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Results for “In silico screening”

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PubMed2003

Putative dynamics of vasopressin in its V1a receptor binding site.

The molecular architecture of the GPCRs, including the dynamic set of interactions between the receptor and the ligand, is one of the key structural questions of biophysical approaches. In the present study, molecular dynamics (MD) simulations were performed on the well-validated molecular model of the vasopressin V1a receptor applying different parameters (i.e., force fields, time variation, use of constraints) in order to sample the conformational space of the endogenous ligand arginine vasopressin (AVP), to explore different putative binding modes, and to analyze the simulation results with respect to experimental data. Noteworthy, it is to mention that for the first time a model of the vasopressin receptor remained stable in a 500 ps MD simulation run under vacuo boundary conditions using the Kollman all-atom FF even though no constraints were imposed. Conclusively, we determined an optimized experimental procedure for studying the dynamics and structure-functionship of this highly important family of GPCRs: the use of MD simulations with the Kollman all-atom force-field parameters on a constrained receptor. Our simplified model may be used as a basis for structure based design of new GPCR ligands and for in silico screening of virtual combinatorial chemistry libraries.

Arginine↗

PubMed2003

Identification and pharmacological characterization of SRBP-2: a novel SR31747A-binding protein.

SR31747A is a sigma ligand with potent antiproliferative activity against tumor cells and for which three binding proteins have been identified to date: (a) SRBP-1 (also called sigma 1); (b) HIS; and (c) sigma 2. In this study, we characterized an additional SR31747A binding site, i.e., SRBP-2 (SR31747A-binding protein 2). Using an in silico screening approach, we identified this novel sequence, which exhibits 41% homology with HSI. The 1142-bp cDNA was found to encode a 206 amino acid protein not related to SRBP-1. Northern blot analysis of SRBP-2 mRNA expression revealed a single 1.1-kb transcript that was widely expressed in organs; the liver was particularly enriched, and the brain showed the lowest abundance. A murine homologue that exhibited a similar expression pattern was also characterized. Subcellular localization analysis using specific polyclonal antibodies revealed that SRBP-2 had the same nuclear membrane and endoplasmic reticulum localization as other members of the SR31747A-binding protein family. Considering SRBP-2-binding properties, pharmacological analysis clearly highlighted that SRBP-2 was distinct from sigma 2. Scatchard plot analysis revealed K(d) values of 10 and 3 nM for SR31747A and Tamoxifen, respectively. In contrast with HSI, the protein also did not exhibit detectable isomerase activity. When analyzing SRBP-2 expression in human breast cancer biopsies, we obtained evidence that SRBP-2 expression, together with SRBP-1 and HSI, may be of interest as a prognostic marker. These findings demonstrated that SRBP-2 represents an additional molecular target for SR31747A, which could help to understand the immunosuppressive and antiproliferative effects of the molecule.

Amino Acid Sequence↗

PubMed2004

Predicting human pharmacokinetics from preclinical data.

Approaches used for the prediction of pharmacokinetics in relevant populations of human patients mostly rely on in vivo data from animals, using allometric scaling or time-invariant methods. The growth of in vitro and, more recently, in silico screens for evaluating pharmaceutical, pharmacokinetic and toxicity properties can also be used to predict complex in vivo behavior in humans. In most cases, careful and educated application of available approaches provides predictions of pharmacokinetic parameters within 2- or 3-fold of that observed. Attention should now be directed toward integrating information from different sources to increase the precision and accuracy of these pharmacokinetic predictions and to enable a better understanding of the processes underlying ADME behavior in humans.

Algorithms↗

PubMedDate not supplied

Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps.

Malaria has been one of the most significant public health problems for centuries. It affects many tropical and subtropical regions of the world. The increasing resistance of Plasmodium spp. to existing therapies has heightened alarms about malaria in the international health community. Nowadays, there is a pressing need for identifying and developing new drug-based antimalarial therapies. In an effort to overcome this problem, the main purpose of this study is to develop simple linear discriminant-based quantitative structure-activity relationship (QSAR) models for the classification and prediction of antimalarial activity using some of the TOMOCOMD-CARDD (TOpological MOlecular COMputer Design-Computer Aided "Rational" Drug Design) fingerprints, so as to enable computational screening from virtual combinatorial datasets. In this sense, a database of 1562 organic chemicals having great structural variability, 597 of them antimalarial agents and 965 compounds having other clinical uses, was analyzed and presented as a helpful tool, not only for theoretical chemists but also for other researchers in this area. This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. Afterward, two linear classification functions were derived in order to discriminate between antimalarial and nonantimalarial compounds. The models (including nonstochastic and stochastic indices) correctly classify more than 93% of the compound set, in both training and external prediction datasets. They showed high Matthews' correlation coefficients, 0.889 and 0.866 for the training set and 0.855 and 0.857 for the test one. The models' predictivity was also assessed and validated by the random removal of 10% of the compounds to form a new test set, for which predictions were made using the models. The overall means of the correct classification for this process (leave group 10% full-out cross validation) using the equations with nonstochastic and stochastic atom-based quadratic fingerprints were 93.93% and 92.77%, respectively. The quadratic maps-based TOMOCOMD-CARDD approach implemented in this work was successfully compared with four of the most useful models for antimalarials selection reported to date. The developed models were then used in a simulation of a virtual search for Ras FTase (FTase = farnesyltransferase) inhibitors with antimalarial activity; 70% and 100% of the 10 inhibitors used in this virtual search were correctly classified, showing the ability of the models to identify new lead antimalarials. Finally, these two QSAR models were used in the identification of previously unknown antimalarials. In this sense, three synthetic intermediaries of quinolinic compounds were evaluated as active/inactive ones using the developed models. The synthesis and biological evaluation of these chemicals against two malaria strains, using chloroquine as a reference, was performed. An accuracy of 100% with the theoretical predictions was observed. Compound 3 showed antimalarial activity, being the first report of an arylaminomethylenemalonate having such behavior. This result opens a door to a virtual study considering a higher variability of the structural core already evaluated, as well as of other chemicals not included in this study. We conclude that the approach described here seems to be a promising QSAR tool for the molecular discovery of novel classes of antimalarial drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of malaria illnesses.

Algorithms↗

PubMed2005

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network.

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146+/-0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p=0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2003

Identification of novel anti-angiogenic factors by in silico functional gene screening method.

Angiogenesis, the formation of new blood vessels out of pre-existing capillaries, occurs in a variety of pathophysiological conditions, and is regulated by a balance of angiogenic activators and inhibitors. To identify novel angiogenic factors, we developed a gene screening method by combining the prediction analysis of transcription factor (TF) binding site and the chromosomal localization analysis. First, we analyzed the promoter sequences from known angiogenesis-related factors using the MATINSPECTOR program in TRANSFAC database. Interestingly, we found that the binding site of LMO2 complex is highly conserved in the promoter regions of these factors. Second, we analyzed chromosome loci based on the hypothesis that angiogenesis-related factors might be co-localized in a specific chromosomal band. We found that angiogenesis-related factors are localized in specific 14 chromosomal bands including 5q31 and 19q13 using AngioDB and LocusLink database mining. From these two approaches, we identified 32 novel candidates that have the LMO2 complex binding site in their promoter and are located on one of 14 chromosomal bands. Among them, human recombinant troponin T and spectrin markedly inhibited the neovascularization in vivo and in vitro. Collectively, we suggest that the combination of the prediction analysis of TF binding site and the chromosomal localization analysis might be a useful strategy for gene screening of angiogenesis.

Angiogenesis Inducing Agents↗

PubMed2006

Progressive docking: a hybrid QSAR/docking approach for accelerating in silico high throughput screening.

A combination of protein-ligand docking and ligand-based QSAR approaches has been elaborated, aiming to speed-up the process of virtual screening. In particular, this approach utilizes docking scores generated for already processed compounds to build predictive QSAR models that, in turn, assess hypothetical target binding affinities for yet undocked entries. The "progressive docking" has been tested on drug-like substances from the NCI database that have been docked into several unrelated targets, including human sex hormone binding globulin (SHBG), carbonic anhydrase, corticosteroid-binding globulin, SARS 3C-like protease, and HIV1 reverse transcriptase. We demonstrate that progressive docking can reduce the amount of computations 1.2- to 2.6-fold (when compared to traditional docking), while maintaining 80-99% hit recovery rates. This progressive-docking procedure, therefore, substantially accelerates high throughput screening, especially when using high accuracy (slower) docking approaches and large-sized datasets, and has allowed us to identify several novel potent nonsteroidal SHBG ligands.

Binding Sites↗

PubMed2006

Current status of virtual screening as analysed by target class.

In silico virtual screening for drug discovery has become a hot topic in medicinal chemistry research during the last 5 years, growing from a largely academic pursuit concerned principally with validating the methods used, to a major early-stage technique for lead discovery in the pharmaceutical industry. In this review we highlight a few recent successes in ligand docking associated with virtual screening, paying particular attention to four major target classes of pharmaceutical interest (G Protein-Coupled receptors, nuclear hormone receptors, kinases, proteases). We also discuss some emerging trends in the field, some common limitations, and how they are being overcome.

Binding Sites↗

PubMed2006

In silico and in vitro screening for inhibition of cytochrome P450 CYP3A4 by comedications commonly used by patients with cancer.

Cytochrome P450 3A4 (CYP3A4) is the major enzyme responsible for phase I drug metabolism of many anticancer agents. It is also a major route for metabolism of many drugs used by patients to treat the symptoms caused by cancer and its treatment as well as their other illnesses, for example, cardiovascular disease. To assess the ability to inhibit CYP3A4 of drugs most commonly used by our patients during cancer therapy, we have made in silico predictions based on the crystal structures of CYP3A4. From this set of 33 common comedicated drugs, 10 were predicted to be inhibitors of CYP3A4, with the antidiarrheal drug loperamide predicted to be the most potent. There was significant correlation (r(2) = 0.75-0.66) between predicted affinity and our measured IC(50) values, and loperamide was confirmed as a potent inhibitor (IC(50) of 0.050 +/- 0.006 microM). Active site docking studies predicted an orientation of loperamide consistent with formation of the major (N-demethylated) metabolite, where it interacts with the phenylalanine cluster and Arg-212 and Glu-374; experimental evidence for the latter interaction comes from the approximately 12-fold increase in K(M) for loperamide observed for the Glu-374-Gln mutant. The commonly prescribed drugs loperamide, amitriptyline, diltiazem, domperidone, lansoprazole, omeprazole, and simvastatin were identified by our in silico and in vitro screens as relatively potent inhibitors of CYP3A4 that have the potential to interact with cytotoxic agents to cause adverse effects, highlighting the likelihood of drug-drug interactions affecting chemotherapy treatment.

Antidiarrheals↗

PubMed2003

DNA aptamers that recognize fluorophore using on-chip screening in combination with an in silico evolution.

We successfully developed a novel screening method for the acquisition of DNA aptamers. The technique selectively recognizes resorufin using on-chip screening in combination with an in silico evolution method. This method proved efficient for screening for DNA aptamers of single-stranded oligo-DNAs. A genetic algorithm was applied to make oligonucleotide sequences for the combinatorial library. A fluorophore, resorufin was applied to the ligand screening as a target. The affinity of the library was analyzed by the DNA microarray. This method for screening DNA ligands includes on-chip selection and a computer-evolved sequence, where the highest affinity was chosen. The fluorescence intensity of the library on the DNA microarray increased after three repetitions of the selection round.

Base Sequence↗

PubMed2005

The devil is still in the details--driving early drug discovery forward with biophysical experimental methods.

This review comments on some recent trends and insights in the field of lead identification and optimization with a bias toward the increased use of biophysical methods, particularly in combination with three-dimensional structural information. While high-throughput screening, combinatorial chemistry and, most recently, in silico virtual screening techniques have made well-resourced but only partially successful attempts to meet the challenge of identifying new drug candidates by playing 'the large numbers game', another group of technologies are now approaching the same challenge from what might be considered the opposite extreme. The common strategy of these technologies is to focus on a smaller set of low-molecular-weight compounds whose interactions with a target are characterized with the aid of sensitive assays, most often high-quality biophysical techniques such as biosensors, calorimetry, nuclear magnetic resonance spectroscopy and X-ray crystallography. The advantages of such an approach include more optimal and chemically attractive starting points, immediate access to reliable measurements of binding properties, the mapping of ligand interactions on the atomic level and, most importantly, a greater control of experimental errors at the initial stages of drug discovery where compounds are either discovered or lost. When correctly supported, this more careful approach appears to deliver quality leads, even for the so-called 'difficult' targets. As these techniques are complementary to traditional methods, companies should be less hesitant to invest in them. The biophysical methods that are used to drive this approach have made something of a return to drug discovery after having been discarded for being too slow, too expensive or too old-fashioned by the over-optimistic supporters of high-throughput and statistical/computational in silico methods.

Animals↗

PubMed2007

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions.

BACKGROUND: A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer. Analysis of publicly available databases can help identify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of their huge potential to affect gene function, no systematic attention has been paid so far to the changes that occur in untranslated regions of mRNA. RESULTS: In this study, we used Expressed Sequence Tag (EST) databases as a source for cancer-related sequence polymorphism discovery at the whole-genome level. Using a novel computational procedure, we focused on the identification of untranslated region (UTR)-localized non-coding Single Nucleotide Polymorphisms (UTR-SNPs) significantly associated with the tumoral state. To explore possible relationships between genetic mutation and phenotypic variation, bioinformatic tools were used to predict the potential impact of cancer-associated UTR-SNPs on mRNA secondary structure and UTR regulatory elements. We provide a comprehensive and unbiased description of cancer-associated UTR-SNPs that may be useful to define genotypic markers or to propose polymorphisms that can act to alter gene expression levels. Our results suggest that a fraction of cancer-associated UTR-SNPs may have functional consequences on mRNA stability and/or expression. CONCLUSION: We have undertaken a comprehensive effort to identify cancer-associated polymorphisms in untranslated regions of mRNA and to characterize putative functional UTR-SNPs. Alteration of translational control can change the expression of genes in tumor cells, causing an increase or decrease in the concentration of specific proteins. Through the description of testable candidates and the experimental validation of a number of UTR-SNPs discovered on the secreted protein acidic and rich in cysteine (SPARC) gene, this report illustrates the utility of a cross-talk between in silico transcriptomics and cancer genetics.

Computational Biology↗

PubMed2002

Search for potential vaccine candidate open reading frames in the Bacillus anthracis virulence plasmid pXO1: in silico and in vitro screening.

A genomic analysis of the Bacillus anthracis virulence plasmid pXO1, aimed at identifying potential vaccine candidates and virulence-related genes, was carried out. The 143 previously defined open reading frames (ORFs) (R. T. Okinaka, K. Cloud, O. Hampton, A. R. Hoffmaster, K. K. Hill, P. Keim, T. M. Koehler, G. Lamke, S. Kumano, J. Mahillon, D. Manter, Y. Martinez, D. Ricke, R. Svensson, and P. J. Jackson, J. Bacteriol. 181:6509-6515, 1999) were subjected to extensive sequence similarity searches (with the nonredundant and unfinished microbial genome databases), as well as motif, cellular location, and domain analyses. A comparative genomics analysis was conducted with the related genomes of Bacillus subtilis, Bacillus halodurans, and Bacillus cereus and the pBtoxis plasmid of Bacillus thuringiensis var. israeliensis. As a result, the percentage of ORFs with clues about their functions increased from approximately 30% (as previously reported) to more than 60%. The bioinformatics analysis permitted identification of novel genes with putative relevance for pathogenesis and virulence. Based on our analyses, 11 putative proteins were chosen as targets for functional genomics studies. A rapid and efficient functional screening method was developed, in which PCR-amplified full-length linear DNA products of the selected ORFs were transcribed and directly translated in vitro and their immunogenicities were assessed on the basis of their reactivities with hyperimmune anti-B. anthracis antisera. Of the 11 ORFs selected for analysis, 9 were successfully expressed as full-length polypeptides, and 3 of these were found to be antigenic and to have immunogenic potential. The latter ORFs are currently being evaluated to determine their vaccine potential.

Animals↗

PubMed2004

Utility of the Trypanosoma cruzi sequence database for identification of potential vaccine candidates by in silico and in vitro screening.

Glycosylphosphatidylinositol (GPI)-anchored proteins are abundantly expressed in the infective and intracellular stages of Trypanosoma cruzi and are recognized as antigenic targets by both the humoral and cellular arms of the immune system. Previously, we demonstrated the efficacy of genes encoding GPI-anchored proteins in eliciting partially protective immunity to T. cruzi infection and disease, suggesting their utility as vaccine candidates. For the identification of additional vaccine targets, in this study we screened the T. cruzi expressed sequence tag (EST) and genomic sequence survey (GSS) databases. By applying a variety of web-based genome-mining tools to the analysis of approximately 2,500 sequences, we identified 348 (37.6%) EST and 260 (17.4%) GSS sequences encoding novel parasite-specific proteins. Of these, 19 sequences exhibited the characteristics of secreted and/or membrane-associated GPI proteins. Eight of the selected sequences were amplified to obtain genes TcG1, TcG2, TcG3, TcG4, TcG5, TcG6, TcG7, and TcG8 (TcG1-TcG8) which are expressed in different developmental stages of the parasite and conserved in the genome of a variety of T. cruzi strains. Flow cytometry confirmed the expression of the antigens encoded by the cloned genes as surface proteins in trypomastigote and/or amastigote stages of T. cruzi. When delivered as a DNA vaccine, genes TcG1-TcG6 elicited a parasite-specific antibody response in mice. Except for TcG5, antisera to genes TcG1-TcG6 exhibited trypanolytic activity against the trypomastigote forms of T. cruzi, a property known to correlate with the immune control of T. cruzi. Taken together, our results validate the applicability of bioinformatics in genome mining, resulting in the identification of T. cruzi membrane-associated proteins that are potential vaccine candidates.

Animals↗

PubMed2005

Improving on nature's defenses: optimization & high throughput screening of antimicrobial peptides.

Antimicrobial peptides (AMPs) are ubiquitous in nature where they play important roles in host defense and microbial control. Despite their natural origin, antimicrobial spectrum and potency, the lead peptide candidates that so far have entered pharmaceutical development have all been further optimized by rational or semi-rational approaches. In recent years, several high throughput screening (HTS) systems have been developed to specifically address optimization of AMPs. These include a range of computational in silico systems and cell-based in vivo systems. The in silico-based screening systems comprise several computational methods such as Quantitative Structure/Activity Relationships (QSAR) as well as simulation methods mimicking peptide/membrane interactions. The in vivo-based systems can be divided in cis-acting and trans-acting screening systems. The cis-acting pre-screens, where the AMP exerts its antimicrobial effect on the producing cell, allow screening of millions or even billions of lead candidates for their basic antimicrobial or membrane-perturbating activity. The trans-acting screens, where the AMP is secreted or actively liberated from the producing cell and interacts with cells different from the producing cell, allow for screening under more complex and application-relevant conditions. This review describes the application of HTS systems employed for AMPs and lists advantages as well as limitations of these systems.

Animals↗

PubMed2005

Are hERG channel inhibition and QT interval prolongation all there is in drug-induced torsadogenesis? A review of emerging trends.

Contemporary preclinical in vitro and in vivo methods have been imperfect in predicting drug-induced Torsades de Pointes (TdP) in humans. A better understanding of additional relevant factors in the genesis of drug-induced TdP is necessary. New sophisticated in vitro techniques, such as arterially perfused ventricular wedge preparations or isolated perfused hearts, potentially offer a better understanding of torsadogenic mechanisms and a refinement of drug testing. Of particular interest are the dispersion of repolarization and the refractoriness of different cell types across the ventricular wall, triangulation of the action potential, reverse use dependence and instability of the action potential duration. In vivo models are currently refined by establishing parameters such as beat-to-beat variability and T-wave morphology as derived from the in vitro proarrhythmia indices. Animal models of proarrhythmia are to date not recommended for routine evaluation. A pharmacodynamic interaction with combinations of torsadogenic compounds is another area to be considered. Little is known about channel/receptor cross talk, although considerable evidence exists that cardiac G protein-coupled receptors can modulate hERG channel function. More investigations are necessary to further evaluate the role of altered gene expression, mutations, and polymorphisms in drug-induced TdP. A novel mechanism of drug-induced torsadogenesis is the reduced expression of hERG channel protein on the plasma membrane due to a trafficking defect. Pharmacokinetic and metabolism data are crucial for calculating the risk of a torsadogenic potential in man. Consideration of intracardiac accumulation can help in delineating pharmacokinetic-pharmacodyamic relationships. In silico virtual screening procedures with new chemical entities to predict hERG block may develop as a promising tool. The role of in silico modeling of TdP arrhythmia is likely to become increasingly important for organizing and integrating the vast amount of generated data. At present, however, in silico methods cannot replace existing preclinical in vitro and in vivo models.

Animals↗

PubMed2002

Determination of the genomic structure and mutation screening in schizophrenic individuals for five subunits of the N-methyl-D-aspartate glutamate receptor.

The glutamatergic system is the major excitatory neurotransmitter system in the CNS. Glutamate receptors, and in particular N-methyl-D-aspartate (NMDA) receptors, have been proposed as mediators of many common neuropsychiatric phenotypes including cognition, psychosis, and degeneration. We have reconstructed the genomic structure of all five genes encoding NMDA receptors in silico. We screened each for sequence variation and estimated the allele frequencies of all detected SNPs in pooled samples of 184 UK Caucasian schizophrenics and 184 UK Caucasian blood donor controls. Only a single non-synonymous polymorphism was found indicating extreme selection pressure. The rarity of non-synonymous changes suggests that such variants are unlikely to make a common contribution to common phenotypes. We found a further 26 polymorphisms within exonic or adjacent intronic sequences. The minor alleles of most of these have a relatively high frequency (63% above 0.2). These SNPs will therefore be suitable for studying neuropsychiatric phenotypes that are putatively related to NMDA dysfunction. Pooled analysis provided no support for association between any of the GRIN genes and schizophrenia.

Base Sequence↗

PubMed2026

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗