搜索结果：Biochimica et biophysica acta. Proteins and proteomics

Valproate (VPA), a widely used anticonvulsant, is also employed to establish experimental autism spectrum disorder (ASD) models. This study aims to elucidate mechanisms underlying VPA's effects in ASD by analyzing proteomic profiles and oxidant-antioxidant dynamics in zebrafish embryos, uncovering the cellular pathways driving these changes. Zebrafish embryos were exposed to two concentrations of VPA (10 μM and 25 μM) for 72 h post-fertilization (hpf), and LC-MS/MS analyses were performed. Differentially expressed proteins (DEPs) were subjected to bioinformatic analysis to identify associated cellular pathways, and their biological significance was evaluated. Oxidant-antioxidant parameters and locomotor activities were determined. High-dose induced more pronounced proteomic changes, while most of the identified proteins in both groups, including key metabolic enzymes such as adenylate kinase 1 (Ak1), adenosine monophosphate deaminase 1 (Ampd1), pyruvate kinase (Pkmb) and creatine kinase (Ck), exhibited a dose-dependent decrease. Functional enrichment analyses revealed that these alterations were primarily associated with purine metabolism, energy metabolism, and microtubule dynamics. In addition, malondialdehyde, nitric oxide, and glutathione S-transferase, increased in a dose-dependent manner, whereas superoxide dismutase decreased. Decreased average speed, distance swam, and explored areas were found in both VPA treated groups, reflecting early sensorimotor dysfunction. Our findings demonstrate that VPA induces dose-dependent proteomic alterations in zebrafish embryos, with metabolic pathways and cytoskeletal dynamics being particularly affected. Extent of molecular disruptions appears to correlate with VPA concentration, suggesting potential implications for energy homeostasis and cellular structure. Understanding these effects could provide valuable insights into the developmental toxicity mechanisms of VPA and its broader biological significance.

Intrinsic disorder in proteins is ubiquitous in nature across various proteomes. The association of intrinsically disordered proteins (IDP) with a large variety of biomolecules is essential in many cellular functions. Moreover, IDPs are involved in many macromolecular assemblies including ribosome and spliceosome. Intrinsic disorder has been reported in several RNA binding proteins (RBPs), which makes the study of disordered regions in RBPs crucial. Experimental difficulties in accurately determining the structure of proteins with disordered regions using biophysical techniques necessitates their computational prediction. A large number of available computational tools have been developed to predict disordered residues in proteins, but the majority of them are generic in nature. In this study, we have addressed the lack of specific intrinsic disorder predictors in RBPs. We have developed MetIoR, a prediction tool specifically trained on RBPs, to predict intrinsically disordered regions in RBP using a meta-approach. We have developed our meta predictor, which requires a protein sequence, using five individual disorder predictors. We have employed a number of machine learning classifiers with stratified ten-fold cross validation and selected the best method in terms of prediction metrics. We have also examined the most optimal combination of individual predictors. MetIoR developed using Random Forest is fast, accurate and outperforms all its individual component predictors achieving an accuracy of 93.87 %, and a high AUC value of 0.91. The developed meta predictor is deployed as the MetIoR webserver, which can be freely accessed at http://www.csb.iitkgp.ac.in/applications/MetIoR/index.php.

BRAF, a serine/threonine kinase, functions as a key effector of the MAPK signaling cascade and regulates cell proliferation and survival. Oncogenic BRAF mutations disrupt MAPK pathway homeostasis, contributing significantly to cancer progression and pathogenesis. BRAF phosphorylation is pivotal for modulating downstream signaling events. In this study, we performed a comprehensive analysis of global human phosphoproteomic datasets to elucidate BRAF phosphorylation dynamics and associated regulatory networks. The systematic annotation identified BRAF phosphorylation in 912 qualitative profiles across 166 studies and 234 quantitative differential datasets from 73 studies, revealing 44 and 21 distinct phosphosites, respectively. Class I phosphosites with localization probability ≥75% or A-score > 13 were filtered. A fold-change threshold of ≥1.3 for upregulation and ≤ 0.76 for downregulation was applied. Particularly, six predominant phosphorylation sites, S446, S729, S151, T401, S365, and S447, were frequently observed. Further analysis of melanoma-melanoma-specific phosphoproteomic datasets and correlations with gene expression data from melanoma cell lines revealed several key co-regulated proteins associated with the predominant BRAF phosphosites, including STAT3, BAD, CDK16, ITPKB, NPM1, MDC1, CHEK2, PRKDC, EIF3A, TP53BP1, RB1, and CDK14. These co-regulated proteins highlight the integration of BRAF signaling with critical processes, such as cell cycle control, apoptosis, DNA damage response, and protein synthesis in melanoma. Our analysis suggests that targeting BRAF-interacting proteins may also modulate oncogenic signaling pathways and represent promising biomarkers for melanoma diagnosis and therapy.

The Janus kinase (JAK)/STAT signaling pathway plays a pivotal role in cancer biology as well as in inflammatory and autoimmune disorders such as psoriasis. Recent advances in biomedical research and targeted therapies have highlighted the importance of computational approaches for accelerating the discovery of selective kinase inhibitors. This study aimed to develop a robust computational framework for predicting the inhibitory potency of JAK2 ligands and for analyzing their binding interactions using structure-based methods. A curated dataset of 1869 chemically valid JAK2 ligands with experimentally reported Ki values was compiled, standardized, and converted to pKi. Using this dataset, a bond-aware graph neural network (GNN) was trained and evaluated for pKi prediction. Top-ranked predicted ligands were further examined via molecular docking, pharmacophore modeling, and molecular dynamics simulations to assess their interactions within the JAK2 ATP-binding site. The proposed model achieved strong predictive performance, yielding an average test-set R2 of 0.91 ± 0.01, MAE of 0.14 ± 0.01, and RMSE of 0.26 ± 0.02 across repeated data splits. Structure-based analyses supported the predicted binding poses and identified key stabilizing interactions within the JAK2 ATP-binding site. Overall, this integrative computational framework provides a reliable approach for predicting JAK2 inhibitory potency and offers mechanistic insights that may support the computational prioritization of candidate molecules for future experimental evaluation.

Crosstalk among tRNA modification proteins have been implicated in important functional roles in biology. We previously reported that Physcomitrium tRNA (cytosine38-C5)-methyltransferase, TRDMT1/DNMT2 catalyses C38, C48 and C49 methylation in tRNAAsp(GUC) and plays a crucial role in regulating transcription/stability of tRNAAsp(GUC) under oxidative stress. To gain insight into its precise mode of regulation, in the present study we show that moss TRDMT1/DNMT2 participates in broader networks of tRNA modification pathways. Using in silico methods we first identified that the yeast homologs of m7G46 methyltransferase Trm8 that catalyses m7G46 methylation, its obligate partner protein Trm82 and the Queuine tRNA ribosyl transferase (QTRT1) exist in a functional network with PpTRDMT1/PpDNMT2. To examine genetic interaction between PpTRDMT1/PpDNMT2, Trm8-82 and QTRT1, PpTRDMT1/PpDNMT2 loss-of-function mutants (ppdnmt2) and ppdnmt2 rescued lines (PpDNMT2-c) were used and transcript abundance of each gene was examined by qRT-PCR in background of these plants. Thereafter, physical interaction between PpTrm8/8 L1/8 L2 and PpQTRT1 with specific catalytic motifs in PpTRDMT1/PpDNMT2 were studied by yeast two-hybrid assay. The observed protein-protein interaction was also supported by in silico analysis of docked complexes of stretches of PpTRDMT1/PpDNMT2 and the Trm8 homologs. Alanine scanning mutagenesis study identified Threonine 11 in PpTRDMT1/PpDNMT2 located close to catalytic motif IV to be crucial for stable complex formation between PpTRDMT1/PpDNMT2 and its binding partners. On the basis of the results obtained we propose that pathways leading to m7G46 modification by tRNA-Guanine-N-7-methyltransferases and incorporation of Queuine by QTRT1 may be modulated at transcriptional/post-transcriptional levels by PpTRDMT1/PpDNMT2 function in P. patens. SIGNIFICANCE STATEMENT: tRNA modifying enzymes form a network in the moss P. patens.

The global rise of drug-resistant Mycobacterium tuberculosis (Mtb) underscores an urgent need for antitubercular agents with novel targets and mechanisms of action. Among these, the de novo purine biosynthesis pathway is essential for Mtb growth and survival, making its constituent enzymes attractive targets for therapeutic intervention. Within this pathway, adenylosuccinate (ADS) synthetase (ADSS) Rv0357c catalyzes the first committed step in biosynthesis of adenosine monophosphate (AMP) by converting inosine monophosphate (IMP) to ADS through a GTP-dependent reaction with l-aspartate. Despite its importance, Mtb ADSS remains poorly characterized at the biochemical level. In this study, we report the expression, purification, and enzymatic characterization of recombinant Mtb ADSS. To overcome the challenge of the enzyme being predominantly expressed as inclusion bodies in Escherichia coli, we established both protein refolding and chaperone-assisted expression strategies to obtain soluble, catalytically active protein. Using complementary spectrophotometric, colorimetric, and fluorescence-based assays, we determined steady-state kinetic parameters and confirmed robust ADSS activity consistent with Michaelis-Menten behaviour. Furthermore, we developed scalable, nonradioactive assays compatible with high-throughput screening (HTS), enabling the quantitative monitoring of ADSS activity via GTP hydrolysis and phosphate release. As a proof of concept, the MESG assay successfully detected inhibition of Mtb ADSS by the previously reported ADSS inhibitor Aurodox, demonstrating its utility for inhibitor characterization and screening. Collectively, these results provide the first comprehensive biochemical framework for studying Mtb ADSS and establish a foundation for structure-guided inhibitor discovery targeting purine biosynthesis as a novel antitubercular strategy.

The death of photoreceptors is a primary driver of retinal degenerative diseases, leading to irreversible vision loss. In Retinitis Pigmentosa (RP), a wide spectrum of mutations has been identified. Among these, the Crumbs (CRB) family of proteins, comprising CRB1, CRB2, and CRB3, plays a critical role in maintaining retinal homeostasis. The distribution of CRB mutations indicates population-specific variations, which may be influenced by factors such as founder effects, consanguinity, and geographic isolation. These findings suggest that genetic diagnostics and therapeutic strategies could benefit from considering population diversity. However, genotype-phenotype correlations remain complex, suggesting a modulatory role for genetic modifiers and environmental factors. Dysfunction of the CRB proteins disrupts apicobasal polarity of Retinal Pigment Epithelia (RPE) and photoreceptors, weakening their interaction and impairing phototransduction. This review highlights how mutations in conserved domains, especially the Epidermal Growth Factor (EGF)-like and Laminin G-like regions, compromise structural integrity and trigger degenerative cascades, establishing them as critical biomarkers and promising therapeutic targets for retinal degenerations.

Serine hydroxymethyltransferase (SHMT) catalyzes the conversion of l-serine and tetrahydrofolate (THF) to glycine and 5,10-methylenetetrahydrofolate, respectively. In a previous study, we found that human and Escherichia coli SHMTs possess THF-dependent d-serine dehydratase activity, which degrades d-serine to pyruvate and ammonia. Some activities including aldolase and racemase activities have also been reported for SHMTs. In the present study, we investigated the multifunctionality of E. coli SHMT and two human SHMTs. All three SHMTs displayed high aldolase activity toward l-allo-threonine and l-threo-phenylserine, and measurable activity toward l-threonine, but they did not act on d-allo-threonine and d-threonine. The catalytic efficiency (kcat/Km) for l-allo-threonine was higher than for l-threo-phenylserine. None of the SHMTs displayed racemase activity toward various amino acids, although slight alanine racemase activity was detected for E. coli SHMT. Likewise, none of the SHMTs showed lyase, aminotransferase, or aspartate decarboxylase activities, and none exhibited dehydratase activity toward other hydroxy amino acids except d-serine. SHMTs are multifunctional enzymes possessing canonical hydroxymethyltransferase, d-serine dehydratase, and low-specificity l-threonine aldolase activities.

Platelet-rich fibrin (PRF) has emerged as a pivotal autologous biomaterial in regenerative medicine. Yet, comparative proteomic insights into its diverse formulations advanced PRF (A-PRF), injectable PRF (I-PRF), and titanium-prepared PRF (T-PRF) remain scarce. This study presents a comprehensive proteomic characterization of A-PRF, I-PRF, and T-PRF, employing imputed intensity data, principal component analysis (PCA), correlation matrices, and differential expression analysis. PCA identified distinct clustering patterns, reinforcing reproducible and formulation-specific proteomic signatures. Correlation and intensity distribution analyses demonstrated strong intra-group consistency, with A-PRF exhibiting the highest reproducibility, whereas T-PRF displayed greater variability. Differential expression analysis further delineated significant inter-group molecular variations, revealing unique proteomic compositions. Protein-protein interaction (PPI) network analysis identified key regulatory proteins such as fibrinogen alpha (FGA), fibrinogen beta (FGB), and fibronectin 1 (FN1), In contrast, enrichment analyses revealed biological processes related to coagulation, platelet activation, immune modulation, and extracellular matrix dynamics. Functional pathway mapping underscored divergent biological roles, A-PRF was enriched in coagulation-related pathways, while I-PRF linked to lipid metabolism and immune signaling. These findings validate the molecular distinctiveness of PRF variants and establish a proteomic framework for their precision-driven application in tissue engineering and regenerative therapy.

Cellular process relies on specific binding of drug molecules with desired biological receptors. Biomolecular receptors and drugs exhibit their biological functions upon their interactions. Understanding the binding parameters and energetics of the binding provides a plethora of information that may be helpful in drug design and discovery. Further, drug interaction with carrier proteins affects the pharmacokinetics and dynamics of other exogenous and endogenous drugs. In this work, we report the binding behavior of esomeprazole with human serum albumin using several biophysical techniques. Qualitative and quantitative aspects of the binding along with the binding energetics has been focused in extracting the thermodynamics parameters and key interaction force. The binding constants (Kb)obtained from Scatchard analysis are-1.17 × 105, 7.49 × 104, and 3.23 × 104 M-1 at 298, 303, 308 K respectively. Esomeprazole binds preferentially at site 1 in subdomain IIA of human serum albumin and was confirmed, supported by site-specific marker displacement studies and docking simulations. Negative value of change in free energy (∆G0) -32 kJ/mol at 298 K showed thermodynamically favourable interaction and outweigh of entropic factor (T∆S0 = 230.76 ± 3 kJ for T = 298 K) over the enthalpic contribution (∆H0 = -261.99 kJ/mol) revealed an entropy-driven process. Binding of esomeprazole affected the helical structure of human serum albumin. Molecular docking studies (theoretical) shows the binding pocket of ESM at site1(IIA), which is in accordance with the experimental result. Further, the interface residues involved in the binding were analysed from the 2D diagram and ligplot of the docked complex.

Dual-specificity phosphatases (DUSPs) require two conserved motifs, the HCX₅R nucleophilic loop and a WPD/FPD-type general-acid loop, to support cysteine-dependent dephosphorylation. Although annotated as a DUSP, the catalytic competence of DUSP15 has remained ambiguous, with only weak activity reported against artificial substrates and paradoxical roles in sustaining ERK and Jak1-STAT3 signalling. Here, sequence analysis, crystallographic inspection, structural modelling, evolutionary profiling, interaction-network inference, and molecular dynamics (MD) simulations are integrated to reassess the functional properties of DUSP15. Motif analysis identifies two defining deviations: a phenylalanine immediately following the catalytic cysteine within a divergent HCFAGISR loop, and complete absence of a WPD/FPD-type general-acid loop. Structural examination of a DUSP15 crystal fragment, together with AlphaFold predictions, shows that the inserted phenylalanine projects into and sterically occludes the active-site cleft, in contrast to the open catalytic pocket of the active phosphatase DUSP7. Comparative analysis of 11 mammalian orthologs reveals absolute conservation of both anomalies, indicating long-standing selective maintenance of a catalytically divergent architecture. 100-ns all-atom MD simulations reveal a globally stable and compact fold with a conformationally rigid, tightly packed, and selectively dehydrated catalytic motif, lacking the flexibility and solvent accessibility typically required for productive cysteine-based catalysis. Comparative MD simulations performed under identical conditions further distinguish DUSP15 from the catalytically competent phosphatase DUSP7. Interaction-network analysis places DUSP15 within phosphatase-, transcriptional-, and metabolism-associated modules, consistent with scaffold-like regulatory roles. Together, these convergent structural, evolutionary, and dynamical features support a model in which DUSP15 functions predominantly as a non-catalytic adaptor, providing a mechanistic framework for its non-canonical regulation of ERK and Jak1-STAT3 signalling and its tumour-selective expression in chromophobe renal cell carcinoma.

Leishmania (Viannia) braziliensis Thor strain contains subpopulations Thor03, Thor10, and Thor22 with distinct biological and infection profiles in vitro and vivo. This study investigated GPI-anchored metalloproteases from the Thor strain and its subpopulations using phospholipase C treatment followed by Zn2+-charged affinity chromatography. An electrochemical biosensor based on screen-printed carbon electrodes and differential pulse voltammetry was used to detect metalloprotease activity and responsiveness to the ortho-phenanthroline inhibitor. Promastigotes and axenic amastigotes metalloproteases exhibit different binding profiles to the ortho-phenanthroline inhibitor which led to specific discrimination of these analytes. The highest sensitivity was observed in proteases from Thor10 promastigotes and Thor axenic amastigotes, with detection limits of 5 μM and 1 μM, respectively. The detectable concentration range for these analytes varied among subpopulations: for promastigotes, from 1000 μM down to 5 μM (with Thor10 showing the broadest range), and for axenic amastigotes, from 1000 μM down to 1 μM (with Thor and Thor10 exhibiting the greatest sensitivity). These differences suggest a variable abundance of metalloproteases among subpopulations. The findings underscore the biosensor's potential as a sensitive, specific tool for real-time analysis of Leishmania enzymes, offering a novel, systematic approach for studying enzyme function and virulence in parasite phenotypes.

Liquid-liquid phase separation (LLPS) and biomolecular condensates formation in crowded bio-milieus are of paramount importance for cell compartmentalization. A topic of biophysics relevance is the responsiveness of the condensates to environmental stimuli. Here, we investigated solutions of bovine serum albumin (BSA) and polyethylene glycol of average molecular weight of 5000 Da (PEG5k) subject up to nc = 7 consecutive cooling cycles. The LLPS and condensate formation were assessed by temperature-dependent optical density measurements at 400 nm (OD400) and by microscopy observations, respectively. Insight into the dynamics of the homogeneous and condensed phases of BSA/PEG5k/buffer solutions was obtained by electron paramagnetic resonance spectroscopy. Freshly prepared ternary solutions are characterized by high OD400 over the whole temperature range and protein droplets with diameters from few to tens of micrometers. BSA in PEG5k containing solutions is more densely packed and exhibits restricted dynamics with respect to the PEG5k-free protein. Well-defined LLPS with upper critical solution temperature profiles are recorded in BSA/PEG5k/buffer solutions for nc ≥ 2. Homogeneous solutions, with nearly zero optical density and absence of droplets, convert into the condensed state, with high optical density and presence of droplets, on decreasing the temperature. The LLPS temperature progressively decreases from approximately 35 °C at nc = 2 to approximately 21 °C at nc = 7, whereas the morphology, packing and dynamics of the droplets are slightly affected by nc. Moreover, coacervates in the dense and lean phases show similar microscopic and dynamic features. The overall findings highlight the effectiveness of temperature stimuli in modulating the LLPS in protein solutions.

Study of protein-protein interaction (PPI) network is fundamental to all cellular processes in living organisms. PPI study based on experimental techniques such as high-throughput assays, mass spectrometry is time-consuming and expensive. Computational techniques like molecular docking are restricted to availability of 3D protein structures and not suitable for large numbers of proteins. On the contrary Sequence-based PPI study is advantageous over the limitations of above techniques. Sequence-based PPI study has gained a broader scope with the help of deep learning techniques and large language model (LLM). In this study, we proposed AttnSeq-PPI, a deep learning framework based on two channels of hybrid attention mechanism. The protein sequences are embedded in high dimensional space using ProtT5 language model. In our model hybrid attention mechanism is designed by combining self-attention and cross-attention which enable the model to extract features from each protein with respect to the contextual features of both the proteins. The hybrid attention mechanism effectively captures long-range dependencies within protein sequences as well as interacting features of both proteins. The model was trained and evaluated based on 5-fold cross validation on intra-species and multi-species datasets. Additionally, four datasets of independent species and true PPI network datasets were used for validation. AttnSeq-PPI demonstrates superior generalization and outperforms existing models, achieving 99 % accuracy for both human and multi-species datasets. It can provide prediction of novel PPIs with fewer false negatives and higher precision. Additionally, we developed a web-based tool on AttnSeq-PPI accessible at https://compbiosysnbu.in/attnseqppi/ to provide PPI prediction based on protein sequences.

FOXP2 and PAX6 are transcription factors essential for neural development, with mutations in both linked to autism spectrum disorders (ASDs). Their DNA-binding domains include a forkhead domain (FHD) for FOXP2 and a paired domain (PD) plus homeodomain (HD) for PAX6. We investigated whether the FOXP2 FHD interacts directly with PAX6 PD or HD, and how such interactions influence DNA binding. Fluorescence anisotropy showed that all three domains bind specifically to their respective DNA targets with similar affinities. The FOXP2 FHD also interacts directly with both PAX6 PD and HD, with low micromolar binding affinities. Despite its stronger intrinsic DNA affinity, the FHD was displaced from its target DNA by both PAX6 domains, suggesting that protein-protein interactions can override DNA affinity under competitive conditions. In contrast, FOXP2 could not displace PD or HD from their DNA targets. Molecular docking supported these findings: DNA-protein interfaces were largely unchanged by the second protein, but protein-protein interfaces were strongly influenced by DNA occupancy. The H3 helix of FHD was identified as a central point for assembly, contributing to both DNA and protein interfaces. When FHD was bound to DNA, H3 was occupied, forcing PD or HD to dock at alternative, less optimal sites. HD maintained stronger contacts in these rearranged states, consistent with its greater competitive strength. This asymmetric interplay indicates competitive dominance by PAX6 and suggests mechanisms that could underlie transcriptional regulation in neurodevelopment.

Immunoglobulin light chains (LCs) exhibit diverse aggregation behaviours that depend sensitively on sequence composition and intermolecular interactions. Understanding how specific residues modulate aggregation kinetics remains a key challenge in elucidating the molecular basis of light-chain amyloidosis. Here, we investigate sequence-dependent aggregation using recombinant λ LCs derived from the IGLV2 gene family. Comparison of two closely related LCs differing by only 16 amino acids revealed striking differences in aggregation behaviour under thermal stress. Bioinformatic analysis identified an additional aggregation-prone segment in the CDR1 region of the aggregation-prone M10 variant, associated with residues Ser33 and Tyr34. Rational substitution of these residues (S33D/Y34S) markedly reduced aggregation while leaving the thermal transition temperature largely unchanged (∼53 °C). Differential scanning calorimetry revealed that the wild-type M10 LC unfolds with a significantly lower apparent activation energy (∼290 kJ/mol) compared with the non-aggregating H9 (∼605 kJ/mol) and the stabilised double mutant (∼560 kJ/mol), indicating reduced kinetic stability. Aggregation of unfolded species showed much weaker temperature dependence (Ea ≈ 10-70 kJ/mol) and exhibited strong concentration dependence consistent with a multimolecular association process. Additional experiments suggest that aromatic interactions involving Tyr34 contribute to the stabilisation of intermolecular assemblies. Together, these results establish a quantitative link between local sequence variation in the CDR1 region, kinetic stability of the LC fold, and aggregation propensity, highlighting how targeted mutations can modulate aggregation behaviour in immunoglobulin light chains.

Male infertility affects 16.9% of couples and accounts for up to 30% of infertility cases. Its causes include genetic, acquired, and idiopathic factors, as well as environmental exposures and lifestyle habits like smoking and poor diet. Conventional semen analysis remains the gold standard but provides limited insight into molecular mechanisms and often fails to explain subfertility. Fertilization requires complex biochemical changes in spermatozoa, such as capacitation and the acrosome reaction, dependent on protein and glycoprotein interactions. Altered glycosylation patterns are linked to impaired capacitation, reduced fertilization potential, and disrupted spermatozoa-oocyte interactions. Specific glycoproteins, including clusterin, fibronectin, glycodelin-S, and others, are emerging as diagnostic and prognostic biomarkers. This review focuses on the structure, function, and glycosylation of glycoproteins in human semen, emphasizing their impact on male reproductive health and fertility. Their characterization offers new insights for understanding infertility and developing biomarkers for improved spermatozoa selection in assisted reproductive technologies.

Lung adenocarcinoma remains a major challenge in cancer research due to its complex molecular underpinnings. In this study, we developed an integrated machine learning framework to identify novel driver genes associated with lung adenocarcinoma by leveraging multi-omics data. We curated gene candidates from methylation, RNAseq, mutation, and miRNA levels, and mapped them onto a protein-protein interaction network from STRING to generate informative feature vectors using a node2vec method. Furthermore, they were also represented by GO and KEGG enrichment features. All features were then refined through a multi-step process, beginning with the Boruta algorithm for filtering and followed by the minimum redundancy maximum relevance method for ranking. An incremental feature selection strategy was employed to determine the optimal feature subsets, which were used to build predictive models with random forest and support vector machine classifiers. To address class imbalance, synthetic sampling was applied, and ten-fold cross-validation ensured model robustness. Consequently, we predicted 428, 105, 1039, and 1748 potential lung adenocarcinoma driver genes for RNAseq, methylation, mutation, and miRNA levels, respectively. Integrated analysis of overlapping gene sets further highlighted key candidates, including PQLC3, FAM192A, FAM83D, SPRED1, SFTPB, and TM4SF5, with high composite probability scores. Some identified genes may be the driver genes of lung adenocarcinoma and have some druggable potential. These findings provide new insights into the molecular mechanisms of lung adenocarcinoma and suggest promising targets for future diagnostic and therapeutic strategies.

Manganese superoxide dismutase (MnSOD), localized in the mitochondrial matrix, is considered pivotal enzyme in the mitochondrial powerhouse. This is primarily due to its central role in the scavenging of excess superoxide anions (O2-) produced during the electron transport chain. In this study, a highly thermostable MnSOD from Lantana camara (LcMnSOD) was identified via SOD activity assays. Isozyme profiling and thermostability assays demonstrated a high thermal stability of LcMnSOD as it was found to be active even at the temperature exceeding 80 °C. Full-length cDNA (944 bp) encoding LcMnSOD (675 bp) was amplified by rapid amplification of cDNA ends (RACE), followed by recombinant expression in Escherichia coli and further purification via affinity chromatography. The purified enzyme (~24 kDa) exhibited Km value of 0.026 ± 0.001 μM, Vmax value of 266.29 ± 10.42 Units/mg and was functional across a broad pH (5.0-9.0) and temperature (4-70 °C) range. LcMnSOD existed as a homotetramer and had a high α-helical content, as confirmed by in silico and circular dichroism (CD) analysis. Interestingly, LcMnSOD was resistant to heat inactivation at 80 °C (kd = 0.0063 ± 0.0004 min-1, t1/2 = 116.48 ± 13.96 min), and was also stable to varying concentrations of denaturants, inhibitors, and reducing agents. LcMnSOD could maintain its activity at high temperatures by maintaining a dense hydrogen-bonding network, a robust hydrophobic core, and structural integrity of its catalytic site, ensuring accessibility of substrate-binding residues, as revealed by Molecular dynamics (MD) simulations studies. Owing to its remarkable stability and functional robustness, LcMnSOD holds promise for biotechnological applications.