Despite its role in healthcare delivery and accessibility, the potential of bioinformatics to transform disease diagnosis in SSA remains unknown. This narrative review aims to assess the current status of bioinformatics initiatives in SSA and evaluate the role of bioinformatics in transforming diagnosis of major diseases in the region. A narrative review of peer-reviewed literature, policy reports, and program documentation published between 2000 and 2025 was conducted. Sources were identified through targeted database searches and organizational websites (e.g., Africa CDC, H3Africa, genome.gov, etc.). Key themes were synthesized, including bioinformatics pipelines, infrastructure, training and capacity development, integration of sequencing technologies, and disease-specific applications. Bioinformatics capacity in SSA has expanded primarily through international collaborations and regional networks such as H3Africa and Africa CDC's Pathogen Genomics Initiative. While significant advances in training and expertise are evident, sequencing infrastructure and data management systems remain unevenly distributed. For priority diseases-including malaria, typhoid, HIV, tuberculosis, and neglected tropical diseases-bioinformatics applications show promise in detecting drug resistance, guiding treatment, and enabling genomic surveillance. However, infrastructural barriers such as limited wet-laboratory facilities, data storage capacity, and internet connectivity constrain implementation. Bioinformatics-based diagnostics have the potential to transform healthcare delivery in SSA by enabling more accurate, timely, and context-specific disease diagnosis. Realizing this potential requires simultaneous investment in sequencing infrastructure, sustainable bioinformatics training, and policies that support data sharing and integration into clinical practice. Strengthening these systems could reduce diagnostic inequities, empower African-led research, and advance the region toward self-reliant and equitable healthcare.
Bioinformatics has transformed modern virology by linking genomic variation to epidemiology, protein structure, and public health action. This review integrates core analytical frameworks-sequence alignment and genome annotation; maximum-likelihood and Bayesian phylogenetic/phylodynamic inference; codon-based selection and recombination analyses; and AI-assisted structural prediction combined with deep mutational scanning (DMS)-to convert viral sequences into mechanistic and predictive insight. We emphasize how global surveillance ecosystems (GISRS, GISAID, and Nextstrain) and sustained regional programs reveal genotype turnover, antigenic drift, and seasonality in RSV, HPIV, norovirus, and SARS-CoV-2, enabling near real-time lineage tracking and vaccine-strain deliberation. Mapping positively selected residues and recombination breakpoints onto three-dimensional protein structures clarifies immune escape in key surface glycoproteins (e.g., RSV F/G, HPIV HN/F, norovirus VP1) and strengthens genotype-phenotype interpretation. Comparative reinfection patterns-lifelong immunity in measles versus recurrent RSV/HPIV infections-illustrate how evolutionary rate and antigenic constraint shape population immunity and control strategies. Despite major advances, progress remains constrained by geographic sampling bias, incomplete metadata, uneven computational capacity, and uncertainties in molecular clocks, recombination inference, and machine-learning predictions. The field is now moving toward predictive virology, integrating AI-enabled structural modeling, mutational fitness landscapes, and clinical-immunological metadata within real-time analytical platforms to anticipate immune-escape trajectories. Prioritizing pediatric respiratory pathogens alongside influenza and coronaviruses, and reinforcing equitable data-sharing and governance, will be essential for globally inclusive, forward-looking viral surveillance and intervention.
From animals to plants, the proposed competing endogenous RNAs (ceRNAs) mechanism has expanded our understanding of gene regulation, suggesting a complex "communication network" involving RNA molecules. In plant gene regulation, ceRNAs studies are increasingly uncovering new insights into the precision and complexity of the mechanisms controlling gene expression. Research suggests that ceRNAs may play critical fine-tuning roles in plant life activities, ranging from growth and development to stress responses. However, plant ceRNAs appear to differ from animal ceRNAs in terms of sequence characteristics, interaction modes, and functions. This review provides an overview on the research conducted to date on plant ceRNAs, revealing key differences between animals and plants and summarizing the progress in research on the relationships between ceRNAs and stress responses (e.g., drought, salinity, and disease) as well as signal transduction and hormone regulation. Moreover, research methods that combine cutting-edge technologies (such as single-cell sequencing and spatial transcriptomics) with multi-omics tools to comprehensively investigate plant ceRNAs are discussed. Our analysis indicates that ceRNAs networks are characterized by plant-specific features, posing distinct research challenges. These range from the limited predictive accuracy of existing bioinformatics tools to the difficulties in functional validation within complex plant tissues. To overcome these hurdles, we propose that future research must prioritize the creation of plant-optimized prediction models and the integration of multi-omics data within a spatially resolved context. Ultimately, unraveling plant ceRNAs networks will not only fill a critical knowledge gap in post-transcriptional regulation, but also unlock novel strategies for crop improvement.
Microalgae and cyanobacteria have attracted significant interest from green biotechnology and the bioeconomy due to their numerous beneficial qualities: extremely high growth rate compared to vascular plants; independence from arable land or clean water; ability to produce a variety of industrially important bioproducts, such as biofuels, food and feed additives, and substances for the cosmetics and pharmaceutical industries. They also offer the possibility of combining bioproduction with industrial carbon dioxide capture and wastewater utilization. One of the main limitations to the widespread use microalgae is the economic feasibility of their cultivation. Consequently, researchers in the field are primarily concerned with the identification of novel high-productive strains and the augmentation of biomass and/or target metabolite yield. Omics technologies are the key to unlocking the biotechnological potential of photosynthetic microorganisms, enabling significant acceleration in the identification of novel, efficient strains and providing valuable insights into the microbial metabolism. The present review focuses on the latest advances in the field of omics-driven research of photosynthetic microorganisms and provides a comprehensive outlook of practical instruments, such as open-access omics databases and stoichiometric metabolic models, that can improve the efficiency of biotechnological research pipelines to achieve breakthroughs in the industrial application of microalgae and cyanobacteria. 1. Omics unlocks algal potential for biofuels, pharma, food, and remediation2. Multi-omics enable shift from costly iterative screening to predictive approach3. Omics-driven databases and models enhance biotech research pipelines.
Laccases are the most common multicopper oxidase (EC 1.10.3.2); as a result, they have attracted great interest as natural cleaners for environmental bioremediation purposes. However, the fragile structure and low recyclability of laccases, along with their high price, are constraints in practical applications. Enzyme immobilization has been identified as one of the best strategies to circumvent these problems and the application of nanomaterials for enzyme immobilization has resulted in nanobiocatalysts (NBCs). Nanomaterials have great advantages for the immobilization of enzymes attributed to their special chemical and physical properties, including large specific surface area, tunable pore size, and strong solid mechanical stability. With better stability, efficacy and specificity, these nano-engineered systems have been very successful in the treatment of a variety of pollutants ranging from persistent organic pollutants and emerging contaminations to industrial waste. Herein, we give an updated review of the most recent advances in laccase-based NBCs, with a particular focus on new strategies for nanomaterial functionalization and laccase enzyme immobilization. Additionally, this review aims to gather information about laccase's sources, mechanisms of action, substrates, and mediators, providing a useful point of reference. This review also compiles numerous recent discoveries about the biology of laccase and its applications as a nanobiocatalyst and provides a concise synopsis that aids researchers in comprehending its possibilities. It also emphasizes the possible use of laccase-based NBCs for bioremediation, especially in dealing with emerging pollutants (EPs) like antibiotics, pharmaceutical residues, textile effluents, and other xenobiotics.
Interleukin-15 (IL-15) has emerged as a central cytokine for next-generation cancer immunotherapy because of its unique ability to sustain the survival, proliferation, and cytotoxic function of memory CD8+ T cells and natural killer (NK) cells without promoting the expansion of regulatory T cells (Treg). These properties make IL-15 particularly attractive for achieving durable antitumor immunity, especially in solid tumors where immune persistence remains a major limitation. Although IL-15 shares the same signal-transducing receptor subunits (IL-2Rβ and the common γ chain) with interleukin-2 (IL-2), the two cytokines drive fundamentally different CD8+ T-cell fates, a distinction that underlies their markedly divergent therapeutic profiles in cancer immunotherapy. In recent years, multiple IL-15-based therapeutic strategies including recombinant IL-15, and IL-15 immunocytokines have entered clinical evaluation, demonstrating potent immune activation with manageable toxicity profiles. Recent clinical progress includes the FDA approval of Nogapendekin alfa inbakicept (N-803), the first IL-15-based immunotherapy approved for cancer treatment, alongside the advancement of other IL-15 superagonists into Phase II trials and growing evidence that IL-15 can enhance the efficacy of immune checkpoint blockade and engineered adoptive cell therapies such as CAR-T cells, CAR-NK cells, γδ T cells, and invariant NKT cells. Despite these advances, important challenges remain, including cytokine-associated toxicities, optimal delivery strategies, and the immunosuppressive tumor microenvironment. This review summarizes recent progress in IL-15-based cancer immunotherapy, integrates emerging insights into IL-2Rβγ-driven CD8+ T-cell fate decisions, and discusses key opportunities and challenges for translating IL-15-mediated immune enhancement into durable clinical benefit.
T cell recognition of malignant cells is central to cancer immunotherapy. This process is elicited by interactions between T cell receptors (TCRs) and antigenic peptides displayed on major histocompatibility complex molecules. Sequencing technologies enable characterization of genomic, transcriptomic and epigenetic alterations that can give rise to epitopes in cancer cells, alongside TCR repertoire profiling in T cells. An important challenge is to determine which peptides are recognized by T cells and which TCRs mediate this recognition. This Perspective highlights how technological and computational advances have improved epitope predictions, shed light on TCR-epitope recognition and could help leverage TCR repertoires for therapeutic innovations in cancer immunotherapy.
Exosomes are nanoscale extracellular vesicles (EVs) that mediate intercellular communication and carry proteins, lipids, mRNAs, and non-coding RNAs reflective of their parental cells. Their biogenesis, molecular composition, and ability to traverse physiological barriers, including the blood-brain barrier, position exosomes as powerful candidates for biomarker development and therapeutic delivery in neurodegenerative diseases (NDDs). In Alzheimer's disease, Parkinson's disease, multiple sclerosis, and prion disorders, exosomes not only mirror pathological processes but actively participate in the propagation of misfolded proteins and neuroinflammatory signals through cell-type-specific vesicle subpopulations. This review synthesises current advances in exosome biology, cargo sorting, release mechanisms, and pathophysiological roles in the central nervous system, with emphasis on how neuron-, astrocyte-, and microglia-derived exosomes diverge in their cargo profiles and functional consequences across diseases. We highlight disease-specific exosomal signatures, including amyloid-β (Aβ), tau, α-synuclein, myelin proteins, prion proteins (PrP) and regulatory microRNAs. We evaluate emerging technologies such as microfluidic isolation, single-vesicle analysis, and multi-omics profiling that are accelerating biomarker discovery, and review exosome-based therapeutic strategies, including native stem cell-derived exosomes and surface-engineered vesicles loaded with neuroprotective miRNAs, small molecules, and gene-editing cargo. We address critical unmet challenges in translating these approaches to the clinic, including scalable and standardised production, incomplete pharmacokinetic /pharmacodynamic characterisation in preclinical models, immunogenicity and off-target safety concerns, and the absence of specific regulatory guidance for EV drug products. Together, these insights highlight the transformative potential of exosomes as both precision diagnostic tools and disease-modifying therapeutic platforms for NDDs.
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Genetically engineered plants incorporate the use of a novel bioreactor known as molecular pharming, which has a transformative view on the pharmaceutical industry. The technique enables mass production, at a low cost, and reproducibly of a large number of different protein-based drugs, vaccines, and industrial enzymes. This review-based study outlines the chronological evolution of molecular pharming, investigates its essential principles and elective applications, and meticulously compares it with other methods, namely conventional biomanufacturing. We present the numerous host organisms employed, the leading-edge genetic engineering procedure, and the sophisticated approaches for protein purification and extraction. Additionally, we deliver in-depth analysis of the noteworthy advantages that take place in molecular pharming as a captivating substitute, in conjunction with obstinate challenges, including concerns of public insights, intricate regulatory frameworks, and consideration for economic sustainability. Finally, this comprehensive study explores the promising direction, evolving innovations, and essential areas that influence future research to fully reveal the extensive potential of plant-based biopharmaceutical production for industrial strains and global health.
A central enigma in crop improvement lies in introducing beneficial traits without fitness trade-offs. Rice, the cornerstone of global food security, demands multifaceted genetic innovation to sustain yield, quality, and resilience in the face of mounting climatic constraints. With the global population projected to surpass ten billion, functional master regulators such as miRNAs stand out as transformative molecular tools, capable of orchestrating complex trait networks and offering a tangible path toward the next green revolution. These are robust fine-tuners that orchestrate a myriad of functional processes and provide a value addition in emerging technologies such as assisted breeding, genome editing, and genomic selection to make rice production feasible. Herein, we have provided a comprehensive synthesis and updates on functional miRNA-mediated agronomically advantageous trait improvement exclusively for rice. It represents the latest functional understanding of miRNAs and their involvement as signatures of domestication and divergence processes, in support of the previously established notion and recent updates on emerging miRNA-assisted resources and technologies, such as their application as artificial solutions for improving genotypes, coding, and dietary potentialities for environmental safeguards and innovative biotherapeutics. Recent updates signify their robust cross-kingdom communicators' potential for multifaceted non-host dialoguing, and their integrative action relies on the coordination with other non-coding regulatory elements for various downstream trait regulation. Moreover, specific highlights refer to the application of miRNAs for rice agronomical trait improvements, broadly classified into three functional domains, viz., biotic and abiotic stresses and yield and quality traits. Such updated functional aspects of different miRNA modules would strengthen rice improvement by facilitating a foundation and future roadmap for miRNA-mediated trait discovery and improvement.
Fusarium crown rot (FCR) is a devastating soil-borne disease of wheat, primarily caused by Fusarium pseudograminearum, Fusarium graminearum and Fusarium culmorum. It causes substantial yield losses worldwide and contaminates grains with mycotoxins, posing major threats to food and feed safety. Given the lack of effective resistance in existing wheat cultivars, elucidating FCR resistance mechanisms and accelerating genetic improvement are of paramount importance. This review synthesises and evaluates the progress over the past 5 years, highlighting advances in the identification of major resistance loci from wheat and its wild relatives, as well as the assessment of their diversity. Advances in multi-omics approaches have underscored a sophisticated defence network, involving transcriptional reprogramming and metabolic remodelling. Furthermore, functional studies have identified key genes that enhance FCR resistance by modulating cell wall integrity, maintaining reactive oxygen species homeostasis and reprogramming defence metabolism, phytohormone pathways, as well as the transcriptome. Finally, we outline future research directions, including the establishment of standardised FCR phenotyping systems, the employment of gene editing technologies and artificial intelligence and the elucidation of the regulatory networks underlying FCR resistance. We conclude that a multi-disciplinary approach, integrating precision phenotyping, bioinformatics and genetics, is essential for overcoming key biological constraints in FCR resistance breeding and ensuring global wheat security.
With the intensified exploration of marine resources, marine bioactive peptides have become one of the research focuses in biomedicine, food science, and materials science because of their structural diversity, unique biological activities, and broad application potential. At present, the extraction of marine peptides has expanded beyond conventional chemical extraction and enzymatic hydrolysis, with microbial fermentation and gastrointestinal simulation technologies further broadening peptide diversity. In addition, the integration of multiple chromatographic techniques with advanced detectors has significantly improved the efficiency of marine peptide identification. Owing to their diverse biological activities, including immunoregulatory, antioxidant, antibacterial, antitumor, hypotensive, and hypoglycemic effects, marine peptides not only enrich the pool of candidates for marine drug development but also provide new perspectives for addressing numerous health challenges. Importantly, substantial progress has been made in the screening, identification, and mechanistic elucidation of marine bioactive peptides, driven by advances in high-throughput technologies and the bioinformatics. However, marine peptide research still faces several challenges, including complex sourcing, difficulties in large-scale acquisition, and insufficient exploration of biological activities. Therefore, this article concisely reviews recent progress in the extraction, purification, and identification of marine bioactive peptides, summarizes current research on their biological activities, and highlights the application of bioinformatics in marine peptide studies.
Despite advances in acute ischemic stroke (AIS) research, identifying reliable biomarkers and regulatory mechanisms remains challenging. We first identified AIS-related genes via extensive literature review, retrieved dataset GSE16561 from the Gene Expression Omnibus (GEO, https://ncbi.nlm.nih.gov/geo/), and performed differential/enrichment analyses. Bioinformatics verified N6-methyladenosine (m6A) sites in target genes, focusing on the methyltransferase-like protein 14 (METTL14)/growth arrest and DNA damage-inducible β (GADD45B) m6A methylation/brain-derived neurotrophic factor (BDNF) axis. Peripheral blood samples, biochemical indicators, and demographic data were collected from Hongqi Hospital Affiliated to Mudanjiang Medical University. Human umbilical vein endothelial cells (HUVECs) underwent transfection and oxygen-glucose deprivation (OGD). METTL14, GADD45B, and BDNF were detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR); BDNF protein by enzyme-linked immunosorbent assay (ELISA); global RNA m6A by Dot Blot; and GADD45B m6A by methylated RNA immunoprecipitation-quantitative polymerase chain reaction (MeRIP-RT-qPCR). Differential, diagnostic efficacy, logistic regression analyses, and nomogram validation were conducted. AIS patients showed increased METTL14, decreased GADD45B/BDNF, and increased levels of global m6A RNA and GADD45B m6A RNA (P < 0.05). Receiver operating characteristic (ROC) analysis confirmed the three genes' good diagnostic efficacy. The nomogram integrating these genes, globulin (GLOB), diabetes, high-density lipoprotein cholesterol (HDL-C), and hypertension performed excellently. This study highlights METTL14, GADD45B, and BDNF as key AIS biomarkers; METTL14 may indirectly regulate BDNF via GADD45B m6A methylation, providing potential therapeutic targets and novel mechanistic insights.
Chemotherapy in breast cancer (BC) can substantially affect mental wellness. Advances in metabolomics enable comprehensive profiling of metabolic changes over time during and after treatment, offering insights into biological mechanisms linking chemotherapy to mental health outcomes. To study the association between metabolite profiles and mental wellness, correlation-based analyses are particularly useful. Spearman's rho is a widely used correlation measure and popular alternative to Pearson's correlation, since it also applies to non-linear association between variables. However, existing methods are not designed for longitudinal data and do not allow for covariate adjustments. In this paper, we propose a novel regression-based framework grounded in a class of semiparametric models, the functional response models, to extend this popular correlation measure to longitudinal settings with missing data under the missing at random assumption. This framework facilitates inferences about temporal changes in correlations over time and association of explanatory variables for such changes. We use simulation studies to evaluate performance of the approach with moderate sample sizes. We apply the approach to a one-year longitudinal substudy of the EPIGEN study to examine the longitudinal association between metabolite profiles and mental wellness in BC patients undergoing chemotherapy. The identified metabolites may serve as candidates for future in-depth bioinformatics analyses and translational investigations.
Single-cell RNA sequencing has transformed immunological research by enabling high-resolution transcriptional profiling of individual immune cells. Despite its transformative impact, annotating immune cells based solely on transcriptomic data remains challenging. These difficulties arise from biological factors, including gene expression heterogeneity and post-transcriptional regulation, as well as technical limitations that contribute to mismatches between mRNA and protein expression. Such discrepancies may lead to cell misclassification and obscure functional insights, particularly in heterogeneous populations such as peripheral blood mononuclear cells. This review highlights the major challenges in immune cell annotation by detailing the mechanisms underlying mRNA-protein discrepancies, examining both the biological factors and technical artifacts that drive this divergence, and emphasizing their implications for accurate cell classification. A critical overview of current single-cell profiling technologies follows, with evaluation of the respective advantages and limitations of transcriptomic, proteomic, and multimodal approaches. In particular, technologies such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing integrate transcriptomic and proteomic data, addressing the shortcomings of single-modality analyses. Further examination focuses on computational strategies for immune cell annotation, with emphasis on automated methods and bioinformatics frameworks tailored to multi-omics datasets. The unique computational challenges of integrating mRNA and protein data, together with solutions for improved annotation accuracy, are discussed. This review integrates key challenges, technologies, and computational tools, highlighting the need for standardized multimodal profiling of immune cells. Such integration enhances annotation reliability and advances disease understanding and therapy discovery.
Field-based environmental metabolomics offers a powerful tool for examining how organisms respond to complex mixtures of chemical and environmental stressors. When coupled with traditional ecotoxicology, metabolomics can help reveal mechanistic pathways; however, laboratory-based tests cannot fully replicate the dynamic, multifactorial conditions of natural ecosystems. This review synthesises current advances, challenges, and opportunities in applying metabolomics to ecotoxicology under real-world field conditions. We highlight the growing use of wild-caught organisms, caged exposures, mesocosms, and laboratory studies using field-collected samples to detect sub-lethal metabolic disruptions associated with contaminants such as PFAS, metals, organic pollutants, and wastewater-derived mixtures of compounds. Key themes include the sensitivity of metabolomics to early physiological changes, integration with complementary chemical and ecological data, the challenges in distinguishing natural variability from contaminant effects, the importance of establishing baselines and dose-response relationships, and the need for improved QA/QC and metadata reporting. As the methodological and logistical challenges are overcome, metabolomic profiles from field-exposed organisms are increasingly demonstrating value for environmental risk monitoring and forecasting. Environmental metabolomics has been successfully used for environmental monitoring, supporting regulatory frameworks, and identifying mechanistically grounded biomarkers of exposure and effects. However, to realise its full potential, coordinated efforts among current and future metabolomics practitioners are still needed to advance the current Metabolomics Standards Initiative (MSI) guidance. The MSI should, ideally, be expanded to include common standardised workflows, strengthen bioinformatics infrastructure, expand case studies, and fully embed and integrate metabolomics within routine environmental assessment and decision-making processes, thereby transitioning these 'academic' approaches into practical regulatory tools.
Ferritin, a natural iron-storage protein, has emerged as a versatile platform in nanotechnology and biomedicine due to its biocompatible 12 nm nanocage, intrinsic targeting via the transferrin receptor 1, and adaptability for diverse applications. This review integrates recent experimental and computational advances in ferritin-based nanoparticles, Ferritin is used for drug delivery, vaccine delivery, gene therapy, imaging and diagnostics, antioxidant therapy, and anti-inflammatory and neuroprotective therapies. Experimentally, ferritin nanocages achieve high-capacity loading (up to 400 molecules per cage) of therapeutics such as doxorubicin, siRNA, and CRISPR-Cas9 through pH-responsive disassembly, passive diffusion, and engineered self-assembly. Its natural TfR1 affinity enables precise tumor targeting and blood-brain barrier penetration, improving outcomes in cancers, infectious diseases, and neurological disorders. Computationally, molecular dynamics simulations predict stable antigen-ferritin interfaces. Density functional theory elucidates metal-oxide interactions in catalytic nanozymes. Machine learning classifiers leverage ferritin biomarkers for iron deficiency anemia detection, and bioinformatics tools like weighted gene co-expression network analysis and protein-protein interaction networks reveal ferritinophagy mechanisms in neurodegeneration and cancer. Docking-guided designs enhance vaccine epitope exposure and PROTAC degradation efficiency, fostering precision diagnostics and sustainable nanocarrier optimization. Despite promising preclinical results, challenges in scalability, long-term immunogenicity, and regulatory validation persist. This review highlights ferritin's revolutionary potential in nanomedicine, proposing future directions for AI-assisted design, personalized therapies, and sustainable nanotechnology to overcome barriers for clinical use.
Fungal immunomodulatory proteins (FIPs) are low-molecular-weight proteins from macrofungi that share the potential to modulate immune responses. Structurally, they fall into five subgroups, with the Fve-type and Cerato-type being the most representative. Originally, these proteins evolved in fungi for mycoparasitism and defense; their immunomodulatory, antitumor, anti-inflammatory, and hepatoprotective effects validated in human cells and animal models are surprisingly beneficial. Post-translational modifications and specific oligomeric states regulate the FIP functionality. These structural features critically govern receptor engagement and downstream signaling, whereby FIPs orchestrate immune responses via Toll-like receptor/NF-κB modulation and exert antitumor effects through EGF receptor/Akt interference. Recent advances in genomic mining and bioinformatics have accelerated novel FIP discovery, while scalable production is now achievable through optimized heterologous expression systems incorporating solubility-enhancing tags, promoter engineering, endotoxin removal, and tailored fermentation. This review examines the structure-activity relationships, mechanism-driven bioactivities, and bioproduction platforms of FIPs, highlighting their potential in biopharmaceutical and functional food applications.
Respiratory syncytial virus (RSV) infections have a high prevalence in young children, immunocompromised adults, and elderly, raising global concerns. Global RSV surveillance infers an 8%-27% mortality rate in preterm-born children, with 2.8 million/year. Continuous surveillance studies, coupled with molecular epidemiological investigations, are essential to comprehend the virus's evolutionary dynamics and devise effective preventive strategies. The study investigates the evolutionary dynamics and molecular characterization of the RSV F-protein using bioinformatic pipelines on NCBI and GISAID data sets and molecular analysis of clinical specimens collected from children. We found S255N/G, N262S, N268I, K272M/N, and S275F/A mutations in the heptad repeat region "A" that were associated with inducing resistance toward palivizumab from bioinformatics analysis of surveillance data sets. Molecular characterization of RSV F protein from clinical specimens found L45F mutations responsible for forming new clades, L172Q and S173L substitutions for suptavumab resistance, and N276S mutations for potentially impacting palivizumab resistance. Six N-glycosylation sites are found at 27, 70, 116, 120, 126, and 500 in all RSV strains. Purifying selection, maintaining fusion protein stability, was observed. Phylogenetic analysis reveals genetic variability, with RSV B showing higher diversity than RSV A, forming distinctive clades belonging to B.D (BA9) and A.D (ON1) strains of RSV B and A, respectively. The phylodynamics of RSV indicate a uniform increase in effective population size. Understanding the F protein's structure and dynamics is essential for elucidating the virus's pathogenic mechanisms and developing effective vaccines and antiviral therapies. Respiratory syncytial virus remains a major cause of severe respiratory disease in infants, the elderly, and immunocompromised populations worldwide. Despite recent advances in monoclonal antibodies and vaccines, the virus continues to evolve, posing challenges for long-term control. The RSV fusion (F) protein is central to viral entry and the primary target for neutralizing antibodies, yet little is known about its global evolutionary dynamics and drug-resistance associated changes. By integrating large-scale surveillance data with clinical isolates, our study identifies critical mutations, glycosylation patterns, and evolutionary pressures that shape the diversity of the F protein. These findings provide mechanistic insight into how RSV adapts under immune and therapeutic pressure, highlighting both vulnerabilities and conserved features of the F protein. Continuous monitoring of these evolutionary patterns will be crucial for maintaining vaccine effectiveness and informing the development of next-generation therapeutics to reduce RSV-associated morbidity and mortality.