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Results for “In silico”

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At least 19 recordsLinked to original sources

PubMed2024

Exploring novel MYH7 gene variants using in silico analyses in Korean patients with cardiomyopathy.

BACKGROUND: Pathogenic variants of MYH7, which encodes the beta-myosin heavy chain protein, are major causes of dilated and hypertrophic cardiomyopathy. METHODS: In this study, we used whole-genome sequencing data to identify MYH7 variants in 397 patients with various cardiomyopathy subtypes who were participating in the National Project of Bio Big Data pilot study in Korea. We also performed in silico analyses to predict the pathogenicity of the novel variants, comparing them to known pathogenic missense variants. RESULTS: We identified 27 MYH7 variants in 41 unrelated patients with cardiomyopathy, consisting of 20 previously known pathogenic/likely pathogenic variants, 2 variants of uncertain significance, and 5 novel variants. Notably, the pathogenic variants predominantly clustered within the myosin motor domain of MYH7. We confirmed that the novel identified variants could be pathogenic, as indicated by high prediction scores in the in silico analyses, including SIFT, Mutation Assessor, PROVEAN, PolyPhen-2, CADD, REVEL, MetaLR, MetaRNN, and MetaSVM. Furthermore, we assessed their damaging effects on protein dynamics and stability using DynaMut2 and Missense3D tools. CONCLUSIONS: Overall, our study identified the distribution of MYH7 variants among patients with cardiomyopathy in Korea, offering new insights for improved diagnosis by enriching the data on the pathogenicity of novel variants using in silico tools and evaluating the function and structural stability of the MYH7 protein.

Humans↗

PubMed2025

Multi-criteria decision making and its application to in silico discovery of vaccine candidates for Toxoplasma gondii.

Vaccine discovery against eukaryotic parasites is not trivial and few exist. Reverse vaccinology is an in silico vaccine discovery approach, designed to identify vaccine candidates from the thousands of protein sequences encoded by a target genome. Previously, we produced the Vacceed bioinformatics pipeline for identification of parasite membrane and excreted/secreted proteins that were likely be exposed to the hosts immune system. More recently, we improved upon machine learning as the final decision-making process to identify parasite proteins that induce a protective response in an animal model. Subsequently, we combined Vacceed with metrics on B and T cell epitope types to produce a new in silico discovery workflow. In this study we extend this in silico workflow to the developability of proteins as vaccines by the incorporation of metrics on the physicochemical properties of proteins. To demonstrate this process, every Toxoplasma gondii protein was ranked in its capacity to provide exposure to the immune system (Vacceed exposure score), presence of epitopes and solubility characteristics by several multicriteria decision making (MCDM) tools (such as TOPSIS, VIKOR and MABAC). A consensus rank was subsequently generated from the results of these tools using a variety of aggregate ranking methods. Levels of uncertainty in the aggregate protein rankings was assessed by conformal interval prediction in association with a machine learning model. Several of the top ranked proteins identified by this approach were novel, uncharacterized membrane transporters or proteins associated with RNA metabolism. In conclusion, MCDM automated the decision making using well known algorithms while conformal prediction intervals varied significantly across the 8000+ proteins of T. gondii. Highly ranked proteins (e.g. the top 100) typically generated low prediction intervals, providing high levels of confidence in their ranks.

Toxoplasma↗

PubMed2025

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

Journal Article↗

PubMed2025

In vivo and in silico models of Drosophila for Parkinson's disease.

The fruit fly Drosophila melanogaster has emerged as an important model organism to shed light on neurodegeneration. Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, the cause of which is still mostly unclear. The long-term use of available PD drugs may have major side effects, and they only target the symptoms without providing any effective cure for the disease. Therefore, in vivo and in silico approaches are extensively used to model PD-like phenotypes in Drosophila and investigate cellular alterations underlying PD pathogenesis. In vivo models are particularly crucial to provide insight into the PD-related molecular processes. It has been a preferred approach to investigate these models by collecting omics datasets, which can be further analysed using in silico modeling such as genome-scale metabolic models and artificial intelligence applications. This review aims to summarise in vivo and in silico modeling studies in the literature to illustrate the potential of the Drosophila in the characterisation of PD-related biological mechanisms towards providing early biomarkers and novel treatment options for PD.

Humans↗

PubMed2026

Epigenetics and In Silico Transcriptome Analysis of Pediatric Acute Myeloid Leukemia.

Pediatric acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy that accounts for about 15%-20% of childhood leukemias. Despite therapeutic advances, relapses remain common, and survival for high-risk patients is below 60%. Unlike adult AML, pediatric AML displays distinct genetic mutations, including FLT3-ITD, NPM1, KMT2A rearrangements, and core-binding factors (CBF) fusions, as well as extensive epigenetic dysregulation. Aberrant DNA methylation, histone modifications, and altered non-coding RNA expressions disrupt hematopoietic differentiation and activate oncogenic transcriptional networks. Recent advances in silico transcriptomic analysis have transformed the study of pediatric AML by integrating gene expression and epigenetic data to identify molecular drivers and regulatory networks. Computational RNA-seq pipelines and pathway analyses have highlighted key epigenetic regulators, including DNMT3A, TET2, and HDACs, as potential therapeutic targets. Multi-omics approaches combining transcriptomic, methylomic, and chromatin accessibility data are increasingly used to define biomarkers for diagnosis, prognosis, and therapeutic response. This review provides a comprehensive overview of the molecular and epigenetic landscape of pediatric AML, emphasizing the power of in silico transcriptome analysis to uncover disease mechanisms, refine patient stratification, and guide the development of precision-based epigenetic therapies aimed at improving long-term outcomes in children with AML.

Humans↗

PubMed2026

Patterns of antimicrobial resistance genes in pathogens across One Health sectors in Ireland: an in silico approach.

As part of a rapid risk assessment, an in silico approach was used to detect antimicrobial resistance (AMR) in pathogenic isolates from humans, animals, and the environment. A total of 11,670 genomic data sets were retrieved from the NCBI Pathogen Detection system for Ireland, which represented 47 pathogenic species, including Salmonella enterica, Escherichia coli/Shigella spp., Staphylococcus aureus, Klebsiella pneumoniae, and Enterococcus faecium. Identifying the most critical pathogenic strains over time is essential, as these organisms significantly contribute to mortality, morbidity, and hospitalization. The analysis identified 799 antimicrobial resistance genes (ARGs), including their allelic diversity, 117 plasmid replicons, and 274 virulence factors. Several critical ARGs, particularly those conferring resistance to beta-lactams, aminoglycosides, quinolones, and colistin, were common across isolates originating from human, animal, and environmental sources, suggesting shared resistance profiles across One Health sectors. Klebsiella pneumoniae, E. coli/Shigella spp., S. enterica, and S. aureus were the dominant hosts of these ARGs and associated mobile genetic elements. Increasing resistance across major antibiotic classes aligned with trends reported across other European countries. This study provides a national-scale in silico comparison of AMR across pathogens and One Health sectors using publicly available genomic data. The findings help reinforce Ireland's AMR surveillance by showing which resistance genes are present and how they spread across critical pathogens in humans, animals, and the environment. These findings highlight the urgent need for improved antibiotic stewardship and integrated One Health surveillance to limit the emergence and spread of AMR.IMPORTANCEAntimicrobial resistance (AMR) is a growing threat to human, animal, and environmental health. This study used publicly available genomic data to identify antimicrobial resistance genes (ARGs) in key bacterial pathogens circulating in Ireland. By analyzing over 11,000 genomes from humans, animals, and the environment, we found that several dangerous resistance genes, including those against last-resort antibiotics, were widespread across different sources. The study highlights which bacteria and resistance genes are most critical and how they may spread between humans, animals, and the environment. These insights provide a national snapshot of AMR, supporting more effective monitoring and prevention strategies. By revealing patterns of resistance and modes of transmission, our findings underscore the importance of coordinated antibiotic stewardship and One Health approaches to slow the emergence and spread of resistant infections, protecting public health and ensuring antibiotics remain effective.

Humans↗

PubMed2026

Multitargeted comparative evaluation suggests 2-Aoeobenoxmide shows favourable in silico binding compared to Tucatinib against ERα, HER2, AKT1, EGFR, and PIK3CA in breast cancer.

Breast cancer is a leading cause of cancer-related morbidity and mortality globally, with the WHO reporting approximately 2.3 million new cases and 685,000 deaths annually. Drug resistance in breast cancer complicates treatment, with mutations in critical proteins contributing to therapy failure. Key oncogenic proteins involved in breast cancer progression-namely ERα (a ligand-activated nuclear receptor; PDB: 1A52) and the kinase domains of HER2 (PDB ID: 3PP0), AKT1 (PDB ID: 4EJN), EGFR (PDB ID: 4I23) and PIK3CA (PDB ID: 7R9V)-are pivotal in tumour progression and resistance mechanisms. Targeting these proteins using multitargeted therapeutic strategies may overcome resistance by disrupting key signalling pathways involved in cell proliferation, survival, and metastasis. Such combinatorial approaches promise to improve treatment efficacy and patient outcomes in cases of resistant breast cancer. In this study, we performed multitarget docking on prepared and validated protein structures against the ZINC natural compound library using HTVS, SP, and XP, with pose validation using MM-GBSA. We identified 2-Aoeobenoxmide (2-[1-(2-amino-2-oxo-ethoxy)-6-oxo-benzo[c]chromen-3-yl]oxyacetamide, ZINC134008) with docking and MM-GBSA scores ranging from -8.162 to -10.327 kcal/mol and from -47.18 to -57.62 kcal/mol, respectively, and compared the results with the FDA-approved drug Tucatinib, which exhibited lower binding affinity scores. We further evaluated pharmacokinetic properties using QikProp and electronic properties using DFT (Jaguar) and compared the descriptors of 2-Aoeobenoxmide with those of Tucatinib and with accepted reference ranges. We also performed the WaterMap for 5 nanoseconds (ns), computed various energies, interactions and hydration sites, and the comparison suggests that 2-Aoeobenoxmide shows more favourable hydration-site displacement and binding interactions than Tucatinib. Additionally, a 100 ns MD Simulation has resulted in far less deviation, fluctuations, and intermolecular interactions than Tucatinib, suggesting stable protein-ligand interactions, while the binding free energy and total complex energy computed across 0-1000 frames of the MD trajectories indicate that 2-Aoeobenoxmide is a promising in silico candidate. Importantly, because the entire study is computational, the findings should be interpreted as in silico hypotheses, and experimental validation through in vitro and in vivo assays is warranted before any clinical translation is considered.

Humans↗

PubMed2025

In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions.

Plant Nucleotide-binding leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification of a myriad of NLRs in numerous agriculturally important plant species. However, deciphering which NLRs recognize specific pathogen effectors remains challenging. Predicting NLR-effector interactions in silico will provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR-effector interactions reported in literature. Binding affinities- and energies were predicted using 97 machine learning models from Area-Affinity. We show that AlphaFold2-Multimer predicted structures have acceptable accuracy and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol-1, depending on the Area-Affinity model used. For 2427 "forced" NLR-effector complexes, these estimates showed larger variability, enabling identification of novel NLR-effector interactions with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for "true" interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all pathosystems. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for plant-pathogen resistance, advancing our understanding of plant immunity.

Plant Proteins↗

PubMed2026

Probiogenomic analysis of functional potential and safety of L. plantarum 8p-a3 and DMC-S1 strains: in silico vs in vitro and in vivo data.

The molecular basis of the beneficial effects and the causes of the negative effects of probiotics are not entirely clear. Clarifying these issues is important for understanding the biology and assessing the safety of the microbes. Omics technologies have opened up new resources for obtaining relevant knowledge. Here, for the first time, we present the results of a comparative analysis of the functional potential and safety of two L. plantarum strains: the approved probiotic 8p-a3 and the Drosophila intestinal resident, which exhibit opposite effects on D. melanogaster as the model host organism. Through genomic analysis, extracellular vesicle studies, and in vitro and in vivo assays, we have identified the common and specific characteristics of the strains. The strains proved to be similar in a set of genes that determine benefits to the host organism, as well as in the presence of some risk factors. Significant differences between the strains are related to genes responsible for adhesion, sialic acid metabolism, mucin degradation, antimicrobial peptides, tannin resistance, and immunomodulation. In silico data correlated with in vitro and in vivo data, with the exception of antimicrobial sensitivity. Pronounced differences between the strains were found in terms of the composition and biological effects of their vesicles. In vivo data on the effects of the strains correlate with the corresponding data of their vesicles in the fruit fly model. The results obtained open up new facets in L. plantarum strains relevant for evaluating the functionality and safety of probiotics.IMPORTANCEUsing a probiogenomic approach, common and specific features regarding functionality and safety were identified in the strains (the approved probiotic strain L. plantarum 8p-a3 and the Drosophila intestinal bacterium L. plantarum DMC-S1), which exhibit opposite effects on the model host organism (D. melanogaster). The genomic analysis was supplemented by the analysis of extracellular vesicles of the strains. Comparative analysis of in silico data in combination with in vitro and in vivo studies was performed, and unexpected capabilities of the strains were discovered. Novel factors, essential for evaluating the safety of probiotics, were identified. New facets in the interplay of probiotic bacterium with host organism have been revealed.

Animals↗

PubMed2025

In silico identification of Leishmania GP63 protein epitopes to generate a new vaccine antigen against leishmaniasis.

BACKGROUND: The surface of Leishmania spp. presents glycoprotein 63 (GP63), a metalloprotease that acts as one of the parasite's major antigens. A vaccine against leishmaniasis has not yet been developed and stationary phase promastigotes have utmost importance in transmitting Leishmania spp. from phlebotomine sand fly to humans or reservoirs. Therefore, this study aimed to analyze GP63 protein in three different Leishmania spp. to determine new vaccine candidate antigen against leishmaniasis using sequencing data of locally detected Leishmania strains and in silico approaches. METHODOLOGY/PRINCIPAL FINDINGS: The GP63 protein sequences of the stationary phase/amastigote form of L. infantum, L. major, and L. tropica were identified and then the gene encoding GP63 protein in Leishmania positive samples (n:59) was amplified and sequenced for variation analysis. According to the results, 4, 6, 19 GP63 variants were found within L. infantum, L. major, and L. tropica isolates, respectively. The most prevalent variants within each species were selected for further analysis using in silico approaches. Accordingly, all selected GP63 proteins were antigenic and the amount of B and T cell epitopes were 23 for L. infantum, 10 for L. major, and 9 for L. tropica. The analysis of each epitope showed that all of them were non-toxic, non-allergen, and soluble but had different antigenicity values. Among these epitopes, EMEDQGSAGSAGS associated with L. major, STHDSGSTTC and AEDILTDEKRDILRK epitopes associated with L. infantum had the highest antigenicity values for B cell, MHC-I, and MHC-II epitopes, respectively. Moreover, conserved epitopes were detected among two or three Leishmania species. CONCLUSIONS/SIGNIFICANCE: This study detected many epitopes that could be used in vaccine studies and the development of serological diagnostic assays.

Antigens, Protozoan↗

PubMed2026

Enhanced Production of Recombinant Thermophilic Xylanase X11P in Ogataea polymorpha via In-Silico Signal Peptide Discovery and Fed-Batch Fermentation.

Efficient secretion of heterologous proteins is essential for advancing yeast-based bioprocesses, yet signal peptide (SP) optimization in the thermotolerant methylotrophic yeast Ogataea polymorpha remains limited. This study integrates in-silico SP discovery, experimental validation, and bioprocess engineering to enhance secretion of the thermophilic xylanase X11P under sucrose-inducible expression. Genome-wide screening of 5184 O. polymorpha proteins using SignalP, Phobius, DeepLoc, WoLF PSORT, and ProP identified 11 high-confidence SP candidates. Comparative analysis with Komagataella phaffii endogenous proteins guided selection of seven SPs for experimental evaluation. Among these, the novel O. polymorpha α-mating factor-like peptide FUN_005010 exhibited strong secretion-promoting activity, with its prepro-sequence yielding the highest extracellular xylanase levels and outperforming the classical Saccharomyces cerevisiae α-MF. To evaluate industrial applicability, sucrose-based fermentation strategies were systematically optimized in a 5-L bioreactor. Controlled sucrose feeding and balanced C/N ratios were found to be critical for maximizing maltase (MAL) promoter-driven expression. A stepwise increasing sucrose feed combined with induction at 30°C enabled X11P titers up to 770 U/mL, representing a 15-fold improvement over shake-flask cultures. This work demonstrates that the combination of SP evaluation and optimized sucrose-inducible fed-batch operation significantly enhances X11P production in O. polymorpha. The identified FUN_005010-prepro SP and the refined process framework provide valuable tools for developing O. polymorpha as a high-performance industrial expression platform.

Fermentation↗

PubMed2026

In-Silico and Functional Characterization of EcdLp, an ABC Transporter of Aspergillus nidulans NRRL11440.

Echinocandin B (ECB) biosynthesis in Aspergillus nidulans is primarily governed by multiple genes located within the biosynthetic echinocandin (ecd) gene cluster. The contributory functions of many genes, including transcription factors and tailoring enzymes of the ecd gene cluster, have been previously studied. The present study focused on determining the role of transporter proteins, EcdLp, EcdCp, and EcdDp, in ECB efflux using in silico and biochemical approaches. The molecular docking analysis revealed that ECB relatively showed higher binding affinity for EcdLp than the other co-clustered MFS transporters EcdCp and EcdDp, suggesting a preferred substrate of EcdLp. These results were further confirmed by heterologous integration of the ecdL gene in the ABC transporters-deficient Saccharomyces cerevisiae AD1-8u⁻, confirming active efflux. However, the binding of ECB in EcdLp is distinct from the R6G binding, overlapping the promiscuous site of farnesol, resulting in inhibition of R6G efflux in a dose-dependent manner. In conclusion, these results decipher the ECB binding and efflux mechanism and unveil the evolutionarily specialized architecture of EcdLp that permits targeted metabolite export in addition to environmental responsiveness, and lay the groundwork for optimizing ECB production via transporter engineering.

Aspergillus nidulans↗

PubMed2026

Comparative Genomics-Guided Epitope Prioritization and in Silico Design of a Multi-Epitope DNA Vaccine Candidate Against Megalocytivirus pagrus 1.

Megalocytivirus pagrus 1 infection is a World Organisation for Animal Health-listed aquatic animal disease caused by a virus species comprising the RSIV, ISKNV, and TRBIV genogroups. Here, we integrated comparative genomics and immunoinformatics to prioritize a multi-epitope protein construct, pMEV, and to design a DNA vaccine candidate encoding it, with emphasis on RSIV-type infection relevant to rock bream aquaculture. Analysis of 61 complete genomes identified 28 core gene clusters, from which myristoylated membrane protein (MMP) and major capsid protein (MCP) were prioritized as source antigens for epitope screening. Four cytotoxic T-cell, five helper T-cell, and five linear B-cell epitope candidates were selected based on sequence-based screening and exploratory peptide-MHC docking. The selected epitopes were assembled with rock bream beta-defensin-3, PADRE, and peptide linkers to generate the 283-aa pMEV construct. Sequence-based physicochemical analyses indicated properties relevant to subsequent structural and expression-based evaluation, while computationally refined structural modeling identified nine putative conformational B-cell epitope regions. TLR3 docking, normal mode analysis, and a 200-ns molecular dynamics simulation characterized the structural behavior of the selected computational complex without inferring receptor activation. C-ImmSim further generated model-dependent generic humoral and helper T-cell-associated response patterns within a mammalian-based simulation framework. Finally, the pMEV coding sequence was codon-optimized and incorporated into an in silico pcDNA3.1(+)-based DNA vaccine design. Collectively, this study provides a comparative genomics-guided framework for prioritizing an experimentally testable multi-epitope DNA vaccine candidate against M. pagrus 1, while construct expression, immunogenicity, and protective efficacy remain to be evaluated experimentally.

Animals↗

PubMed2026

In silico analysis of metal resistance genes in Pseudomonas extremaustralis 2E-UNGS: Genomic insights and safety assessment for wastewater biotreatment.

Pseudomonas extremaustralis 2E-UNGS is a non-pathogenic strain isolated from the polluted Reconquista River basin (Buenos Aires Metropolitan Area, Argentina), with a 20-year history of study focused on its survival strategies that have enabled its application in various processes such as waste biotreatment and biosensing. Regarding bacterial-metal interactions, P. extremaustralis 2E-UNGS is capable of biosorbing Cd(II), Zn(II), and Cu(II), and biotransforming Cr(VI) to Cr(III), facilitating both the removal of these metals from aqueous systems and their use in biosensor development. The complete circular chromosome (6,372,594 bp) has been annotated in the NCBI GenBank under accession number NZ_CP091043.1. The aim of this work was to perform an in-depth exploration of the P. extremaustralis 2E-UNGS genome to support the optimization of sustainable bioprocesses within the One Health framework. To this end, the integration of experimental evidence with a detailed in silico analysis of key genes involved in metal-microorganism interactions, antibiotic resistance, and their interconnections provides valuable insights for the optimization of future biotechnological applications. Considering its antibiotic resistance profile, together with the activation of efflux pumps induced by metal stimuli-particularly observed under Zn(II) exposure-P. extremaustralis 2E-UNGS can be regarded as suitable for the design of confined bioreactor processes, minimizing the risk of potential accidental environmental releases. Therefore, modulation of gene expression emerges as a promising approach to enhance the efficiency of metal-loaded wastewater biotreatments.

Pseudomonas↗

PubMed2026

Molecular Cloning, Recombinant Expression, and In Silico Structural Analysis of Cu/Zn-Superoxide Dismutase from Trachyspermum ammi.

Superoxide dismutase (SOD) is an essential antioxidant metalloenzyme that is critical for the cellular defense against oxidative damage, as it scavenges superoxide radicals and maintains the redox status. Cytosolic Cu/Zn-SOD is particularly important in the regulation of oxidative stress among different isoforms in higher plants. While Cu/Zn-SODs from several plant species have been characterized, molecular information is limited for Trachyspermum ammi, a medicinally important member of a family Apiaceae with antioxidant potential.In the present study, an integrated molecular and in silico approach has been taken to clone and analyze a Cu/Zn type SOD gene from T. ammi to get insight into its structural and evolutionary characteristics. PCR amplification yielded an open reading frame of 456 bp encoding a protein of 152 amino acids. Sequence analysis showed that plant Cu/Zn-SODs, especially those from Daucus carota, were highly similar to one another (about 90-95%).Multiple sequence alignment confirmed the presence of conserved catalytic motifs and metal-binding histidine residues, both of which are crucial for enzymatic function. Physicochemical analysis predicted the protein to be stable, hydrophilic and compatible with cytosolic localization. The analysis of secondary structure indicated a predominance of β-strands, consistent with the conserved β-barrel architecture of plant Cu/Zn-SODs.The three-dimensional structure was built by homology modeling using a closely related plant Cu/Zn-SOD template with high sequence identity. Structural validation demonstrated an acceptable stereochemical quality with 86.3% residues in the favored region of Ramachandran plot, satisfactory ERRAT and Verify3D scores, and a low RMSD value of 0.104 Å on structural superimposition. Phylogenetic analysis placed the enzyme in the Apiaceae lineage, suggesting evolutionary conservation among related plant species. In conclusion, this study presents the first molecular and structural characterization of Cu/Zn-SOD from T. ammi and confirms the existence of a conserved structural framework typical of plant Cu/Zn-SODs. These results provide a basis for further studies concerning recombinant expression, enzymatic validation and potential relevance in antioxidant and plant stress biology.

Cloning, Molecular↗

PubMed2015

In Silico Reconstruction of the Viral Evolutionary Lineage Yields a Potent Gene Therapy Vector.

Adeno-associated virus (AAV) vectors have emerged as a gene-delivery platform with demonstrated safety and efficacy in a handful of clinical trials for monogenic disorders. However, limitations of the current generation vectors often prevent broader application of AAV gene therapy. Efforts to engineer AAV vectors have been hampered by a limited understanding of the structure-function relationship of the complex multimeric icosahedral architecture of the particle. To develop additional reagents pertinent to further our insight into AAVs, we inferred evolutionary intermediates of the viral capsid using ancestral sequence reconstruction. In-silico-derived sequences were synthesized de novo and characterized for biological properties relevant to clinical applications. This effort led to the generation of nine functional putative ancestral AAVs and the identification of Anc80, the predicted ancestor of the widely studied AAV serotypes 1, 2, 8, and 9, as a highly potent in vivo gene therapy vector for targeting liver, muscle, and retina.

Dependovirus↗

PubMed2026

Targeting EGFR in cancer using Terminalia arjuna: An integrated In Silico, molecular dynamics, experimental validation, and network pharmacology study.

The Epidermal Growth Factor Receptor (EGFR) plays a pivotal role in 20-60% of cancer cases, including glioblastoma, lung adenocarcinoma, and head and neck squamous cell carcinoma, as reported in The Cancer Genome Atlas (TCGA) dataset. The present study employed an integrated in silico and experimental workflow to evaluate EGFR-targeted compounds from Terminalia arjuna. Drug-likeness and ADMET screening were performed, followed by molecular docking and 1000 ns molecular dynamics simulations. In vitro validation was conducted using cancer cell-based assays and network pharmacology to explore the molecular mechanisms associated with the identified compound. Screening shortlisted eight compounds from T. arjuna. Molecular docking identified Arjunaside C (-8.2 kcal/mol), Arjunapthanoloside (-7.7 kcal/mol), and Beta-sitosterol (-7.4 kcal/mol) as potential EGFR inhibitors compared to Erlotinib (-6.6 kcal/mol). Arjunapthanoloside formed more H-bonds and exhibited most stable interactions with EGFR. MD simulations at 1000 ns revealed lower RMSD, RMSF, SASA, and Rg values for the Arjunapthanoloside-EGFR complex, indicating enhanced stability. Direct binding validation was limited by the unavailability of purified Arjunapthanoloside; therefore, Arjuna extract was evaluated, which demonstrated potent cytotoxicity with an IC₅₀ of 9 µg/mL in H357 oral cancer cells. Flow cytometry confirmed apoptosis-mediated cell death by increased early- and late-apoptotic cell populations. Network pharmacology analysis further identified additional targets (MMP3, MMP7, MMP9, and HRAS) that are directly involved in various cancers. Overall, the findings provide new insights into the therapeutic potential of Arjunapthanoloside as a stable compound that interacts with EGFR from T. arjuna, highlighting its significance in EGFR-targeted anticancer research.

ErbB Receptors↗

PubMed2026

Discovery and characterization of multifunctional bioactive peptides from Alaska Pollock (Gadus chalcogrammus) milt: hybrid in silico, in vitro, and proteomic approaches.

The growing demand for multifunctional bioactive peptides has sparked interest in underutilized marine by-products as sustainable bioresources. This study explored Alaska Pollock (Gadus chalcogrammus) milt protein as a novel source of peptides with anti-inflammatory, anti-hypertensive, and anti-diabetic effects. Protein composition was analyzed via LC-MS, followed by in silico digestion and bioactivity prediction. Molecular docking identified peptides targeting DPP-IV, α-glucosidase, ACE, GLP-1 receptor, COX-2, MuRF1, and the 20S proteasome. Among the candidates, a promising peptide (CLPPH) was synthesized and validated in vitro, demonstrating inhibitory effects on nitric oxide production, DPP-IV, ACE, and α-glucosidase. These results highlight CLPPH's potential as a multifunctional bioactive peptide and support the valorization of Alaska Pollock milt as a sustainable source for functional foods and nutraceutical applications.

Animals↗