Given the established oncogenic role of KRAS in lung cancer and its predicted regulation by hsa-miR-134 and transcription factors (NCOA4, NR3C2, ETS2), we systematically catalogued genetic variation across the KRAS gene. In this study, we identified 21,598 SNPs, of which 475 were missense (2.19%), 580 were synonymous (2.68%), and 18,093 were intronic (83.77%). In silico prioritization combined with cross-validation in TCGA-LUAD and MSK-IMPACT cohorts identified four high-confidence deleterious variants (G13C, G13D, G60S, G60V), confirming the recurrence of G13C/G13D and highlighting the fully conserved switch II residue G60 as a structurally distinctive candidate. In MD simulations, G60S showed the largest conformational displacement (RMSD ≈ 8-9 Å), elevated per-residue flexibility (residues 28-37: 1.90 → 2.99 Å), increased terminal Rg, and reduced hydrogen-bond occupancy, suggesting localized, mutation-specific perturbations within the G-domain. In contrast, G13D maintained near-wild-type dynamics, indicating that functional impairment may not require global structural destabilization. Consistent ensemble analyses (PCA, DCCM, FEL) revealed a dominant single-mode rearrangement for G60S (PC1 ≈ 50.8%), a heterogeneous multi-state landscape for G60V, and a restricted low-energy ensemble for G13D, supporting distinct, position-specific mechanisms. Gene-gene and protein-protein interaction analyses linked KRAS to multiple signaling partners, GO and KEGG enrichment analyses associated KRAS with GTPase activity, Rac protein signaling, and the RAS-MAPK and PI3K-AKT pathways implicated in lung cancer progression. Overall, these findings identify G13C, G13D, G60S, and G60V as high-priority candidates for experimental validation and may inform future strategies for genomic risk assessment and mutation-specific therapeutic intervention in lung cancer.

Lymphatic filariasis remains a major neglected tropical disease and there is an urgent need for parasite-specific therapeutic targets. The enzyme UDP-galactopyranose mutase (UGM) is essential for galactofuranose biosynthesis in Brugia malayi and is absent in humans, which makes it a promising drug target. In this study, we investigated phytochemicals from the bark of Tecomella undulata to find potential drug candidates against B. malayi UGM using an in silico approach. The structure of the target protein was predicted using the Alphafold model, and the active site was predicted using the combination of PrankWeb, DoGSiteScorer, and FTMap servers and further supported by superposition with a homologous experimentally determined UGM structure. Forty phytochemicals of T. undulata bark were screened by molecular docking using AutoDock Tools. Docking analysis showed Dehydrotectol (- 11.73 kcal/mol), Daucosterol (- 11.35 kcal/mol), urs-12-ene (- 10.87 kcal/mol), β-sitosterol (- 10.72 kcal/mol), and betulinic acid (- 10.47 kcal/mol) are the top-scoring ligands. Based on predicted binding free energy and interaction with key catalytic residues of UGM, Daucosterol was selected for molecular dynamics simulation using the GROMACS package. MD analysis and MM/PBSA calculations supported the thermodynamic stability of the docked complex. ADMET predictions indicated acceptable drug-likeness and low toxicity of several phytoconstituents of T. undulata bark. This study provides computational evidence supporting T. undulata bark phytochemicals can be a new drug formulation against B. malayi.

The exploration of protein structure and folding mechanisms is a cornerstone of molecular biology, with significant implications for understanding biological functions and advancing drug discovery. Traditional computational methods, such as AlphaFold, RoseTTAFold and I-TASSER, have been instrumental in this field. However, these methods are often constrained by extensive challenges in predicting structures for proteins lacking significant homologous sequences in the training data, impacting accuracy in novel or less well-characterized proteins. In contrast, quantum computing offers a promising avenue for overcoming these limitations through algorithms like the Variational Quantum Eigensolver (VQE). Quantum computing is in an early stage for optimization problems, particularly for protein folding. This study harnesses the power of VQE to explore the folding behavior and low-energy conformational landscape of 15 peptides, each comprising eight amino acids, selected from the RCSB Protein Data Bank (PDB). Classical 100-ns molecular dynamics (MD) simulations are used to characterize the structural stability and relative energetic trends of the corresponding atomistic conformations. These results are then contrasted with energies obtained from a lattice-based Hamiltonian optimization using the VQE algorithm, executed on the IBM quantum simulator. Although the absolute energy scales differ between MD and lattice Hamiltonian models, this comparison enables a correlation-based evaluation of how effectively VQE identifies physically plausible low-energy folded states. The results demonstrate that VQE consistently converges to competitive low-energy conformations. Further investigation was carried out by converting the PDB structure into a lattice model using the LatFit tool for visual comparison with the VQE-predicted structure. In addition, quantitative evaluation was performed by comparing the VQE-predicted structure with the original PDB structure using Cα Root Mean Square Deviation (RMSD) and Template Modeling (TM) score. Furthermore, the TM-scores obtained from the VQE predictions were compared with those obtained from the AlphaFold predictions. This work highlights the benefits of utilizing quantum algorithms to examine protein folding and also paves the road for future quantum biology studies.

Nuclear receptors (NRs) are ligand-dependent transcriptional regulators whose ligand-binding residues are crucial for receptor activation and drug-target analysis. In this study, we developed NRB-FusePS, a protein language model-based multimodal framework for predicting BioLiP-defined ligand-binding residues in NRs. Nuclear receptor ligand-binding information was collected from the BioLiP database, and residue-level samples were generated using 21-residue sliding windows. A protein-level independent test strategy was used to reduce information leakage caused by overlapping windows from the same protein. Sequence composition, amino acid pair, structural, evolutionary, and ESM2 protein language model descriptors were extracted and evaluated using conventional machine learning models. Based on these results, a three-branch deep learning model called NRB-Fuse was constructed to integrate sequence statistical descriptors, structural descriptors, and ESM2 embeddings, and protein-level probability smoothing was introduced to obtain NRB-FusePS. On the independent test set, NRB-FusePS-Full achieved an area under the receiver operating characteristic curve (AUC) of 0.814, a Matthews correlation coefficient (MCC) of 0.406, and a sensitivity of 0.964. Shapley additive explanations (SHAP) analysis showed that ESM2 was the main information source, and the structural case of O00482 supported the predictive utility. The NRBsite web server provides three prediction modes for different levels of input information and is freely available at http://47.95.115.29:8501/ .

A series of thiadiazol-hydrazine derivatives (3a-3d) were rationally designed, synthesized, and characterized to explore their antimicrobial potential and possible interactions with dihydrofolate reductase (DHFR). The antimicrobial activity of the synthesized compounds was evaluated against representative Gram-positive bacteria (S. aureus and S. pyogenes), Gram-negative bacteria (E. coli and P. aeruginosa), and fungal strains (C. albicans, A. niger, and A. clavatus) using the minimum inhibitory concentration (MIC) method. Among the synthesized derivatives, compound 3b exhibited the most favorable overall antimicrobial profile, particularly against the tested bacterial strains, whereas the antifungal activity of the derivatives was comparatively limited.To investigate the possible molecular basis of the observed antimicrobial activity, molecular docking studies were performed against dihydrofolate reductase (DHFR; PDB ID: 1AOE). The synthesized derivatives exhibited favorable predicted binding within the DHFR active-site region, with compound 3b showing the most favorable docking score among the investigated compounds. The predicted binding mode of 3b was characterized by hydrogen-bonding and hydrophobic interactions with residues lining the DHFR binding pocket. Molecular dynamics simulations and interaction-energy analyses were subsequently employed to examine the stability and dynamic behavior of the predicted protein-ligand complexes. The simulation results supported the persistence of the predicted interactions and provided additional computational evidence for the favorable accommodation of 3b within the DHFR binding site.Density functional theory (DFT) calculations at the B3LYP-D3BJ/def2-TZVP level were performed to investigate the electronic properties of the synthesized derivatives. The calculated frontier molecular orbital characteristics and related electronic descriptors provided complementary insights into the influence of structural variation on molecular reactivity and the observed biological activity. Furthermore, haemolytic toxicity studies demonstrated low erythrocyte membrane disruption (< 3% haemolysis), indicating favorable preliminary erythrocyte compatibility under the tested conditions.Overall, the integrated experimental and computational results identified compound 3b as the most promising member of the present series, particularly with respect to antibacterial activity, while the comparatively limited antifungal activity indicates the need for further structural optimization. The computational findings provide a preliminary basis for proposing DHFR as a potential molecular target; however, experimental enzyme inhibition studies will be required to confirm the proposed DHFR-mediated mechanism.

In this study, isocitrate dehydrogenase (IDH) was employed exclusively as a structural protein template for the synthesis of gold nanoparticles, rather than relying on its enzymatic catalytic function. The reduction of Au[3+] to Au[0] is accomplished through the intrinsic reducing capability of amino acid residues (particularly tryptophan and tyrosine) within the protein scaffold, which simultaneously acts as a capping and stabilizing agent. This protein-templating strategy provides a straightforward, biocompatible, and cofactor-free approach for developing AuNP-based biosensing platforms. So, the IDH-based AuNPs offers promising applications across medical diagnostics, environmental monitoring, food safety, and research. In this study, we produced the recombinant isocitrate dehydrogenase of Yarrowia lipolytica yeast in E. coli BL21. After optimizing the enzyme activity/stability in the proper pH and temperature, its activity was then measured with Km of 50.1 µM and 9.7 µM for isocitrate and NADP[+], respectively. Then, we synthesized gold nanoparticles with a size of 16.3 nm and investigated their synthesis in the absence and presence of IDH. The in situ synthesized AuNPs showed acceptable results in the presence of IDH during the emergence of ruby red color or the detection of increased Au[0] at 526 nm. IDH facilitates in situ AuNP formation through a combination of: (i) enzymatic generation of NADPH, (ii) reducing amino acid residues on the protein surface, and (iii) a templating effect that controls nucleation. This multifunctional protein matrix eliminates the need for post-synthesis enzyme attachment, offering a streamlined platform for biosensor development.

The development of sustainable multifunctional hydrogels with antibacterial activity and protein delivery capability is of great interest for biomedical applications. In this study, plant oil-derived cationic hydrogels were developed by UV-induced polymerization using acrylated methyl ricinoleate (AMR), a renewable monomer derived from castor oil, and 2-aminoethyl methacrylate (AEMA). The influence of hydrogel composition on morphology, pH-responsive swelling, antibacterial activity, cytocompatibility, and protein encapsulation behavior was investigated. FTIR and SEM analyses confirmed successful hydrogel formation and revealed composition-dependent structural differences. The swelling behavior was strongly influenced by pH and monomer ratio. The hydrogels exhibited significant antibacterial activity against Escherichia coli and Staphylococcus aureus while maintaining good cytocompatibility toward human umbilical vein endothelial (HUVEC) cells, with cell viability exceeding 80%. Bovine serum albumin (BSA) was used as a model protein to evaluate encapsulation and release properties. Protein encapsulation efficiency was found to depend strongly on crosslinking density, with lower crosslinking ratios leading to enhanced loading. Overall, the results demonstrate that combining a renewable plant oil-derived monomer with a cationic component enables the fabrication of sustainable hydrogels with tunable physicochemical and biological properties, highlighting their potential for wound healing and localized protein delivery applications.

Sodium dodecyl sulfate (SDS) is widely used in protein research. Micellar binding of SDS is accepted as a major mechanism of SDS binding to proteins. Here, we investigated SDS-protein interactions using near-UV circular dichroism (CD), fluorescence spectroscopy, and SDS-gel electrophoresis. Bovine serum albumin (BSA), IgG, and lysozyme showed unfolding by SDS without heating or disulfide reduction as determined by SDS-gel electrophoresis. Near-UV CD and fluorescence spectroscopy showed changes in local structures surrounding tryptophan residues upon addition of 0.05-1% SDS. The fluorescence emission was blue-shifted by SDS, indicating that SDS creates more hydrophobic environments for the fluorescent tryptophan residues. We propose that, as one of the modes of SDS binding, both stoichiometric and cluster-like SDS binding occur around the fluorescent tryptophan residues, leading to changes in aromatic CD and fluorescence spectra.

The aim of this study was to develop a bioactive keratin-based nanocomposite for modulation of tyrosinase structure and function. Keratin nanoparticles (KNPs) were synthesized from ostrich feathers and functionalized with Diospyros lotus leaf extract (DLE). Physicochemical analyses (XRD, FT-IR, ζ-potential, DLS, TEM) confirmed a stable surface-functionalized nanocomposite, evidenced by a ζ-potential shift from - 16.9 mV to -40.9 mV and increased particle sizes (TEM: 87.1 to 113.3 nm; DLS: 275.3 to 325.2 nm). DLE-KNPs exhibited dose-dependent inhibition of mushroom tyrosinase with an IC50 of 0.077 mg/mL. Kinetic evaluation revealed a mixed-type inhibitory mechanism, supported by fluorescence quenching (KA=1.83 mL/mg). Far-UV CD spectroscopy showed a reduction in α-helical content (14.1% to 10.2%) with increased β-sheet (33.9% to 35.3%) and random coil (35.0% to 39.8%), indicating conformational modulation without global unfolding. Cellular evaluation on A-375 melanoma cells showed excellent biocompatibility at low doses (≤ 50 µg/mL; >90% viability) and an IC50 of approximately 300 µg/mL for the composite, compared to 230 µg/mL for free DLE extract. Flow cytometry revealed that DLE-KNPs induced 44.6% total apoptosis at 300 µg/mL (predominantly early apoptosis), while free DLE extract caused 54% apoptosis and bare KNPs 18.5%. Overall, this study identifies DLE-KNPs as a natural, mechanistically well-defined nano-bio platform with potential for modulating tyrosinase conformation and melanogenic activity, while simultaneously providing preliminary antiproliferative activity against melanoma cells.

Encapsulation of protein-based drug carriers represents a promising strategy for enhancing therapeutic efficiency by improving stability, bioavailability, and controlled release. Protein nanoparticles, due to their inherent biocompatibility, biodegradability, tunable particle size and morphology, enriched surface properties, porosity, and high encapsulation efficiency, have attracted considerable attention as drug delivery carriers. Their ability to sustain therapeutic activity has been demonstrated in the treatment of neurological disorders, infections, tumors, and cancer. A wide range of proteins, including albumin, gliadin, gelatin, zein, ferritin, and silk fibroin, have been investigated using diverse encapsulation strategies such as coacervation, self-assembly, emulsification, and ionotropic gelation. This review discusses the chemical and structural properties of protein nanoparticles, highlights recent advances in encapsulation methodologies, demonstrates the key parameters that enhance the loading and release efficiency, and analyzes drug stabilization, loading, and release profiles in relation to therapeutic performance. While protein-based delivery systems offer significant advantages, challenges such as enzymatic degradation of drugs, sensitivity to pH fluctuations, and the selectivity of protein carriers continue to limit clinical translation. In summary, this review emphasizes the potential of protein nanoparticle encapsulation to improve drug design, loading efficiency, and release behavior, thereby opening new opportunities for the development of safer and more effective therapeutic systems. At the same time, it highlights the importance of addressing existing barriers through continued research to visualize the clinical impact of protein-based drug delivery platforms.

Glucose-6-phosphate dehydrogenase (G6PD) is the rate-limiting enzyme of pentose phosphate pathway (PPP). It provides essential NADPH for redox balance and ribose-5-phosphate for nucleotide synthesis. Cancer and metastasis are linked to G6PD dysregulation, which allows tumors to progress and tolerate chemotherapy. The molecular mechanisms that hyperactivate G6PD to encourage proliferation, epithelial-mesenchymal transition (EMT), and metastatic niche adaptation are described in this work. Additionally, this review details new post-translational, protein-interaction, and epitranscriptomic control of G6PD and highlights its developing role in cancer. The function of G6PD in nucleotide synthesis and antioxidant defense is being investigated to see if it can lead to resistance to conventional and customized treatments. Lastly, we look at the predictive and therapeutic potential of G6PD. We also discuss new inhibitors and sensible combination strategies that have the potential to transform cancer therapy.

Gasdermin E (GSDME), a pivotal executor of pyroptosis, has emerged as a central regulator of immune responses across a spectrum of diseases. Initially identified as a tumor suppressor and a deafness-associated gene (DFNA5), GSDME is now recognized for its complex, context-dependent roles in health and pathology. Its activation, primarily via caspase-3 cleavage, converts apoptotic signals into a lytic, pro-inflammatory form of cell death, releasing damage-associated molecular patterns and cytokines that profoundly shape the immune microenvironment. This review synthesizes the current research landscape of GSDME, detailing its structural characteristics and canonical activation mechanisms, alongside recent discoveries of non-canonical, cleavage-independent pathways. We systematically dissect its dualistic functions in major disease categories-cancer, inflammatory/autoimmune disorders, neurodegenerative conditions, and organ injuries-highlighting the key cell-type-specific signaling pathways and their resultant immunomodulatory outcomes. Furthermore, we evaluate the burgeoning clinical potential of GSDME as a diagnostic/prognostic biomarker and a therapeutic target. We critically analyze the functional characteristics of emerging pharmacological strategies designed to either activate or inhibit the GSDME pathway, including small molecules, epigenetic modulators, and advanced nanoplatforms. Finally, we outline future research directions and propose a framework for the clinical translation of GSDME-targeted therapies, emphasizing the need for precision medicine approaches to harness its immunostimulatory potential in oncology while restraining its pathological role in inflammatory diseases.

Research publications on various aspects of functional genomics are constantly growing, providing an opportunity and challenge to mine for the information of interest, among them are the annotation of proteins into their specific functional class or the sheer degeneracy of their functions. While knowledge-driven approaches to functional annotations are based on mechanistic basis or data-driven predictive models based on deterministic features, they do not harness what is already reported in literature in different contexts. Natural language processing, combined with machine learning aims to bridge this gap. We have earlier developed a method to predict an intriguing protein functional property, called moonlighting in DNA-binding proteins using protein features with reasonable accuracy. However, the very development of training data and harnessing of available functional information from literature are the tasks, not addressed well so far for this problem. It may be noted that moonlighting being a problem of functional redundancy, literature mining may be a way to provide a cross-study perspective and hence a better prediction performance. Here we present an NLP-based model for literature mining and identifying moonlighting behaviour of proteins. A high-performing PubMed BERT model pre-trained on PubMed publications was further optimized through retraining on particular data sets, allowing accurate identification of moonlighting function in proteins. We show that this approach can identify moonlighting proteins with high accuracy and outperform first principle approaches reported earlier. The methods presented here are for moonlighting behaviour of proteins but are scalable to any literature-mining problem in biological domain. Data sets and codes used in this work are provided in GitHub repository https://github.com/Sciwhylab/moonlighting_nlp .