Search Results (325)

Background/Objectives: Soybean (Glycine max) productivity is increasingly constrained by soil salinity. WIP transcription factors, a subfamily of C2H2-type zinc finger proteins, regulate cell division, differentiation, and tissue patterning in several plant species, but this gene family had not previously been systematically characterized in soybean or any other major legume crop. This study aimed to identify and characterize the GmWIP gene family genome-wide and evaluate its potential involvement in the soybean salt-stress response. Methods: Genome-wide identification of GmWIP genes was performed using sequence similarity and domain-based searches against the Wm82.gnm4.ann1 reference genome, followed by characterization of physicochemical properties, chromosomal distribution, phylogenetic relationships, gene duplication, conserved motifs, gene structure, and promoter cis-acting elements. Tissue-specific expression was examined using transcriptome data, and GmWIP responses to salt stress were profiled by RT-qPCR in roots, stems, and leaves of a salt-tolerant cultivar (HN531) and a salt-sensitive cultivar (HN563), alongside physiological measurements of oxidative stress and osmotic adjustment. Results: Thirty GmWIP genes were identified, with molecular weights from 26.90 to 57.52 kDa, distributed unevenly across 15 soybean chromosomes, with chromosomes 11, 12, and 13 forming a major hotspot (53.3% of the family). Duplication analysis detected 54 reconciled segmental duplicate gene pairs, all exhibiting Ka/Ks values < 1 (ranging from 0.0351 to 0.4471; mean 0.214), consistent with purifying selection acting on this gene set. GmWIP promoters were enriched for ABRE, MBS, and MeJA cis-acting elements. RT-qPCR showed genotype- and tissue-dependent differential expression under salt stress (e.g., up to 14.9-fold induction of GmWIP22 in HN531 stems), paralleled by superior proline accumulation (+45%), soluble sugars, and CAT activity (+38%) alongside reduced MDA accumulation in the tolerant cultivar. Conclusions: The GmWIP gene family has expanded substantially in soybean relative to previously characterized species and shows genotype-dependent transcriptional responses to salt stress, suggesting that specific GmWIP members are candidate regulators of salt tolerance and warrant further functional investigation. Full article

Background/Objectives: Salmonella enterica (S. enterica) is a major poultry-associated foodborne pathogen and a persistent public health concern. The global rise in antimicrobial resistance has accelerated the search for alternative control strategies, including the use of bacteriophages. However, their successful application requires a comprehensive evaluation of their biological performance, genomic safety, and functional proteins. This study aimed to characterize Salmonella phage WP110 and assess its potential as a biocontrol agent in broiler-associated production systems. Methods: Phage WP110 was evaluated against 251 S. enterica isolates from broiler-related sources. Adsorption kinetics, one-step growth, environmental stability (temperature and pH), and effective multiplicity of infection (MOI) were determined using Salmonella Kentucky S1H28. Whole-genome sequencing (WGS) and bioinformatic analyses were performed for genome annotation, taxonomic classification, and safety evaluation. In addition, protein structural prediction of a putative endolysin (WP110-gp057) was conducted using AlphaFold2, followed by structural comparison and molecular docking with peptidoglycan. Biocontrol efficacy was evaluated in contaminated rice husk, chicken meat, and on non-food materials. Results: Phage WP110 demonstrated a broad lytic spectrum, lysing 248/251 S. enterica isolates (98.8%). It adsorbed rapidly (within 3–15 min) to host cells and exhibited a latent period of ~20 min with a burst size of 134 particles per infected cell. Phage WP110 remained stable at 4–45 °C and pH 5–11 but was inactivated at ≥75 °C and pH 2. Complete bacterial inactivation in broth assay was achieved at an MOI of 10⁴. Genomic analysis revealed a 110,216 bp linear dsDNA genome (39.74% GC) comprising 204 ORFs, 25 tRNAs, and long direct terminal repeats, with no detectable antibiotic resistance genes. Phylogenetic and intergenomic analyses classified phage WP110 as a novel species within the genus Epseptimavirus. Structural modeling of WP110-gp057 revealed conserved catalytic residues and high structural similarity to T5 endolysin, while docking analysis supported a structurally plausible interaction with peptidoglycan at the predicted active-site groove, consistent with its proposed role in host cell wall degradation. In application models, phage WP110 significantly reduced Salmonella contamination in rice husk (up to 4.3 log CFU/g), chicken meat (up to 1.7 log CFU/g), and on non-food material surfaces (0.7–1.5 log CFU reduction). Conclusions: Phage WP110 is a broad-host-range lytic phage with favorable infection kinetics, environmental robustness, and genomic safety. Its functionally supported endolysin and strong antibacterial efficacy across broiler-associated matrices highlight its potential as a biocontrol agent for Salmonella mitigation in poultry value chain. Full article

Background: High-grade gliomas pose formidable challenges in neuro-oncology, with a median overall survival (OS) of 12–18 months. Laser interstitial thermal therapy (LITT) offers a minimally invasive cytoreductive option for deep-seated or recurrent tumors, achieving ablation rates of 85–98%. In parallel, artificial intelligence and machine learning are increasingly being applied to neuro-oncology to improve diagnosis, treatment planning, and outcome prediction, although these applications remain largely investigational. Methods: A literature search was conducted using the PubMed, MEDLINE, Embase, and ClinicalTrials.gov databases. The search terms included “laser interstitial thermotherapy,” “glioblastoma,” and “high-grade glioma”, “machine learning”, “artificial intelligence”. A total of 196 articles were identified. Inclusion criteria comprised primary studies, meta-analyses, and systematic reviews involving human data, with LITT used as a primary or secondary treatment modality. Sixty-nine studies were included in this review, while case reports and animal studies were excluded. Results: LITT represents a precision therapy for inoperable gliomas, achieving ablation rates of 85–98%. For primary glioblastoma, median overall survival (mOS) ranges from 11–16 months, and median progression-free survival (mPFS) from 4–9.5 months. In recurrent glioblastoma, LITT demonstrates a median overall survival ranging from 8.5 to 14.1 months and a median progression-free survival of 3–3.5 months with lower complication rates (5.7% vs. 13.8%) and shorter hospital stays (2.2 vs. 7 days). Overall complication rates range from 20–35%, predominantly due to cerebral edema, which is generally responsive to steroid therapy. Its value may be expanded by machine learning tools that integrate clinical, molecular, and imaging features to support patient selection and predict outcomes, though these remain at the proof-of-concept stage. In addition, LITT may serve as a platform for combination therapies, including immunotherapy, chemotherapy, targeted agents, and radiotherapy. Conclusions: Based on current evidence, LITT demonstrates outcomes that appear favorable in selected patient populations with high-grade gliomas and may be considered as a treatment option for primary tumors with challenging localization, near-spherical geometry, and volumes of approximately 30 cm³. It has a particularly important role in recurrent glioblastomas with similar characteristics, offering efficacy comparable to resection but with an improved safety profile in retrospective comparisons. LITT is evolving from a technically focused ablation method into a data-driven therapeutic platform. Integration with artificial intelligence may improve precision, safety, and personalization, helping define the role of LITT within modern neuro-oncology as higher-quality clinical evidence continues to accumulate. Full article

Cow’s milk allergy (CMA) is the most common food allergy in infants. Goat milk exhibits relatively low allergenicity and is widely regarded as a potential substitute for cow milk. However, the high sequence homology between cow and goat milk proteins may trigger cross-reactivity, significantly restricting the practical application of goat milk. This study aims to disrupt cross-linear epitopes of cow and goat milk proteins through enzymatic hydrolysis. We prepare hypoallergenic goat milk protein hydrolysates and systematically evaluate their desensitization effects. Bioinformatics methods were employed to predict the B-cell linear epitopes of six major allergens (α_S1-casein, α_S2-casein, β-casein, κ-casein, α-lactalbumin, and β-lactoglobulin) from cow, goat, and sheep milk, and sequence homology was analyzed through protein-protein Basic Local Alignment Search Tool (BLASTP). The results showed that the sequence similarity of homologous allergens among the three milk sources all exceeded 30%. Subsequently, six proteases (protamex, alcalase, pepsin, trypsin, papain and bromelain) were used to hydrolyze whole goat milk protein. Indirect enzyme-linked immunosorbent assay (ELISA) results indicated that all six hydrolysates significantly reduced immunoreactivity with antibodies against the five major cow’s milk allergens. Among them, alcalase exhibited significant efficacy against all five allergens; papain and protamex were particularly effective against β-casein; trypsin showed pronounced efficacy against α-lactalbumin and β-lactoglobulin; and bromelain and pepsin also significantly reduced immunoreactivity against certain allergens. Mass spectrometry revealed the peptide composition and epitope coverage of the hydrolysates: bromelain yielded two overlapping peptides (FAWPQY, LKDLKDY), protamex one (LAMAAS), Alcalase three (PPQSVLS, IPIQYVLS, LPYPYY), trypsin two (YLGYLEQL, AMAASDISLL), papain three (NEINQFYQK, FQSEEQQQTEDELQDK, AMAASDISL); and no matching peptide was detected for pepsin. These results confirmed at the molecular level that enzymatic hydrolysis can effectively disrupt cross-reactive epitopes. Full article

Background/Objectives: The non-neuronal cardiac cholinergic system (NNCCS) is known to synthesize ACh independently of the parasympathetic nervous system, thereby regulating cardiac homeostasis, which includes sustainability of energy metabolism, anti-inflammatory and anti-ischemic properties, electrical stability, and mitochondrial calcium handling. Given these beneficial functions of NNCCS, we were prompted to search for an inducer. One such inducer is SNPiP, a novel low-molecular-weight chemical compound developed by us. SNPiP accelerates ACh synthesis in the heart via cGMP elevation and, intriguingly, enhances diastolic function, increasing cardiac output and end-systolic pressure without elevating heart rate. However, the pharmacokinetics of SNPiP remain unknown, which led us to conduct the present study. Methods and Results: We found that the half-life of SNPiP in the blood was extremely short, similar to that of a nitric oxide (NO) donor, S-nitroso-N-acetyl-DL-penicillamine. This short half-life is caused by the rapid distribution of SNPiP into organs, including the heart, kidney, and liver. In addition, once transferred into blood cells, SNPiP itself became stable and remained intact for up to 1 h. Moreover, the short half-life was partly explained by the rapid degradation of SNPiP and concomitant loss of the nitroso group in the blood. Notably, when rats were treated with SNPiP, NO levels in the heart elevated bimodally: immediately after administration and again about 12 h later, coinciding with the previous report of NNCCS upregulation and accelerated ACh synthesis with NO production. Importantly, our previous transcriptome analysis of SNPiP-treated hearts supports these findings, as it revealed upregulation of diastolic function-related genes and proteins. Conclusions: Collectively, these results clarify the pharmacokinetics of SNPiP and demonstrate that, despite a shorter half-life, SNPiP is efficiently distributed to the heart, where it confers beneficial effects through induction of NNCCS. Full article

Alzheimer’s disease remains one of the hardest disorders to drug, and most computational pipelines still tackle one target at a time and ignore brain penetration until late. We bring all of that into a single optimization problem. Our framework, called CAGE-QMol(Constraint-Aware Quantum-inspired Molecular optimization), turns the search for a brain-penetrant, dual BACE1/AChE inhibitor into a constrained quadratic unconstrained binary optimization (QUBO) and solves it with an ensemble of classical, quantum-inspired, and Quantum Approximate Optimization Algorithm (QAOA) backends. The pipeline begins with three real public datasets—MoleculeNet BACE, ChEMBL CHEMBL4822 (BACE1) and CHEMBL220 (AChE), and the TDC BBB_Martins blood–brain-barrier set—which together yield 12,465 unique molecules with at least one measured endpoint. Multi-task ExtraTrees predictors trained on Morgan ECFP4 fingerprints and physicochemical descriptors deliver scaffold-split test-set metrics of $R^2=0.624$ ($\mathrm{MAE}=0.587$) for BACE1 and $R^2=0.383$ ($\mathrm{MAE}=0.793$) for AChE. The optimizer combines these predictions with a Lipinski-based feasibility cone, a TDC-derived BBB classifier, and similarity-driven diversity into a constrained QUBO. We adapt the classical exact-penalty rule, $\lambda >{\Delta }_S/{\delta }_g$, which guarantees every global minimizer of the penalized energy is feasible, and specialize it so that the multiplier is computed from the data rather than hand-tuned. A 10-qubit PennyLane QAOA circuit is benchmarked against exact enumeration, simulated annealing, genetic search, Bayesian TPE, and random search across ten seeds; the QUBO formulation lets a genetic solver match the exact ground state on every seed, while ablations show that removing the QUBO selection collapses the mean therapeutic score from 6.242 to 5.976 ($p<{10}^{-3}$, paired t-test). Top-50 candidates exhibit a mean BBB probability of 0.716, a mean QED of 0.768, and 100% Lipinski feasibility, with leading scaffolds (tetrahydroisoquinolinone, methoxy-tetrahydronaphthalene-urea, indanone-piperidine) reproducing motifs found in published dual BACE1/AChE inhibitor families. This paper contributes (i) a mathematically grounded penalty selection rule for constrained drug-discovery QUBOs, (ii) a single end-to-end pipeline from raw public data to ranked, 3D-embedded leads, and (iii) reproducible head-to-head benchmarks between classical, quantum-inspired, and QAOA-simulated optimizers on a real Alzheimer’s task. Full article

Bioactive peptides constitute a highly diverse and therapeutically relevant molecular class, yet their systematic exploration remains challenging because of the vast size, heterogeneity, and fragmented annotation of peptide chemical space. In this context, complex networks have emerged as a complementary computational framework for organizing, analyzing, and exploiting peptide diversity. This methodological review examines the main components of graph-based peptide informatics, from graph-based data integration and curated repositories to descriptor-based representations, similarity-driven network construction, and topology-informed analysis. We describe how peptide sequences can be projected into multidimensional reference spaces using molecular descriptors, aggregation operators, and unsupervised feature selection, and how these representations support the construction of Chemical Space Networks, Half-Space Proximal Networks, and Metadata Networks. Special attention is given to topological analysis, including threshold selection, community detection, and centrality-based identification of representative peptides and scaffolds. We also review the development of Multi-query Similarity Searching Models as training-independent, topology-guided alternatives to conventional supervised predictors. Finally, we highlight the implementation of these methodologies in computational resources such as StarPepDB, StarPep Toolbox, and StarPepWeb, which illustrate the transition of peptide network science from conceptual workflows to accessible, scalable, and reproducible infrastructures. Overall, complex networks are presented as a mature and interpretable paradigm for the structured exploration, analysis, and discovery of bioactive peptides. Full article

Background: Chagas disease, caused by the parasite Trypanosoma cruzi, remains a major neglected tropical disease, with millions of people living with the infection worldwide. Current treatments are effective in the acute stage of the disease, but are poorly tolerated and show reduced efficacy in chronic infections, highlighting an urgent need for novel therapeutic strategies. A key bottleneck in early-stage drug discovery is target identification, which is traditionally dependent on costly and low-throughput experimental methods. Computational approaches offer a cost-effective and fast alternative to traditional methods. Methods: In this study, we present an integrated in silico pipeline that combines ligand-based and structure-based computational approaches to prioritize potential molecular targets for bioactive compounds against T. cruzi. The ligand-based component performed similarity searches across curated bioactivity databases containing known ligand–protein associations, and the most similar candidates were then further evaluated using a structure-based approach through pairwise structural alignment against the T. cruzi proteome from AlphaFold. Results: The pipeline was validated using eight compounds with known targets, successfully recovering the correct target in six cases. Additionally, two compounds with anti-T. cruzi activity but unknown mechanisms of action were analyzed to hypothesize their potential targets. Conclusions: Overall, the pipeline demonstrated moderate success, with limitations arising from challenges in handling novel chemotypes and poorly annotated targets. Nevertheless, its modular nature allows for an easy adaptation to other neglected tropical diseases, providing a flexible and cost-effective framework for early-stage target prioritization. Full article

The primary control mechanism for synaptic uptake of GABA is through γ-aminobutyric acid transporter 1 (GAT-1, SLC6A1), a known target for anti-epileptic drugs. Although there is a clinically used GAT-1 inhibitor, tiagabine, the development of a new ligand with an advanced pharmacological profile is desirable. For this purpose, a multi-tiered virtual approach to screening has been created, involving pharmacophore-based search; application of the Informational Spectrum Method for Small Molecules, followed by EIIP/AQVN filtering (ISM-SM); molecular docking using an ensemble of several experimentally obtained structures of GAT-1; and ADMET predictions. Pharmacophore-based screening of the ZINC database of natural products, combined with ISM-SM/EIIP filtering, yielded 237 candidate compounds. Structural separation analysis discriminated between the positives and negatives, enabling enrichment-based prioritization. The use of a composite normalized rank score based on docking affinity and structural similarity allowed for the identification of the top candidates: ZINC03643214 and ZINC67840571. Collectively, these refinements establish a more sophisticated computational model for identifying novel GAT-1 inhibitors and highlight promising candidates for future experimental evaluation. Full article

The high prevalence and incidence of neurodegenerative diseases pose a public health challenge and drive the search for alternative treatments. This study determined the chemical composition of hydroalcoholic extracts from the Antarctic species Polycauliona candelaria and Placopsis antarctica and evaluated their antioxidant and cholinesterase-inhibitory potential through in vitro assays and molecular docking. Using UHPLC/ESI/QToF/MS, 16 compounds were tentatively identified in P. candelaria and 11 in P. antarctica. P. antarctica exhibited greater antioxidant capacity (2.69 ± 0.15 mg GAE/g in TPC, and an IC₅₀ for DPPH and ABTS of 330.64 ± 0.02 and 63.33 ± 0.02 µg/mL, respectively) and inhibitory activity (IC₅₀ for AChE and BuChE of 654.42 ± 0.03 and 845.58 ± 0.01 µg/mL, respectively) similar to P. candelaria. Molecular docking analyses revealed that gyrophoric acid and stictic acid possess outstanding binding affinities, comparable to the drug galantamine, by effectively interacting with the catalytic sites of the enzymes. This is the first report on the chemical compounds present in extracts of P. antarctica and P. candelaria and contributes to the understanding of their therapeutic potential. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

The circadian clock is a widespread rhythmic phenomenon across organisms, characterized by distinct gene expression patterns and behaviors at specific times of the day. Extensive genetic studies in the model fungus Neurospora crassa have yielded critical insights into the components and molecular mechanisms of circadian oscillators. However, these understandings remain absent across fungal lineages, especially from edible mushrooms. Morels (Morchella spp.) are well-recognized edible ascomycetes of considerable economic value and are partially artificially cultivated, but their biological characteristics are poorly understood. Investigating the presence of their circadian clock components, as well as the molecular underpinnings of circadian rhythms, holds important biological implications. In this study, we firstly performed a genomic search for homologs of known circadian clock genes in Morchella sextelata. Homologs of seven circadian clock genes, including wc-1, wc-2, fwd-1, frh, frq, and two additional clock-controlled genes, were identified, indicating the components necessary for the operation of a FWC oscillator contained in M. sextelata. Then, using reverse transcription quantitative PCR (RT-qPCR), the expression profiles of these seven circadian clock-related genes and four mating-type genes were examined in RNA samples which were extracted from mycelia of MAT1-1, MAT1-2 and MAT1-1 × MAT1-2 co-culture/crossed condition during conidiation under in vitro cultivation across one day. The expression levels of seven circadian clock genes and four mating-type genes displayed similar time-of-day-specific rhythmic patterns, yet remained consistently distinct across the mating-type strains and their co-culture/crossed condition, indicating a potential correlation between circadian clock and mating-type loci. Collectively, these results suggest that M. sextelata harbors conserved circadian clock-related homologs and displays mating-type-associated temporal expression differences under the tested conidiation conditions, offering a novel perspective for exploring the potential link between clock-related regulation and mating-type background in the future. Full article

The search for novel antiviral agents against Newcastle disease virus (NDV) remains a priority in industrial poultry farming due to the virus’s high contagiousness and associated economic losses, prompting evaluation of polyene macrolides as potential therapeutic candidates. We employed a comprehensive approach combining computational modeling (molecular docking and dynamics simulation) and laboratory experiments to investigate the antiviral potential of natamycin, nystatin, and filipin complex against three NDV strains. Molecular docking analysis indicated binding sites for macrolides within the hydrophobic regions of surface glycoproteins HN and F, with binding energies ranging from −6.5 to −10.5 kcal/mol, while 50 ns molecular dynamics simulation confirmed complex stability. Laboratory testing using fluorescence-based neuraminidase assays demonstrated dose-dependent inhibitory activity with IC₅₀ values of 0.0043 ± 0.0015 mg/mL for filipin complex, 0.0117 ± 0.0029 mg/mL for nystatin, and 0.0220 ± 0.0138 mg/mL for natamycin, with similar ranking observed for fusion inhibition (EC₅₀ values of 0.00053 ± 0.00039, 0.00545 ± 0.00560, and 0.01196 ± 0.00965 mg/mL, respectively). While filipin complex exhibited the highest antiviral activity, its significant cytotoxicity limits therapeutic application, whereas natamycin demonstrated a favorable safety profile consistent with its GRAS status. These findings indicate that natamycin exhibits a favorable safety-to-efficacy profile in vitro, warranting further in vivo investigation to clarify its mechanism of action and establish practical application protocols for NDV control in poultry. Full article

Compound repurposing is an efficient method to save both time and costs by redirecting previously synthesized small molecules towards new biological targets. In this research, we employ computational methodologies to investigate and assess target engagement of small molecules as tyrosine kinase inhibitors (TKIs). Therefore, compounds TKI.2a, TKI.2b, TKI.6, TKI.16, TKI.19, and TKI.21b identified from our earlier research, undergo assessments of molecular similarity, docking studies, and pharmacophore modeling along with those discovered through database searches. Compounds TKI.2a, TKI.2b, TKI.6, and TKI.19 appear to exhibit multi-target tyrosine kinase inhibitory activities against VEGFR-2 (Vascular Endothelial Growth Factor Receptor), RET (proto-oncogene tyrosine–protein kinase receptor), PDGFRα (Platelet-Derived Growth Factor Receptor alpha), EGFR (Epidermal Growth Factor Receptor), and HER2 (Human Epidermal Receptor) receptors. Pharmacophore models were applied for ligand-based virtual screening using defined parameters to discover candidate compounds that exhibit drug-likeness with FDA (Food and Drug Administration)-approved tyrosine kinase inhibitors. Molecular docking studies identified lead compounds for each biological target based on their overall affinity values and established interactions. Compound ChEMBL2170947 was found to be the most promising candidate for the VEGFR-2 receptor, ChEMBL5019511 for PDGFRα, ChEMBL2216869 for EGFR, and ChEMBL3355044 for HER2. Full article

The literature collected from various search engines and high-quality scientific databases reveals that amino acid (AA)-functionalized nanoparticles have emerged as a promising field for selective detection and remediation of heavy metals (HMs). Among the various nanoparticles (NPs), gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) have drawn considerable attention, attributed to their unique optical, catalytic, and surface plasmon resonance properties. Functionalization with amino acids significantly enhances nanoparticle stability, biocompatibility, and metal-binding affinity through diverse functional groups. AA-functionalized AuNPs, including glycine, cystine, leucine, methionine, tyrosine, aspartic acid, histidine, and lysine-capped systems, exhibit tunable selectivity toward heavy metal ions. Bifunctionalization strategies further enhance sensitivity by inducing nanoparticle aggregation or signal amplification. Beyond single amino acids, polypeptides and protein-functionalized AuNPs offer enhanced molecular recognition and multivalent binding, expanding their applicability in complex matrices. Similarly, amino acid-functionalized AgNPs, such as those capped with similar amino acids stated above, exhibit strong interactions with heavy metals, AA bifunctionalization, and bimetallic nanoparticles (BNPs), particularly amino acid-functionalized Au–Ag systems, which combine the advantages of both metals, leading to improved sensitivity, selectivity, and signal strength. Although these advances have been made, a major gap remains in the systematic comparison of different amino acids, peptides, and bimetallic systems under real-world conditions. This gap can be addressed by standardized testing methods, clearer structure–function relationships and combined experimentation to guide the rational design of more efficient AA-functionalized nanoparticles. Full article