Secondary Structural Features Research Articles

A comprehensive study on the mitochondrial genome of Volva habei and exploring phylogenetic relationships in Littorinimorpha

In order to enrich our taxonomic and systematic comprehension of Ovulidae within the evolutionary framework of Littorinimorpha. we present a comprehensive analysis of the mitochondrial genome (mitogenome) sequence of Volva habei using next-generation sequencing technology (GenBank accession number OR492307). The mitogenome spans a total length of 16,519 bp, encompassing a complete set of 37 genes, including 13 protein-coding genes (PCGs), 22 tRNAs and two rRNAs, demonstrating a distinct AT bias. Notably, trnS2 lacks a dihydrouracil (DHU) arm, thus preventing the formation of a typical secondary structure. In contrast, the remaining tRNAs exhibit a characteristic cloverleaf-like secondary structure. Comparative analysis with ancestral gastropods reveals substantial differences in three gene clusters (or genes), incorporating fifteen tRNAs and eight PCGs. Of particular significance are the observed inversions and translocations, representing the predominant types of rearrangements in V. habei. Phylogenetic analysis strongly supports the monophyletic grouping of all Littorinimorpha species, with V. habei forming a distinct Ovulidae clade. It is noteworthy that V. habei forms a sister group with Cypraeidae, collectively belonging to the Cypraeoidea. In summary, this study not only advances our comprehension of the entire mitochondrial dataset for Calyptraeoidea but also provides novel insights into the phylogenetic relationships within Littorinimorpha.

The role of secondary structures of peptide polymers on antimicrobial efficacy and antibiotic potentiation.

The rise of antibiotic resistance, biofilm formation, and dormant bacterial populations poses serious global health threats. Synthetic antimicrobial peptide (AMP) mimics offer promising alternatives, though the impact of secondary structures in polymeric AMP mimics on antimicrobial efficacy is underexplored. This study investigates chirality-controlled α-peptide polymers (D-PP and DL-PP), synthesized via ring-opening polymerization of allylglycine N -carboxy anhydrides and post-polymerization modification through thiol-ene click chemistry. D-PP adopts a stable helical structure under biomimetic conditions, whereas DL-PP remains random. This helical structure enhanced D-PP's antibacterial and antibiotic potentiation activities, amplifying antibiotic efficacy by 2- to 256-fold across various classes-including tetracyclines, ansamycins, fusidanes, macrolides, cephalosporins, and monobactams-against multidrug-resistant Gram-negative pathogens, while maintaining low hemolytic activity and high protease stability. Mechanistic investigations revealed that D-PP exhibited greater membrane interaction. D-PP and antibiotic combinations eradicated dormant bacterial populations and disrupted biofilms with minimal antimicrobial resistance development. This study paves the way for the rational design of polypeptide-based antimicrobial agents, harnessing chirality and secondary structural features to enhance the efficacy of synthetic antimicrobial peptide mimics.

Applying Machine Learning and SERS for Precise Typing of DNA Secondary Structures.

Surface-enhanced Raman spectroscopy (SERS) has been demonstrated as an effective method for elucidating secondary structural characteristics of DNA. However, the inherent complexity of the DNA conformation and the lack of SERS samples pose challenges for identifying numerous secondary structures. To address these issues, a synergistic method integrating machine learning with SERS was proposed so as to analyze the SERS spectra of 54 well-defined conformational oligonucleotides, namely, G-quadruplex (G4), i-motif (iM), double-strand (DS), and single-strand (SS) configurations. Principal component analysis (PCA) effectively segregated the oligonucleotides into three distinct conformational groups (G4s, iMs, and others). Furthermore, linear discriminant analysis (LDA), K-nearest neighbor (KNN), and support vector machine (SVM) approaches were utilized to improve the typing accuracy of 54 trained sequences. This enabled the correct classification of the structures of five untrained sequences, as well as the identification of the predominant conformations including G4, iM, and DS formed by two complementary G-rich and C-rich sequences in acidic and neutral pH conditions. The results of this study demonstrated the potential of the proposed methodology for rapid screening and prediction of secondary DNA conformations.

Statistical analysis of the unique characteristics of secondary structures in proteins

Protein folding is a complex process influenced by the primary sequence of amino acids. Early studies focused on understanding whether the specificity or the conservation of properties of amino acids was crucial for folding into secondary structures such as α-helices, β-sheets, turns, and coils. However, with the advent of artificial intelligence (AI) and machine learning (ML), the emphasis has shifted towards the precise nature and occurrence of specific amino acids. In our study, we analyzed a large set of proteins from diverse organisms to identify unique features of secondary structures, particularly in terms of the distribution of polar, non-polar, and charged amino acid residues. We found that α-helices tend to have a higher proportion of charged and non-polar groups compared to other secondary structures and that the presence of oppositely charged amino acid residues in helices stabilizes them, facilitating the formation of longer helices. These characteristics are distinct to α-helices. This study offers valuable insights for researchers in the field of protein design, enabling the de-novo creation of short helical peptides for a range of applications. We have also developed a web server for extensive analysis of proteins from different databases. The web server is housed at https://proseqanalyser.iitgn.ac.in/

Peptide vaccine design against glioblastoma by applying immunoinformatics approach

Brain tumors are considered to be one of the most fatal forms of cancer owing to their highly aggressive attributes, diverse characteristics, and notably low rate of survival. Among these tumors, glioblastoma stands out as the prevalent and perilous variant Despite the present advancements in surgical procedures, pharmacological treatment, and radiation therapy, the overall prognosis remains notably unfavorable, as merely 4.3 % of individuals manage to attain a five-year survival rate; For this reason, it has emerged as a challenge for cancer researchers. Therefore, among several immunotherapy methods, using peptide-based vaccines for cancer treatment is considered promising due to their ability to generate a focused immune response with minimal damage. This study endeavors to devise a multi-epitope vaccine utilizing an immunoinformatics methodology to address the challenge posed by glioblastoma disease. Through this approach, it is anticipated that the duration and expenses associated with vaccine manufacturing can be diminished, while simultaneously enhancing the characteristics of the vaccine. The target gene in this research is ITGA5, which was achieved through TCGA analysis by targeting the PI3K-Akt pathway as a significant association with patient survival. Subsequently, the suitable epitopes of T and B cells were selected through various immunoinformatics tools by analyzing their sequence. Then, nine epitopes were merged with GM-CSF as an adjuvant to enhance immunogenicity. The outcomes encompass molecular docking, molecular dynamics (MD) simulation, simulation of the immune response, prognosis and confirmation of the secondary and tertiary structure, Chemical and physical characteristics, toxicity, as well as antigenicity and allergenicity of the potential vaccine candidate against glioblastoma.

Strategy for improving circular dichroism spectra deconvolution accuracy for macrocyclic peptides in drug discovery

Peptide therapeutics have emerged as an appealing modality in the pharmaceutical industry. Understanding peptide conformation in solution remains one of the most critical areas for peptide drug development. Circular dichroism (CD) spectroscopy is a useful technique to study the secondary structure of proteins and peptides, but the current approaches are limited to protein-focused models to predict high-order structures of peptides, and the models were built based on X-ray crystallography instead of solution-based technique, as a result, such models may have poor predictions for peptides. In this study, we present a novel CD deconvolution model to determine peptide conformation in solution. To quantitatively obtain secondary structure information using CD, a calibration model is needed beforehand to establish the relationship between each secondary structure feature and the corresponding CD response. A reference set containing the majority of cyclic peptides with known structures from solution-state NMR spectroscopy was used to build the calibration model for CD deconvolution. Improved prediction accuracy on the secondary structure determination for cyclic peptides was achieved by this model compared to the commercial standard model using commercially available platforms. This new CD deconvolution method is crucial for peptide conformational analysis in solution, and has the potential to greatly accelerate peptide drug candidate optimization in the pharmaceutical drug discovery field.

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.

Study on the stressing state features of basalt fibre concrete lining structure under sulphate erosion

Basalt fibre (BF) is an environmentally friendly material with excellent corrosion resistance. When tunnels cross gypsum rock strata, the gypsum rock will dissolve sulphate and erode the supporting structure, thus affecting the safe operation of the tunnels in the long term. Adding BF to the supporting structure of gypsum rock tunnels can improve its resistance to sulphate erosion. This paper took Wuzhishan Tunnel as a case, and first determined the sulphate concentration of the gypsum rock stratigraphic environment at the site; then, analysed the change of mechanical properties of basalt fibre concrete (BFC) before and after sulphate erosion by mechanical tests; finally, examined the deformation and force characteristics of the BFC secondary lining structure by numerical simulation. The results showed that the bridging effect of BF fibres and the stable spatial network structure formed inside the concrete enhanced the mechanical properties of BFC and its ability to resist sulphate erosion. However, due to the agglomeration effect of BF fibres, too much BF would reduce the mechanical properties and erosion resistance of BFC. At a BF content of 6 kg/m³, BFC had the strongest ability to resist sulphate erosion, and the cube compressive strength, axial compressive strength, and modulus of elasticity of BFC were the highest, with 47.8 MPa, 33.6 MPa, and 38.7 GPa, respectively. The dense calcium silicate hydrate (C-S-H) gel, generated near the BF, enhanced the connection between the cementitious and the aggregate, inhibited the development of microcracks within the concrete, and reduced the erosion channels for sulphate ions. The maximum displacement of the BFC secondary lining structure was reduced by 20.15 % compared to plain concrete (PC) after sulphate erosion. The secondary lining structures consisting of both BFC and PC were stressed the most at footing. The secondary lining structure composed of BFC had better deformation resistance and higher strength than PC.

Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model.

Allostery is one of the most direct and efficient ways to regulate protein functions. The diverse allosteric sites make it possible to design allosteric modulators of differential selectivity and improved safety compared with those of orthosteric drugs targeting conserved orthosteric sites. Here, we develop an ensemble machine learning method AllosES to predict protein allosteric sites in which the new and effective features are utilized, including the entropy transfer-based dynamic property, secondary structure features, and our previously proposed spatial neighbor-based evolutionary information besides the traditional physicochemical properties. To overcome the class imbalance problem, the multiple grouping strategy is proposed, which is applied to feature selection and model construction. The ensemble model is constructed where multiple submodels are trained on multiple training subsets, respectively, and their results are then integrated to be the final output. AllosES achieves a prediction performance of 0.556 MCC on the independent test set D24, and additionally, AllosES can rank the real allosteric sites in the top three for 83.3/89.3% of allosteric proteins from the test set D24/D28, outperforming the state-of-the-art peer methods. The comprehensive results demonstrate that AllosES is a promising method for protein allosteric site prediction. The source code is available at https://github.com/ChunhuaLab/AllosES.

Prediction of LncRNA-protein Interactions Using Auto-Encoder, SE-ResNet Models and Transfer Learning.

Long non-coding RNA (lncRNA) plays a crucial role in various biological processes, and mutations or imbalances of lncRNAs can lead to several diseases, including cancer, Prader-Willi syndrome, autism, Alzheimer's disease, cartilage-hair hypoplasia, and hearing loss. Understanding lncRNA-protein interactions (LPIs) is vital for elucidating basic cellular processes, human diseases, viral replication, transcription, and plant pathogen resistance. Despite the development of several LPI calculation methods, predicting LPI remains challenging, with the selection of variables and deep learning structure being the focus of LPI research. We propose a deep learning framework called AR-LPI, which extracts sequence and secondary structure features of proteins and lncRNAs. The framework utilizes an auto-encoder for feature extraction and employs SE-ResNet for prediction. Additionally, we apply transfer learning to the deep neural network SE-ResNet for predicting small-sample datasets. Through comprehensive experimental comparison, we demonstrate that the AR-LPI architecture performs better in LPI prediction. Specifically, the accuracy of AR-LPI increases by 2.86% to 94.52%, while the F-value of AR-LPI increases by 2.71% to 94.73%. Our experimental results show that the overall performance of AR-LPI is better than that of other LPI prediction tools.

Middle Eastern carbonate reservoirs – the critical impact of discrete zones of elevated permeability on reservoir performance

Abstract Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organized DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay and earlier-than-expected water or gas breakthrough to production wells. Optimization of production and ultimate recovery rely on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterization process to permit their inclusion in static and dynamic reservoir models.

Mitochondrial sequencing identifies long noncoding RNA features that promote binding to PNPase.

Extranuclear localization of long noncoding RNAs (lncRNAs) is poorly understood. Based on machine learning evaluations, we propose a lncRNA-mitochondrial interaction pathway where polynucleotide phosphorylase (PNPase), through domains that provide specificity for primary sequence and secondary structure, binds nuclear-encoded lncRNAs to facilitate mitochondrial import. Using FVB/NJ mouse and human cardiac tissues, RNA from isolated subcellular compartments (cytoplasmic and mitochondrial) and cross-linked immunoprecipitate (CLIP) with PNPase within the mitochondrion were sequenced on the Illumina HiSeq and MiSeq, respectively. lncRNA sequence and structure were evaluated through supervised [classification and regression trees (CART) and support vector machines (SVM)] machine learning algorithms. In HL-1 cells, quantitative PCR of PNPase CLIP knockout mutants (KH and S1) was performed. In vitro fluorescence assays assessed PNPase RNA binding capacity and verified with PNPase CLIP. One hundred twelve (mouse) and 1,548 (human) lncRNAs were identified in the mitochondrion with Malat1 being the most abundant. Most noncoding RNAs binding PNPase were lncRNAs, including Malat1. lncRNA fragments bound to PNPase compared against randomly generated sequences of similar length showed stratification with SVM and CART algorithms. The lncRNAs bound to PNPase were used to create a criterion for binding, with experimental validation revealing increased binding affinity of RNA designed to bind PNPase compared to control RNA. The binding of lncRNAs to PNPase was decreased through the knockout of RNA binding domains KH and S1. In conclusion, sequence and secondary structural features identified by machine learning enhance the likelihood of nuclear-encoded lncRNAs binding to PNPase and undergoing import into the mitochondrion.NEW & NOTEWORTHY Long noncoding RNAs (lncRNAs) are relatively novel RNAs with increasingly prominent roles in regulating genetic expression, mainly in the nucleus but more recently in regions such as the mitochondrion. This study explores how lncRNAs interact with polynucleotide phosphorylase (PNPase), a protein that regulates RNA import into the mitochondrion. Machine learning identified several RNA structural features that improved lncRNA binding to PNPase, which may be useful in targeting RNA therapeutics to the mitochondrion.

Predicting transcriptional activation domain function using Graph Neural Networks.

Analysis of factors that lead to the functionality of transcriptional activation domains remains a crucial and yet challenging task owing to the significant diversity in their sequences and their intrinsically disordered nature. Almost all existing methods that have aimed to predict activation domains have involved traditional machine learning approaches, such as logistic regression, that are unable to capture complex patterns in data or plain convolutional neural networks and have been limited in exploration of structural features. However, there is a tremendous potential in the inspection of the structural properties of activation domains, and an opportunity to investigate complex relationships between features of residues in the sequence. To address these, we have utilized the power of graph neural networks which can represent structural data in the form of nodes and edges, allowing nodes to exchange information among themselves. We have experimented with two kinds of graph formulations, one involving residues as nodes and the other assigning atoms to be the nodes. A logistic regression model was also developed to analyze feature importance. For all the models, several feature combinations were experimented with. The residue-level GNN model with amino acid type, residue position, acidic/basic/aromatic property and secondary structure feature combination gave the best performing model with accuracy, F1 score and AUROC of 97.9%, 71% and 97.1% respectively which outperformed other existing methods in the literature when applied on the dataset we used. Among the other structure-based features that were analyzed, the amphipathic property of helices also proved to be an important feature for classification. Logistic regression results showed that the most dominant feature that makes a sequence functional is the frequency of different types of amino acids in the sequence. Our results consistent have shown that functional sequences have more acidic and aromatic residues whereas basic residues are seen more in non-functional sequences.

Characterization of the complete mitochondrial genome of Desmaulus extinctorium (Littorinimorpha, Calyptraeoidea, Calyptraeidae) and molecular phylogeny of Littorinimorpha.

For the purpose of determining the placement of Calyptraeidae within the Littorinimorpha, we hereby furnish a thorough analysis of the mitochondrial genome (mitogenome) sequence of Desmaulus extinctorium. This mitogenome spans 16,605 base pairs and encompasses the entire set of 37 genes, including 13 PCGs, 22 tRNAs and two rRNAs, with an evident AT bias. Notably, tRNASer1 and tRNASer2 lack dihydrouracil (DHU) arms, resulting in an inability to form a secondary structure. Similarly, tRNAAla lacks a TΨC arm, rendering it incapable of forming a secondary structure. In contrast, the remaining tRNAs demonstrate a characteristic secondary structure reminiscent of a cloverleaf. A comparison with ancestral gastropods reveals distinct differences in three gene clusters (or genes), encompassing 15 tRNAs and eight PCGs. Notably, inversions and translocations represent the major types of rearrangements observed in D. extinctorium. Phylogenetic analysis demonstrates robust support for a monophyletic grouping of all Littorinimorpha species, with D. extinctorium representing a distinct Calyptraeoidea clade. In summary, this investigation provides the first complete mitochondrial dataset for a species of the Calyptraeidae, thus providing novel insights into the phylogenetic relationships within the Littorinimorpha.

DeepSS2GO: protein function prediction from secondary structure.

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

Enrofloxacin Rapid Detection in Aquatic Foods: Based on DNA Aptamer Sensor.

Enrofloxacin (ENR) is widely used as a synthetic fluoroquinolone antibiotic for disease control in aquatic animals. ENR aptamers were screened in this study using the magnetic bead-SELEX method, and a graphene oxide fluorescent sensor was developed to detect the ENR residues in aquatic products. Firstly, ENR was conjugated to amino magnetic beads by amidation reaction, and then the aptamer sequences showing high affinity to ENR were screened step by step by using the SELEX screening method. Finally, after 10 rounds of SELEX screening, six candidate aptamers with high affinity were obtained. Among these, ENR-Apt 6 was selected based on its secondary structure features, high affinity (Kd = 35.08 nM), and high specificity to ENR. Furthermore, a fluorescent sensor was prepared using graphene oxide and ENR-Apt 6. The results showed that the linear range of the sensor could reach 600 nM (R2 = 0.986), while its optimal linear range was 1-400 nM (R2 = 0.991), with the lowest detection limit of 14.72 nM. The prepared sensor was successfully used for the detection of ENR in real samples, with a recovery range of 83.676-114.992% and a relative standard deviation < 10% for most of the samples.

Predicting lncRNA–protein interactions through deep learning framework employing multiple features and random forest algorithm

RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein–protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

Structural and Functional Analysis of Groundnut bud necrosis virus (GBNV) Using Computational and Biochemical Approaches.

Groundnut bud necrosis virus (GBNV) belonging to the genus Orthotospovirus is transmitted by its vector Thrips palmi. It is a tri-segmented RNA virus that consists of L, M, and S RNA segments. We analysed the secondary structure features of GBNV proteins through various software and predicted the transmembrane helix, glycosylation, and signal peptidase sites within the GBNV protein sequences (GN, GC, N, NSm, and NSs). In glycoprotein sequence, extended strands are predominant (52.87%) whereas the N protein sequence mostly contains alpha helices (47.46%). The random coils are present in movement protein (43.97%) and structural protein (39.41%). We generated the 3D structure of GN and N protein using SWISS MODEL software and quality is validated through PROCHECK and PDBsum software. We also expressed the GBNV proteins (GN, GC, N, NSm, and NSs) in bacterial expression system. The recombinant proteins were used to raise polyclonal antibodies in mice. Our study will be useful in understanding GBNV protein structures in further detail by analysing the important domains that interact with the thrips proteins. This will further aid us in understanding virus-vector relationship through the application of protein-protein interaction and other immunodiagnostic techniques.

Characterizing the West Nile Virus's polyprotein from nucleotide sequence to protein structure – Computational tools

ObjectivesWest Nile virus (WNV) belongs to the Flaviviridae family and causes West Nile fever. The mechanism of transmission involves the culex mosquito species. Infected individuals are primarily asymptomatic, and few exhibit common symptoms. Moreover, 10 % of neuronal infection caused by this virus cause death. The proteins encoded by these genes had been uncharacterized, although understanding their function and structure is important for formulating antiviral drugs. MethodsHerein, we used in silico approaches, including various bioinformatic tools and databases, to analyse the proteins from the WNV polyprotein individually. The characterization included GC content, physicochemical properties, conserved domains, soluble and transmembrane regions, signal localization, protein disorder, and secondary structure features and their respective 3D protein structures. ResultsAmong 11 proteins, eight had >50 % GC content, eight proteins had basic pI values, three proteins were unstable under in vitro conditions, four were thermostable according to >100 AI values and some had negative GRAVY values in physicochemical analyses. All protein-conserved domains were shared among Flaviviridae family members. Five proteins were soluble and lacked transmembrane regions. Two proteins had signals for localization in the host endoplasmic reticulum. Non-structural (NS) 2A showed low protein disorder. The secondary structural features and tertiary structure models provide a valuable biochemical resource for designing selective substrates and synthetic inhibitors. ConclusionsWNV proteins NS2A, NS2B, PM, NS3 and NS5 can be used as drug targets for the pharmacological design of lead antiviral compounds.

Suitability of Adenosine Derivatives in Improving the Activity and Stability of Cytochrome c under Stress: Insights into the Effect of Phosphate Groups.

It is well known that adenosine and its phosphate derivatives play a crucial role in biological phenomena such as apoptosis and cell signaling and act as the energy currency of the cell. Although their interactions with various proteins and enzymes have been described, the focus of this work is to demonstrate the effect of the phosphate group on the activity and stability of the native heme metalloprotein cytochrome c (Cyt c), which is important from both biological and industrial aspects. In situ and in silico characterizations are used to correlate the relationship between the binding affinity of adenosine and its phosphate groups with unfolding behavior, corresponding peroxidase activities, and stability factors. Interaction of adenosine (ADN), adenosine monophosphate (AMP), adenosine 5'-diphosphate (ADP), and adenosine 5'-triphosphate (ATP) with Cyt c increases peroxidase-like activity by up to 1.8-6.5-fold compared to native Cyt c. This activity is significantly maintained even after multiple stress conditions such as oxidative stress and the presence of a chaotropic agent such as guanidine hydrochloride (GuHCl). With binding affinities on the order of ADN < AMP < ADP < ATP, adenosine derivatives were found to stabilize Cyt c by varying the secondary structural features of the protein. Thus, in addition to being a fundamental study, the current work also proposes a way of stabilizing protein systems to be used for real-time biocatalytic applications.