5 attention heads. Our new method, EMBER2, which never requires any MSAs, performed similarly to other methods that fully rely on co-evolution. Although clearly not reaching AlphaFold2, our leaner solution came somehow close at substantially lower costs. By generating protein-specific rather than family-averaged predictions, EMBER2 might better capture some features of particular protein structures. Results from using protein engineering and deep mutational scanning (DMS) experiments provided at least a proof of principle for such a speculation.

Exploration of Cytochrome P450 Structure and Function in In Silico Assays for Toxicity

Cytochrome P450 (CYP or P450) enzymes are known for their role in detoxifying xenobiotics and may at times cause toxicity. CYP1A1gene polymorphisms have been linked to susceptibility risks of various cancers and neoplasms, including bladder cancer. The aim of the study was to use bioinformatics tools to examine structural differences in CYP1A1 wild type and isoforms 2 and 3. Protein sequences of selected CYPs were retrieved from the NCBI protein databank and GenPept. Sequence homologies were searched for by BLAST, and by multiple sequence alignments. Protein structural characteristics such as ligand binding and secondary structure prediction were analyzed by Phyre. The CYPIA1 wild type and isoforms had 33 % variation in their amino acid residues and binding sites. The predicted binding sites for the first 6 amino acid residues were similar between the wild type and isoform two; however, the average distances from the ligands differed. The predicted secondary structure of the CYP1A1 isoform 3 exhibited the widest difference from the wild type in the percentage of disordered helix (17%), alpha helix (66%), beta strand (3%) and trans‐membrane domains (12%). Differences in nucleotide sequence between the CYP1A1 wild type and isoforms caused a change in the protein structure which further altered the trans‐membrane topology and ligand binding potential of the protein. These findings indicate structural deficits among the three Cytochrome P450 isoforms, suggesting the possible conferment of risks to toxicity and associated diseases.

A comparison between analytical approaches for molecular weight estimation of proteins with variable levels of glycosylation.

Biotherapeutics, such as mAbs and fusion proteins, are a major and rapidly growing class of pharmaceuticals. Majority of the biopharmaceuticals are glycoproteins, wherein about 1 to 30% of their molecular weight (MW) are contributed by the glycans. Determination of MW of heavily glycosylated proteins, such as Fc-fusion proteins, is seriously hampered by the physicochemical characteristics and heterogeneity of the attached carbohydrates. Glycosylation influences the expected size of the glycoprotein, which leads to disproportionate MW estimation, in size-dependent methods. Hence, in this study, we have demonstrated the advantages and limitations of four widely used MW estimation techniques for three proteins having varying levels of glycosylation. It was proven that glycosylation had least impact on MW determination by SEC-MALS and SV-AUC. However, MW estimation by LC-MS and SDS-PAGE was extensively hampered by the degree of glycosylation. It is, thus, essential to consider the structural characteristics of proteins while selecting a technique for determining their MW.

Selectin-Mediated Signaling-Shedding Light on the Regulation of Integrin Activity in Neutrophils.

As a consequence of tissue injury or infection, neutrophils are recruited in a stepwise recruitment process from the bloodstream into the surrounding tissue. Selectins are a family of adhesion molecules comprised of L-, E-, and P-selectin. Differences in expression patterns, protein structure, and ligand binding characteristics mediate distinct functions of each selectin. Interactions of selectins and their counter-receptors mediate the first contact of neutrophils with the endothelium, as well as subsequent neutrophil rolling along the endothelial surface. For efficient neutrophil recruitment, activation of β2-integrins on the cell surface is essential. Integrin activation can be elicited via selectin- as well as chemokine-mediated inside-out signaling resulting in integrin conformational changes and clustering. Dysregulation of selectin-induced integrin activation on neutrophils is involved in the development of severe pathological disease conditions including leukocyte adhesion deficiency (LAD) syndromes in humans. Here, we review molecular mechanisms involved in selectin-mediated signaling pathways in neutrophils and their impact on integrin activation, neutrophil recruitment, and inflammatory diseases.

Analyzing structural features of proteins from deep-sea organisms.

Protein adaptations to extreme environmental conditions are drivers in biotechnological process optimization and essential to unravel the molecular limits of life. Most proteins with such desirable adaptations are found in extremophilic organisms inhabiting extreme environments. The deep sea is such an environment and a promising resource that poses multiple extremes on its inhabitants. Conditions like high hydrostatic pressure and high or low temperature are prevalent and many deep-sea organisms tolerate multiple of these extremes. While molecular adaptations to high temperature are comparatively good described, adaptations to other extremes like high pressure are not well-understood yet. To fully unravel the molecular mechanisms of individual adaptations it is probably necessary to disentangle multifactorial adaptations. In this study, we evaluate differences of protein structures from deep-sea organisms and their respective related proteins from nondeep-sea organisms. We created a data collection of 1281 experimental protein structures from 25 deep-sea organisms and paired them with orthologous proteins. We exhaustively evaluate differences between the protein pairs with machine learning and Shapley values to determine characteristic differences in sequence and structure. The results show a reasonable discrimination of deep-sea and nondeep-sea proteins from which we distinguish correlations previously attributed to thermal stability from other signals potentially describing adaptions to high pressure. While some distinct correlations can be observed the overall picture appears intricate.

Genome-wide Identification of Metal Tolerance Protein Genes in Peanut: Differential Expression in the Root of Two Contrasting Cultivars Under Metal Stresses.

Metal tolerance proteins (MTP) are Me2+/H+(K+) antiporters that play important roles in the transport of divalent cations in plants. However, their functions in peanut are unknown. In the present study, a total of 24 AhMTP genes were identified in peanut, which were divided into seven groups belonging to three substrate-specific clusters (Zn-CDFs, Zn/Fe-CDFs, and Mn-CDFs). All AhMTP genes underwent whole genome or segmental gene duplication events except AhMTP12. Most AhMTP members within the same subfamily or group generally have similar gene and protein structural characteristics. However, some genes, such as AhMTP1.3, AhMTP2.4, and AhMTP12, showed wide divergences. Most of AhMTP genes preferentially expressed in reproductive tissues, suggesting that these genes might play roles in metal transport during the pod and seed development stages. Excess metal exposure induced expressions for most of AhMTP genes in peanut roots depending on cultivars. By contrast, AhMTP genes in the root of Fenghua 1 were more sensitive to excess Fe, Cd, and Zn exposure than that of Silihong. Stepwise linear regression analysis showed that the percentage of Fe in shoots significantly and positively correlated with the expression of AhMTP4.1, AhMTP9.1, and AhMTPC4.1, but negatively correlated with that of AhMTPC2.1 and AhMTP12. The expression of AhMTP1.1 showed a significant and negative correlation with the percentage of Mn in shoots. The percentage of Zn in shoots was significantly and positively correlated with the expression of AhMTP2.1 but was negatively correlated with that of AhMTPC2.1. The differential responses of AhMTP genes to metal exposure might be, at least partially, responsible for the different metal translocation from roots to shoots between Fenghua 1 and Silihong.

Ganoderma immunomodulatory proteins: mushrooming functional FIPs

Fungal immunomodulatory protein (FIP) is a novel functional protein family with specific immunomodulatory activity identified from several macro-fungi. A variety of biological activities of FIPs have been reported, such as anti-allergy, anti-tumor, mitogenic activity, and immunomodulation. Among all known FIPs, the firstly discovered FIP was isolated from Ganoderma lucidum, and most FIP members were from Ganoderma genus. Compared with other FIPs, Ganoderma FIPs possess some advantageous bioactivities, like stronger anti-tumor activity. Therein, gene sequences, protein structural features, biofunctions, and recombinant expression of Ganoderma FIPs were summarized and addressed, focusing on elucidating their anti-tumor activity and molecular mechanisms. Combined with current advances, development potential and application of Ganoderma FIPs were also prospected. KEY POINTS: • More than a dozen of reported FIPs are identified from Ganoderma species. • Ganoderma immunomodulatory proteins have superior anti-tumor activity with promising prospects and application. • Current review comprehensively addresses characterization, biofunctions, and anti-tumor mechanisms of Ganoderma FIPs.

Effects of lipoxygenase/linoleic acid on the structural characteristics and aggregation behavior of pork myofibrillar protein under low salt concentration

The present study sought to explore the effects of lipoxygenase/linoleic acid (LOX/LA) oxidation system treatment on protein oxidation, structural characteristics, aggregation behavior, rheological and morphological properties of pork myofibrillar protein (MP) at low salt condition (0.1 M NaCl). Incubation of MP with LOX/LA converted protein sulfhydryl groups to disulfide bonds and promoted carbonylation in a concentration-dependent manner. MP structure unfolded from α-helix to β-sheets with increase in LOX concentration (0–2500 U/mL), and buried hydrophobic residues were exposed as shown by the results from circular dichroism and fluorescence spectroscopy analysis. Particle size distribution and SDS-PAGE analysis showed that LOX/LA treatment promoted MP aggregation to form oligomers. These results were further validated using atomic force microscopy (AFM). Analysis of rheological properties indicated moderate LOX/LA (<1000 U/mL) system treatment significantly decreased storage and loss modulus of MP gel. These findings indicate that LOX can be used to improve the gelling properties of MP at low salt concentration.

Impact of L-cysteine addition on nixtamalized corn dough: proteins and viscoelasticity

&#x0D; The effect of L-cysteine on the solubility, protein structure, rheological and textural characteristics of corn masas were investigated. Masa was prepared with L-cysteine at two levels (0.25, 0.50 %). Size exclusion chromatography and Fourier transform infrared spectroscopy were utilized for protein characterization. Dynamic rheology and textural profile analysis were carried out on masas. There was a significant difference in relative solubility between the control and masas containing L-cysteine. Treatments with L-cysteine increased the relative solubility of proteins. Determination of FT-IR revealed that addition of L-cysteine did not affects secondary structure of protein. Data also showed significant differences in textural properties. Masas con L-cysteine improved the springiness, adhesiveness and cohesiveness. This investigation reveal that L-cysteine, may improve the elasticity or storage moduli, important characteristics of masas.&#x0D;

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

BackgroundEstimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model.ResultWe developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods.ConclusionThis work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA

Plant SWEET Family of Sugar Transporters: Structure, Evolution and Biological Functions.

The SWEET (sugars will eventually be exported transporter) family was identified as a new class of sugar transporters that function as bidirectional uniporters/facilitators and facilitate the diffusion of sugars across cell membranes along a concentration gradient. SWEETs are found widely in plants and play central roles in many biochemical processes, including the phloem loading of sugar for long-distance transport, pollen nutrition, nectar secretion, seed filling, fruit development, plant–pathogen interactions and responses to abiotic stress. This review focuses on advances of the plant SWEETs, including details about their discovery, characteristics of protein structure, evolution and physiological functions. In addition, we discuss the applications of SWEET in plant breeding. This review provides more in-depth and comprehensive information to help elucidate the molecular basis of the function of SWEETs in plants.

Mass Spectrometric Fingerprints of Bacteria and Archaea for Life Detection on Icy Moons.

The icy moons of the outer Solar System display evidence of subsurface liquid water and, therefore, potential habitability for life. Flybys of Saturn's moon Enceladus by the Cassini spacecraft have provided measurements of material from plumes that suggest hydrothermal activity and the presence of organic matter. Jupiter's moon Europa may have similar plumes and is the target for the forthcoming Europa Clipper mission that carries a high mass resolution and high sensitivity mass spectrometer, called the MAss Spectrometer for Planetary EXploration (MASPEX), with the capability for providing detailed characterization of any organic materials encountered. We have performed a series of experiments using pyrolysis-gas chromatography-mass spectrometry to characterize the mass spectrometric fingerprints of microbial life. A range of extremophile Archaea and Bacteria have been analyzed and the laboratory data converted to MASPEX-type signals. Molecular characteristics of protein, carbohydrate, and lipid structures were detected, and the characteristic fragmentation patterns corresponding to these different biological structures were identified. Protein pyrolysis fragments included phenols, nitrogen heterocycles, and cyclic dipeptides. Oxygen heterocycles, such as furans, were detected from carbohydrates. Our data reveal how mass spectrometry on Europa Clipper can aid in the identification of the presence of life, by looking for characteristic bacterial fingerprints that are similar to those from simple Earthly organisms.

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.

Effect of Fermentation on the Milk Allergen Proteins During Yogurt-making

Cow milk and dairy products have a great significance in human nutrition but may lead to concerns due to milk protein allergy. Reduction or elimination of cow milk allergy is mainly achieved by the destruction of allergenic epitopes in the molecular structure of relevant proteins via various treatments during dairy processing. Yogurt is one of the most consumed fermented dairy foods. The purpose of this study was to investigate the effect of fermentation on the protein content and molecular structure of milk during yogurt production. Protein profiles and structural features of the dairy samples collected at various steps of yogurt manufacturing were analyzed using electrophoresis and spectroscopy. Fermentation after pasteurization treatment remarkably altered the content and structure of allergen proteins through unfolding, denaturation, and aggregation. This might suggest a correlation with the modified allergenicity of milk proteins in the end product. Immunological assays ongoing will help a better understanding of the allergenicity of yogurt products analyzed in this study.

The effect of enzymatic pretreatment of chickpea on functional properties and antioxidant activity of alkaline protein isolate

Functional properties and antioxidant activity of protein isolates extracted by alkali from chickpea seed after treatment with cellulase and xylanase cocktail and individual arabinofuranosidase were evaluated. Both enzymatic treatments improved recovery of protein by more than 30%, moreover, arabinofuranosidase enabled higher extraction efficiency - above 93%. Protein extracted after treatment with enzyme cocktail showed improved solubility and oil holding capacity by 14% and 80%, respectively, while water holding capacity was increased by 130% after both applied enzymatic pretreatments. The action of enzyme cocktail was more beneficial for improving emulsifying activity and stability of alkaline isolate by 22% and 31%, respectively. Whipping properties of alkaline protein isolate were significantly improved when both enzymatic pretreatments were applied with foam stability increased by 150%. Protein from extractions with arabinofuranosidase and enzyme cocktail expressed enhanced antioxidant activity by 70% and 110%, respectively. Analysis of protein pattern and structural characteristics indicated differences between investigated isolates.

Natural and Synthetic Halogenated Amino Acids-Structural and Bioactive Features in Antimicrobial Peptides and Peptidomimetics.

The 3D structure and surface characteristics of proteins and peptides are crucial for interactions with receptors or ligands and can be modified to some extent to modulate their biological roles and pharmacological activities. The introduction of halogen atoms on the side-chains of amino acids is a powerful tool for effecting this type of tuning, influencing both the physico-chemical and structural properties of the modified polypeptides, helping to first dissect and then rationally modify features that affect their mode of action. This review provides examples of the influence of different types of halogenation in amino acids that replace native residues in proteins and peptides. Examples of synthetic strategies for obtaining halogenated amino acids are also provided, focusing on some representative compounds and their biological effects. The role of halogenation in native and designed antimicrobial peptides (AMPs) and their mimetics is then discussed. These are in the spotlight for the development of new antimicrobial drugs to counter the rise of antibiotic-resistant pathogens. AMPs represent an interesting model to study the role that natural halogenation has on their mode of action and also to understand how artificially halogenated residues can be used to rationally modify and optimize AMPs for pharmaceutical purposes.

Chemical and structural characteristics of proteins of non-vital and vital wheat glutens

Vital wheat gluten is an important ingredient in the baking industry. Its technological quality is associated to its protein content and protein chemical and structural characteristics. The aim of this study was to evaluate these protein characteristics of commercial vital wheat glutens from different origins (VGA and VGB), improving information for application in baking. Besides, non-vital gluten (NVG) was produced through severe heat treatment to verify the effect of high temperature on its chemical and structural characteristics and gluten network development. Relative amounts of gluten fractions, protein solubility, linkages involved in gluten network structure, secondary structure and gluten network micrographs were analyzed. Differences in the chemical and structural characteristics of VGA and VGB were observed. The differences observed revealed that VGA showed a better balance between gliadin (extensibility) and glutenin (elasticity) than VGB, and also VGA presented a greater amount of β-turn structure, involved in the elastic properties of the gluten network. Furthermore, NVG formed insoluble protein aggregates with high amounts of β-turn structure and undefined secondary structure; as a result, NVG did not form a network-like structure. These differences showed the need for more information about vital gluten proteins that will be used in bread making, contributing to its commercial specifications.