Three-dimensional Protein Research Articles

Assessing Protein Surface-Based Scoring for Interpreting Genomic Variants

Clinical genomics sequencing is rapidly expanding the number of variants that need to be functionally elucidated. Interpreting genetic variants (i.e., mutations) usually begins by identifying how they affect protein-coding sequences. Still, the three-dimensional (3D) protein molecule is rarely considered for large-scale variant analysis, nor in analyses of how proteins interact with each other and their environment. We propose a standardized approach to scoring protein surface property changes as a new dimension for functionally and mechanistically interpreting genomic variants. Further, it directs hypothesis generation for functional genomics research to learn more about the encoded protein’s function. We developed a novel method leveraging 3D structures and time-dependent simulations to score and statistically evaluate protein surface property changes. We evaluated positive controls composed of eight thermophilic versus mesophilic orthologs and variants that experimentally change the protein’s solubility, which all showed large and statistically significant differences in charge distribution (p < 0.01). We scored static 3D structures and dynamic ensembles for 43 independent variants (23 pathogenic and 20 uninterpreted) across four proteins. Focusing on the potassium ion channel, KCNK9, the average local surface potential shifts were 0.41 kBT/ec with an average p-value of 1 × 10−2. In contrast, dynamic ensemble shifts averaged 1.15 kBT/ec with an average p-value of 1 × 10−5, enabling the identification of changes far from mutated sites. This study demonstrates that an objective assessment of how mutations affect electrostatic distributions of protein surfaces can aid in interpreting genomic variants discovered through clinical genomic sequencing.

Quasi-two-dimensional dispersions of Brownian particles with competitive interactions: phase behavior and structural properties.

Competing short-range attractive (SA) and long range repulsive (LR) particle interactions can be used to describe three-dimensional charge-stabilized colloid or protein dispersions at low added salt concentrations, as well as membrane proteins with interaction contributions mediated by lipid molecules. Using Langevin dynamics (LD) simulations, we determine the generalized phase diagram, cluster shapes and size distributions of a generic quasi-two-dimensional (Q2D) dispersion of spherical SALR particles confined to in-plane motion inside a bulk fluid. The SA and LR interaction parts are modelled by a generalized Lennard-Jones potential and a screened Coulomb potential, respectively. The microstructures of the detected equilibrium and non-equilibrium Q2D phases are distinctly different from those observed in three-dimensional (3D) SALR systems, by exhibiting different levels of hexagonal ordering. We discuss a thermodynamic perturbation theory prediction for the metastable binodal line of a reference system of particles with SA interactions only, which in the explored Q2D-SALR phase diagram region separates cluster from non-clustered phases. The transition from the high-temperature (small SA) dispersed fluid (DF) phase to the lower-temperature equilibrium cluster (EC) fluid phase is characterised by a low-wavenumber peak height of the static structure factor (corresponding to a thermal correlation length of about twice the particle diameter) featuring a distinctly smaller value (≈1.4) than in 3D SALR systems. With decreasing temperature (increasing SA), the cluster morphology changes from disk-like shapes in the equilibrium cluster phase, to double-stranded anisotropic hexagonal cluster segments formed in a cluster-percolated (CP) gel-like phase. This transition can be quantified by a hexagonal order parameter distribution function. The mean cluster size and coordination number of particles in the CP phase are insensitive to changes in the attraction strength.

Characterization of the immunodominant regions of Senecavirus A-VP1 structural protein via ELISA epitope mapping

Senecavirus A (SVA) is an RNA virus in the family Picornaviridae that has been detected in swine-production systems and is associated with vesicular disease and neonate mortality. The viral capsid is composed of four structural proteins: VP1–VP4. Although the VP1 protein has been reported to be the most immunogenic protein in vivo, no information on the immunodominant regions of the SVA polyprotein is available. The objective of this study was to identify the immunodominant regions of SVA polyprotein using an enzyme-linked immunosorbent assay (ELISA) epitope-mapping approach. The binding effect of SVA polyclonal antibody (SVA-pAb), SVA-VP1 monoclonal antibodies (SVA-mAb), and SVA-positive sera from clinically affected animals were characterized using a set of 18 overlapping SVA VP1-derived peptides by indirect and blocking ELISAs. All VP1 peptides yielded significant signal against SVA-pAb and SVA-VP1-mAb upon indirect ELISA. One peptide (aa 1–20) showed significantly high optical density on SVA recombinant VP1 protein (rVP1) and whole-virus-based indirect ELISAs. The blocking ELISA results demonstrated that peptides spanning aa 165–185 and 225–245 had a 50 % or greater inhibitory effect on SVA-pAb, while six groups of overlapping peptides spanning aa 1–35, 45–80, 90–140, 150–170, 195–230, and 240–264 and two groups of overlapping peptides spanning aa 1–50 and 60–264 showed a 50 % inhibitory effect or greater on swine VP1-mAb and SVA-seropositive swine serum, respectively, against SVA rVP1. Three-dimensional protein homology modeling showed that the peptides binding SVA-pAb are located on the outer surface of the viral capsid, while SVA mAbs and swine-positive sere can bind to epitopes located in both the inner and outer surfaces of the capsid. These linear epitopes showed differential binding and inhibitory activity on mAb and pAb; however, further studies will be necessary to evaluate whether they can act as decoy or neutralizing epitopes. Because mAb antibodies demonstrated a high binding affinity for this set of peptides, this information could lay the foundation for generating and screening specific antibodies for therapeutic potential.

1.3 Å Crystal Structure of E. coli Peptidyl-Prolyl Isomerase B with Uniform Substitution of Valine by (2S,3S)-4-Fluorovaline Reveals Structure Conservation and Multiple Staggered Rotamers of CH2F Groups.

(2S,3S)-4-Fluorovaline (FVal) is an analogue of valine, where a single CH3 group is substituted by a CH2F group. In the absence of valine, E. coli valyl-tRNA synthetase uses FVal as a substitute, enabling the production of proteins uniformly labeled with FVal. Here, we describe the production and analysis of E. coli peptidyl-prolyl isomerase B where all 16 valine residues have been replaced by FVal synthesized with a 13C-labeled CH2F group. Although the melting temperature is lower by about 11 °C relative to the wild-type protein, the three-dimensional protein structure is almost completely conserved, as shown by X-ray crystallography. The CH2F groups invariably populate staggered rotamers. Most CH2F groups populate two different rotamers. The increased space requirement of fluorine versus hydrogen does not prohibit rotamers that position fluorine next to a backbone carbonyl carbon. 19F NMR spectra show a signal dispersion over 25 ppm. The most high-field shifted 19F resonances correlate with large 3JHF coupling constants, confirming the impact of the γ-gauche effect on the signal dispersion. The present work is the second experimental verification of the effect and extends its validity to fluorovaline. The abundance of valine in proteins and structural conservation with FVal renders this valine analogue attractive for probing proteins by 19F NMR spectroscopy.

Interactions of Potential Endocrine-Disrupting Chemicals with Whole Human Proteome Predicted by AlphaFold2 Using an In Silico Approach.

Binding with proteins is a critical molecular initiating event through which environmental pollutants exert toxic effects in humans. Previous studies have been limited by the availability of three-dimensional (3D) protein structures and have focused on only a small set of environmental contaminants. Using the highly accurate 3D protein structure predicted by AlphaFold2, this study explored over 60 million interactions obtained through molecular docking between 20,503 human proteins and 1251 potential endocrine-disrupting chemicals. A total of 66,613,773 docking results were obtained, 1.2% of which were considered to be high binding, as their docking scores were lower than -7. Monocyte to macrophage differentiation factor 2 (MMD2) was predicted to interact with the highest number of environmental pollutants (526), with polychlorinated biphenyls and polychlorinated dibenzofurans accounting for a significant proportion. Dimension reduction and clustering analysis revealed distinct protein profiles characterized by high binding affinities for perfluoroalkyl and polyfluoroalkyl substances (PFAS), phthalate-like chemicals, and other pollutants, consistent with their uniquely enriched pathways. Further structural analysis indicated that binding pockets with a high proportion of charged amino acid residues, relatively low α-helix content, and high β-sheet content were more likely to bind to PFAS than others. This study provides insights into the toxicity pathways of various pollutants impacting human health and offers novel perspectives for the establishment and expansion of adverse outcome pathway-based models.

Clinical and genetic characterization of three families with Nance-Horan syndrome caused by NHS gene mutations

Objective: To explore the clinical phenotypes and pathogenic gene variation characteristics of three Chinese Han ethnic families affected by Nance-Horan syndrome, a rare X-linked genetic disorder. Methods: A pedigree investigation study was conducted at the First Affiliated Hospital of Zhengzhou University, collecting clinical data from three Chinese Han families with Nance-Horan syndrome between February 2009 and September 2018. Detailed family histories, comprehensive ophthalmological and systemic examinations were documented. Pedigree charts were created, and genetic inheritance patterns were analyzed to preliminarily diagnose the probands and other affected individuals. Genomic DNA was extracted from peripheral blood samples of family members, and next-generation sequencing was used to screen for target gene variations, which were confirmed by Sanger sequencing. Pathogenicity of the genetic variants and their impact on three-dimensional protein structure were analyzed using MutationTaster and computer-aided protein modeling. Results: In Family 1, there are 5 patients, including 4 females (aged 42, 37, 9 and 7) and 1 males (aged 12). In Family 2, there are 5 patients, including 3 females (aged 54, 32 and 16) and 2 males (aged 26 and 9). In Family 3, there are 8 patients, including 5 females (aged 69, 42, 37, 35 and 14) and 3 males (aged 10, 7 and 4). All probands in the three families exhibited nuclear cataracts with typical congenital hereditary cataract features, but no noticeable abnormalities in facial appearance or teeth. Next-generation sequencing identified new variation sites in the NHS gene, specifically c.2519_2520del, exon3del, and c.3847C>T. These variations included nonsense mutation p.(Ser840*), exon deletion p.(?), and nonsense mutation p.(Gln1283*). Combined clinical and genetic sequencing results confirmed X-linked Nance-Horan syndrome in all three families. Bioinformatics analysis indicated these variation sites were pathogenic and resulted in abnormal three-dimensional protein structures, likely being the main cause of Nance-Horan syndrome. Conclusion: The majority of patients from the three Nance-Horan syndrome families studied were affected by congenital hereditary cataracts characterized by nuclear opacities.The NHS gene variations c.2519_2520del, exon3del, and c.3847C>T are newly identified pathogenic sites in Nance-Horan syndrome, reported for the first time across three different families.

Identification of STAM-binding protein as a target for the treatment of gemcitabine resistance pancreatic cancer in a nutrient-poor microenvironment

Pancreatic cancer (PC) is a highly malignant solid tumor whose resistance to gemcitabine (GEM) chemotherapy is a major cause of poor patient prognosis. Although PC is known to thrive on malnutrition, the mechanism underlying its chemotherapy resistance remains unclear. The current study analyzed clinical tissue sample databases using bioinformatics tools and observed significantly upregulated expression of the deubiquitinase STAMBP in PC tissues. Functional experiments revealed that STAMBP knockdown remarkably increases GEM sensitivity in PC cells. Multiple omics analyses suggested that STAMBP enhances aerobic glycolysis and suppresses mitochondrial respiration to increase GEM resistance in PC both in vitro and in vivo. STAMBP knockdown decreased PDK1 levels, an essential regulator of the aerobic glycolytic process, in several cancers. Mechanistically, STAMBP promoted the PDK1-mediated Warburg effect and chemotherapy resistance by modulating E2F1 via direct binding to E2F1 and suppressing its degradation and ubiquitination. High-throughput compound library screening using three-dimensional protein structure analysis and drug screening identified the FDA drug entrectinib as a potent GEM sensitizer and STAMBP inhibitor, augmenting the antitumor effect of GEM in a patient-derived xenograft (PDX) model. Overall, we established a novel mechanism, via the STAMBP–E2F1–PDK1 axis, by which PC cells become chemoresistant in a nutrient-poor tumor microenvironment.

A genetic investigation in five Chinese families with keratoconus.

This study investigated the genetic characteristics of five Chinese families with keratoconus (KC). In the five families affected by KC, medical records, clinical observations, and blood samples were collected from all individuals. All KC family members (n = 20) underwent both whole exome sequencing of genomic DNA and Sanger sequencing to confirm the variants. Online software was utilized to analyze all variants, and the online server I-TASSER was employed for in silico predictions of the three-dimensional protein structures of the variants. The newly discovered variants and single nucleotide polymorphisms were further examined in 322 sporadic KC patients. The Pentacam tomographic composite index in those affected first-degree family members of the probands showed a pathological change. Five new variants were detected in the five probands and other affected members in their families: a heterozygous missense variant g.19043832C>T (p.Ser145Asn) in the homer scaffolding protein 3 (HOMER3) gene; a heterozygous missense variant g.99452113G>A (p.Gly483Arg) in the insulin-like growth factor 1 receptor (IGF1R) gene; a heterozygous missense variant g.55118280G>T (p.Trp843Leu) in the echinoderm microtubule-associated protein like 6 (EML6) gene; a heterozygous frameshift variant c. 1226_1227del (p.Gln410Glufs*17) in the DOP1 leucine zipper-like protein B (DOP1B) gene; and a heterozygous splice-site variant c.7776+2T>A in the neurobeachin-like protein 2 (NBEAL2) gene. These variations were predicted to be potentially pathogenic and associated with KC. Five novel variants in HOMER3, IGF1R, EML6, DOP1B, and NBEAL2 genes were identified in this study and may be associated with the pathogenesis of KC. This study provides new information about the gene variants and their protein changes in KC patients. The findings should be explored further and could potentially be applied to the early diagnosis of KC before clinical onset.

Automated Assignment of 15N And 13C Enrichment Levels in Doubly-Labeled Proteins.

Uniform enrichment of 15N and 13C in proteins is commonly employed for 2D heteronuclear NMR measurements of the three-dimensional protein structure. Achieving a high degree of enrichment of both elements is important for obtaining high quality data. Uniform labeling of proteins and glycoproteins expressed in higher organisms (yeast or mammalian cell lines) is more challenging than expression in Escherichia coli, a prokaryote that grows on simple, chemically defined media but does not provide appropriate eukaryotic modifications. It is difficult to achieve complete incorporation of both heavy isotopes, and quality control measures are important for quantitating the level of their enrichment. Mass spectrometry measurements of the isotopic distribution of the intact protein or its proteolytic fragments provide the means to assess the enrichment level. A mass accuracy of 1 ppm or better is shown to be required to distinguish the correct combination of 13C and 15N enrichment due to subtle shifts in peak centroids with differences in the underlying, but unresolved, isotopic fine structure. A simple computer program was developed to optimize the fitting of experimental isotope patterns to statistically derived distributions. This method can determine the isotopic abundance from isotope patterns and isotopologue masses in conventional MS data for peptides, intact proteins, and glycans. For this purpose, MATLAB's isotope simulator, isotopicdist, has been modified to permit the variation of 15N and 13C enrichment levels and to perform a two-dimensional grid search of enrichment levels of both isotopes. We also incorporated an alternate isotope simulator, js-emass, into MATLAB for use in the same fitting program. Herein we benchmark this technique on natural abundance ubiquitin and uniformly [15N,13C]-labeled ubiquitin at both the intact and peptide level, outline considerations for data quality and mass accuracy, and report several improvements we have made to the previously reported analysis of the [15N,13C]-enriched human IgG Fc domain, a glycoprotein that has been expressed in Saccharomyces cerevisiae.

Clinical and genomic diversity of Treponema pallidum subspecies pallidum to inform vaccine research: an international, molecular epidemiology study

The increase in syphilis rates worldwide necessitates development of a vaccine with global efficacy. Weaimed to explore Treponema pallidum subspecies pallidum (TPA) molecular epidemiology essential for vaccine research by analysing clinical data and specimens from early syphilis patients using whole-genome sequencing (WGS) and publicly available WGS data. In this multicentre, cross-sectional, molecular epidemiology study, we enrolled patients with primary, secondary, or early latent syphilis from clinics in China, Colombia, Malawi, and the USA between Nov 28, 2019, and May 27, 2022. Participants aged 18years or older with laboratory confirmation of syphilis by direct detection methods or serological testing, or both, were included. Patients were excluded from enrolment if they were unwilling or unable to give informed consent, did not understand the study purpose or nature of their participation, or received antibiotics active against syphilis in the past 30days. TPA detection and WGS were conducted on lesion swabs, skin biopsies, skin scrapings, whole blood, or rabbit-passaged isolates. We compared our WGS data to publicly available genomes and analysed TPA populations to identify mutations associated with lineage and geography. We screened 2802patients and enrolled 233participants, of whom 77 (33%) had primary syphilis, 154 (66%) had secondary syphilis, and two (1%) had early latent syphilis. The median age of participants was 28years (IQR22-35); 154 (66%) participants were cisgender men, 77 (33%) were cisgender women, and two (1%) were transgender women. Of the cisgender men, 66 (43%) identified as gay, bisexual, or other sexuality. Among all participants, 56(24%) had HIV co-infection. WGS data from 113participants showed a predominance of SS14-lineage strains with geographical clustering. Phylogenomic analyses confirmed that Nichols-lineage strains were more genetically diverse than SS14-lineage strains and clustered into more distinct subclades. Differences in single nucleotide variants (SNVs) were evident by TPA lineage and geography. Mapping of highly differentiated SNVs to three-dimensional protein models showed population-specific substitutions, some in outer membrane proteins (OMPs) of interest. Our study substantiates the global diversity of TPA strains. Additional analyses to explore TPA OMP variability within strains is vital for vaccine development and understanding syphilis pathogenesis on a population level. US National Institutes of Health National Institute for Allergy and Infectious Disease, the Bill & Melinda Gates Foundation, Connecticut Children's, and the Czech Republic National Institute of Virology and Bacteriology.

The Unusual Functional Role of Protein Flexibility in Photosynthetic Light Harvesting: Protein Dynamics Studied Using Neutron Scattering

In addition to investigations of the three-dimensional protein structure, information on the dynamical properties of proteins is indispensable for an understanding of protein function in general. Correlations between protein dynamics and function are typically anticipated when both molecular mobility and function are concurrently affected under specific temperatures or hydration conditions. In contrast, excitation energy transfer within the major photosynthetic light-harvesting complex II (LHC II) presents an atypical case, as it remains fully operational even at cryogenic temperatures, primarily depending on the interactions between electronic states and involving harmonic protein vibrations only. This review summarizes recent work on vibrational and conformational protein dynamics of LHC II and directly relates these findings to its light-harvesting function. In addition, we give a comprehensive introduction into the use of neutron spectroscopy and molecular dynamics simulations to investigate the protein dynamics of photosynthetic protein complexes in solution, which is information complementary to that obtained by protein crystallography.

Genome-wide search and gene expression studies reveal candidate effectors with a role in pathogenicity and virulence in Fusarium graminearum

ABSTRACT Fusarium graminearum causes Fusarium head blight (FHB) disease in wheat worldwide. Although F. graminearum is reported to secrete several effectors, their role in virulence and pathogenicity is unknown. The study aimed at identifying candidate genes with a role in pathogenicity and virulence using two different host systems, Arabidopsis thaliana and wheat, challenged with F. graminearum TN01. Detached leaf assay and histological studies revealed the virulent nature of TN01. A genome-wide in silico search revealed several candidate genes, of which 23 genes were selected based on reproducibility. Gene expression studies by reverse transcriptase–polymerase chain reaction (RT-PCR) in leaf tissues of Arabidopsis and the two wheat genotypes, the susceptible (Sonalika) and the resistant (Nobeoka Bozu/Nobeoka), compared with mock-treated controls in a time-course study using fungal- and plant-specific genes as internal controls revealed that these genes were differentially regulated. Further, expression of these candidates in F. graminearum–inoculated Sonalika and Nobeoka spikes compared with mock-treated controls revealed their role in pathogenicity and virulence. Gene ontology studies revealed that some of these secretory proteins possessed a role in apoptosis and ceratoplatanin and KP4 killer toxin syntheses. A three-dimensional protein configuration was performed by homology modeling using trRosetta. Further, real-time quantitative PCR (RT-qPCR) studies in F. graminearum–inoculated Arabidopsis and wheat at early time points of inoculation revealed an increased expression of the majority of these genes in Sonalika, suggesting their possible role in pathogenicity, whereas low mRNA abundance was observed for 11 of these genes in the resistant genotype, Nobeoka, compared with Sonalika, indicating their role in virulence of F. graminearum.

Machine Learning Using Template-Based-Predicted Structure of Haemagglutinin Predicts Pathogenicity of Avian Influenza.

Deep learning presents a promising approach to complex biological classifications, contingent upon the availability of well-curated datasets. This study addresses the challenge of analyzing three-dimensional protein structures by introducing a novel pipeline that utilizes open-source tools to convert protein structures into a format amenable to computational analysis. Applying a two-dimensional convolutional neural network (CNN) to a dataset of 12,143 avian influenza virus genomes from 64 countries, encompassing 119 hemagglutinin (HA) and neuraminidase (NA) types, we achieved significant classification accuracy. The pathogenicity was determined based on the presence of H5 or H7 subtypes, and our models, ranging from zero to six mid-layers, indicated that a four-layer model most effectively identified highly pathogenic strains, with accuracies over 0.9. To enhance our approach, we incorporated Principal Component Analysis (PCA) for dimensionality reduction and one-class SVM for abnormality detection, improving model robustness through bootstrapping. Furthermore, the K-nearest neighbor (K-NN) algorithm was fine-tuned via hyperparameter optimization to corroborate the findings. The PCA identified distinct clustering for pathogenic HA, yielding an AUC of up to 0.85. The optimized K-NN model demonstrated an impressive accuracy between 0.96 and 0.97. These combined methodologies underscore our deep learning framework's capacity for rapid and precise identification of pathogenic avian influenza strains, thus providing a critical tool for managing global avian influenza threats.

Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches

Abstract: Computer-Aided Drug Design (CADD) has significantly advanced the drug discovery process, offering tools to enhance efficiency and reduce costs. This review explores essential CADD methodologies, including molecular docking, virtual screening, ADMET profiling, homology modeling, and Quantitative Structure-Activity Relationship (QSAR) models. Molecular docking predicts interactions between drugs and targets, while virtual screening evaluates large compound libraries to identify promising candidates. ADMET profiling assesses pharmacokinetic and toxicological properties early in development. Homology modeling constructs three-dimensional protein models to aid target identification, and QSAR models predict biological activities based on chemical structures. These integrated approaches streamline drug development, providing a robust framework for modern pharmaceutical research.

Genetic variations and gene expression profiles of Rice Black-streaked dwarf virus (RBSDV) in different host plants and insect vectors: insights from RNA-Seq analysis

Rice black-streaked dwarf virus (RBSDV) is an etiological agent of a destructive disease infecting some economically important crops from the Gramineae family in Asia. While RBSDV causes high yield losses, genetic characteristics of replicative viral populations have not been investigated within different host plants and insect vectors. Herein, eleven publicly available RNA-Seq datasets from Chinese RBSDV-infected rice, maize, and viruliferous planthopper (Laodelphax striatellus) were obtained from the NCBI database. The patterns of SNP and RNA expression profiles of expected RBSDV populations were analyzed by CLC Workbench 20 and Geneious Prime software. These analyses discovered 2,646 mutations with codon changes in RBSDV whole transcriptome and forty-seven co-mutated hotspots with high variant frequency within the crucial regions of S5-1, S5-2, S6, S7-1, S7-2, S9, and S10 open reading frames (ORFs) which are responsible for some virulence and host range functions. Moreover, three joint mutations are located on the three-dimensional protein of P9-1. The infected RBSDV-susceptible rice cultivar KTWYJ3 and indigenous planthopper datasets showed more co-mutated hotspot numbers than others. Our analyses showed the expression patterns of viral genomic fragments varied depending on the host type. Unlike planthopper, S5-1, S2, S6, and S9-1 ORFs, respectively had the greatest read numbers in host plants; and S5-2, S9-2, and S7-2 were expressed in the lowest level. These findings underscore virus/host complexes are effective in the genetic variations and gene expression profiles of plant viruses. Our analysis revealed no evidence of recombination events. Interestingly, the negative selection was observed at 12 RBSDV ORFs, except for position 1015 in the P1 protein, where a positive selection was detected. The research highlights the potential of SRA datasets for analysis of the virus cycle and enhances our understanding of RBSDV’s genetic diversity and host specificity.

In silico homology modeling of dengue virus non-structural 4B (NS4B) protein and its molecular docking studies using triterpenoids

BackgroundDengue fever has become a significant worldwide health concern, because of its high morbidity rate and the potential for an increase in mortality rates due to lack of adequate treatment. There is an immediate need for the development of effective medication for dengue fever.MethodsHomology modeling of dengue virus (DENV) non-structural 4B (NS4B) protein was performed by SWISS-MODEL to predict the 3D structure of the protein. Structure validation was conducted using PROSA, PROCHECK, Ramachandran plot, and VERIFY-3D. MOE software was used to find out the in-Silico inhibitory potential of the five triterpenoids against the DENV-NS4B protein.ResultsThe SWISS-MODEL was employed to predict the three-dimensional protein structure of the NS4B protein. Through molecular docking, it was found that the chosen triterpenoid NS4B protein had a high binding affinity interaction. It was observed that the NS4B protein binding energy for 15-oxoursolic acid, betulinic acid, ursolic acid, lupeol, and 3-o-acetylursolic acid were − 7.18, − 7.02, − 5.71, − 6.67 and − 8.00 kcal/mol, respectively.ConclusionsNS4B protein could be a promising target which showed good interaction with tested triterpenoids which can be developed as a potential antiviral drug for controlling dengue virus pathogenesis by inhibiting viral replication. However, further investigations are necessary to validate and confirm their efficacy.

Insights into structural vaccinology harnessed for universal coronavirus vaccine development.

Structural vaccinology is pivotal in expediting vaccine design through high-throughput screening of immunogenic antigens. Leveraging the structural and functional characteristics of antigens and immune cell receptors, this approach employs protein structural comparison to identify conserved patterns in key pathogenic components. Molecular modeling techniques, including homology modeling and molecular docking, analyze specific three-dimensional (3D) structures and protein interactions and offer valuable insights into the 3D interactions and binding affinity between vaccine candidates and target proteins. In this review, we delve into the utilization of various immunoinformatics and molecular modeling tools to streamline the development of broad-protective vaccines against coronavirus disease 2019 variants. Structural vaccinology significantly enhances our understanding of molecular interactions between hosts and pathogens. By accelerating the pace of developing effective and targeted vaccines, particularly against the rapidly mutating severe acute respiratory syndrome coronavirus 2 and other prevalent infectious diseases, this approach stands at the forefront of advancing immunization strategies. The combination of computational techniques and structural insights not only facilitates the identification of potential vaccine candidates but also contributes to the rational design of vaccines, fostering a more efficient and targeted approach to combatting infectious diseases.

The TIR1/AFB Family in Solanum melongena: Genome-Wide Identification and Expression Profiling under Stresses and Picloram Treatment

TIR1/AFB proteins are a class of auxin receptors with key roles in plant development and biotic and abiotic stress responses; several have been identified as targets of the auxin-mimicking herbicide picloram. In this study, we identified five putative TIR1/AFB gene family members in the important vegetable crop Solanum melongena (eggplant) and characterized them using bioinformatics tools and gene expression analyses. Phylogenetic analysis of the TIR1/AFBs classified them into three subgroups based on their Arabidopsis and Solanum lycopersicum homologs. AFB6 homologs were present only in S. melongena and S. lycopersicum, whereas AFB2/3 homologs were found only in Arabidopsis. One pair of S. melongena TIR1 homologs were located in syntenic regions in the genome and appeared to have arisen by segmental duplication. Promoter analysis revealed 898 cis-elements in the TIR1/AFB promoters, 125 of which were related to hormones, stress, light, or growth responses, but only SmAFB5 had a cis-acting regulatory element involved in auxin responsiveness (AuxRR-core). RNA sequencing and expression profiling showed that the TIR1/AFB genes were differentially expressed at different growth stages and in response to light, temperature, and drought. Only SmTIR1A expression was significantly induced by picloram treatment and different growth stages. TIR1/AFB expression is regulated by microRNAs (miRNAs) in other plant species, and we identified 6 or 29 miRNAs that potentially targeted the five TIR1/AFB genes on the basis of comparisons with S. lycopersicum and S. tuberosum miRNAs, respectively. Three-dimensional protein structure predictions revealed that all the TIR1/AFB proteins were very similar in structure, differing only in the numbers of alpha helices and in one angle linking an α helix and a β sheet. For measuring the function of TIR1/AFB genes in response to drought, SmAFB5 was selected, and knockdown by virus-induced gene silence (VIGS) 35S::SmAFB5 lines showed resistance to drought compared to controls. These analyses provide insight into the potential functions of TIR1/AFBs during growth and in response to stress; they highlight differences among the SmTIR1/AFBs that may be useful for eggplant breeding.

Molecular Origins of the Mendelian Rare Diseases Reviewed by Orpha.net: A Structural Bioinformatics Investigation.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the pathogenic missense variants found in the 2515 proteins involved in Mendelian rare diseases (MRDs), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRDs showed different mutation site distributions at different three-dimensional protein depths. We then highlighted the depth-dependent effects of pathogenic variants for the 20,061 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

PPInterface: A Comprehensive Dataset of 3D Protein-Protein Interface Structures

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein–protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein–protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset’s utility and accessibility.The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/.