Peptide Database Research Articles

ACE inhibitory peptides in standard and fermented deer velvet: an in silico and in vitro investigation

BackgroundThe use of deer velvet antler (DVA) as a potent traditional medicine ingredient goes back for over 2000 years in Asia. Increasingly, though, DVA is being included as a high protein functional food ingredient in convenient, ready to consume products in Korea and China. As such, it is a potential source of endogenous bioactive peptides and of ‘cryptides’, i.e. bioactive peptides enzymatically released by endogenous proteases, by processing and/or by gastrointestinal digestion. Fermentation is an example of a processing step known to release bioactive peptides from food proteins. In this study, we aimed to identify in silico bioactive peptides and cryptides in DVA, before and after fermentation, and subsequently to validate the major predicted bioactivity by in vitro analysis.MethodsPeptides that were either free or located within proteins were identified in the DVA samples by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by database searching. Bioactive peptides and cryptides were identified in silico by sequence matching against a database of known bioactive peptides. Angiotensin-converting enzyme (ACE) inhibitory activity was measured by a colorimetric method.ResultsThree free bioactive peptides (LVVYPW, LVVYPWTQ and VVYPWTQ) were solely found in fermented DVA, the latter two of which are known ACE inhibitors. However matches to multiple ACE inhibitor cryptides were obtained within protein and peptide sequences of both unfermented and fermented DVA. In vitro analysis showed that the ACE inhibitory activity of DVA was more pronounced in the fermented sample, but both unfermented and fermented DVA had similar activity following release of cryptides by simulated gastrointestinal digestion.ConclusionsDVA contains multiple ACE inhibitory peptide sequences that may be released by fermentation or following oral consumption, and which may provide a health benefit through positive effects on the cardiovascular system. The study illustrates the power of in silico combined with in vitro methods for analysis of the effects of processing on bioactive peptides in complex functional ingredients like DVA.

Antimicrobial peptide identification using multi-scale convolutional network

BackgroundAntibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem.ResultsIn this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy.ConclusionsMulti-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities.

The BIOPEP-UWM™ database of bioactive peptides (formerly BIOPEP) has recently become a popular tool in the research on bioactive peptides, especially on these derived from foods and being constituents of diets that prevent development of chronic diseases. The database is continuously updated and modified. The addition of new peptides and the introduction of new information about the existing ones (e.g., chemical codes and references to other databases) is in progress. New opportunities include the possibility of annotating peptides containing D-enantiomers of amino acids, batch processing option, converting amino acid sequences into SMILES code, new quantitative parameters characterizing the presence of bioactive fragments in protein sequences, and finding proteinases that release particular peptides.

Analysis of Secondary Structure Biases in Naturally Presented HLA-I Ligands.

Recent clinical developments in antitumor immunotherapy involving T-cell related therapeutics have led to a renewed interest for human leukocyte antigen class I (HLA-I) binding peptides, given their potential use as peptide vaccines. Databases of HLA-I binding peptides hold therefore information on therapeutic targets essential for understanding immunity. In this work, we use in depth and accurate HLA-I peptidomics datasets determined by mass-spectrometry (MS) and analyze properties of the HLA-I binding peptides with structure-based computational approaches. HLA-I binding peptides are studied grouping all alleles together or in allotype-specific contexts. We capitalize on the increasing number of structurally determined proteins to (1) map the 3D structure of HLA-I binding peptides into the source proteins for analyzing their secondary structure and solvent accessibility in the protein context, and (2) search for potential differences between these properties in HLA-I binding peptides and in a reference dataset of HLA-I motif-like peptides. This is performed by an in-house developed heuristic search that considers peptides across all the human proteome and converges to a collection of peptides that exhibit exactly the same motif as the HLA-I peptides. Our results, based on 9-mers matched to protein 3D structures, clearly show enriched sampling for HLA-I presentation of helical fragments in the source proteins. This enrichment is significant, as compared to 9-mer HLA-I motif-like peptides, and is not entirely explained by the helical propensity of the preferred residues in the HLA-I motifs. We give possible hypothesis for the secondary structure biases observed in HLA-I peptides. This contribution is of potential interest for researchers working in the field of antigen presentation and proteolysis. This knowledge refines the understanding of the rules governing antigen presentation and could be added to the parameters of the current peptide-MHC class I binding predictors to increase their antigen predictive ability.

BioDADPep: A Bioinformatics database for anti diabetic peptides.

The increasing number of cases for diabetes worldwide is a concern. Therefore, it is of interest to design therapeutic peptides to overcome side effects caused by the available drugs. It should be noted that data on several known anti-diabetic peptides is available in the literature in an organized manner. Hence, it is of interest to collect, glean and store such data in form of a searchable database supported by RDBMS. Data on anti-diabetic peptides and their related data are collected from the literature using manual search. Data on related peptides from other databases (THPdb, ADP3, LAMP, AHTPDB, AVPdb, BioPepDB, CancerPPD, CPPsite, DRAMP, SATPdb, CAMPR3 and MBPDB) are also included after adequate curation. Thus, we describe the development and utility of BioDADPep, a Bioinformatics database for anti-diabetic peptides. The database has cross-reference for antidiabetic peptides. The database is enabled with a web-based GUI using a simple Google-like search function. Data presented in BioDADPep finds application in the design of an effective anti-diabetic peptide.

Validation of Chimeric Fusion Peptides Using Proteomics Data.

Chimeric RNAs as well as their fused protein products have therapeutic applications ranging from diagnostics to being used as therapeutic target. Many algorithms have been developed to identify chimeric RNAs, however, identification and validation of fused protein product of the chimeric RNA is still an emerging field. These chimeric proteins can be validated by searching and identifying them in publicly available proteomics datasets. Here we describe the detailed steps for (1) downloading and processing publicly available proteomics datasets, (2) developing fusion peptide database by performing in silico tryptic digestion of chimeric proteins, and (3) software used to identify chimeric peptides in the proteomics data.

Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing.

Alternative pre-mRNA splicing has long been proposed to contribute greatly to proteome complexity. However, the extent to which mature mRNA isoforms are successfully translated into protein remains controversial. Here, we used high-throughput RNA sequencing and mass spectrometry (MS)–based proteomics to better evaluate the translation of alternatively spliced mRNAs. To increase proteome coverage and improve protein quantitation, we optimized cell fractionation and sample processing steps at both the protein and peptide level. Furthermore, we generated a custom peptide database trained on analysis of RNA-seq data with MAJIQ, an algorithm optimized to detect and quantify differential and unannotated splice junction usage. We matched tandem mass spectra acquired by data-dependent acquisition (DDA) against our custom RNA-seq based database, as well as SWISS-PROT and RefSeq databases to improve identification of splicing-derived proteoforms by 28% compared with use of the SWISS-PROT database alone. Altogether, we identified peptide evidence for 554 alternate proteoforms corresponding to 274 genes. Our increased depth and detection of proteins also allowed us to track changes in the transcriptome and proteome induced by T-cell stimulation, as well as fluctuations in protein subcellular localization. In sum, our data here confirm that use of generic databases in proteomic studies underestimates the number of spliced mRNA isoforms that are translated into protein and provides a workflow that improves isoform detection in large-scale proteomic experiments.

TCR Fingerprinting and Off-Target Peptide Identification.

Adoptive T cell therapy using patient T cells redirected to recognize tumor-specific antigens by expressing genetically engineered high-affinity T-cell receptors (TCRs) has therapeutic potential for melanoma and other solid tumors. Clinical trials implementing genetically modified TCRs in melanoma patients have raised concerns regarding off-target toxicities resulting in lethal destruction of healthy tissue, highlighting the urgency of assessing which off-target peptides can be recognized by a TCR. As a model system we used the clinically efficacious NY-ESO-1-specific TCR C259, which recognizes the peptide epitope SLLMWITQC presented by HLA-A*02:01. We investigated which amino acids at each position enable a TCR interaction by sequentially replacing every amino acid position outside of anchor positions 2 and 9 with all 19 possible alternative amino acids, resulting in 134 peptides (133 altered peptides plus epitope peptide). Each peptide was individually evaluated using three different in vitro assays: binding of the NY-ESOc259 TCR to the peptide, peptide-dependent activation of TCR-expressing cells, and killing of peptide-presenting target cells. To represent the TCR recognition kernel, we defined Position Weight Matrices (PWMs) for each assay by assigning normalized measurements to each of the 20 amino acids in each position. To predict potential off-target peptides, we applied a novel algorithm projecting the PWM-defined kernel into the human proteome, scoring NY-ESOc259 TCR recognition of 336,921 predicted human HLA-A*02:01 binding 9-mer peptides. Of the 12 peptides with high predicted score, we confirmed 7 (including NY-ESO-1 antigen SLLMWITQC) strongly activate human primary NY-ESOc259-expressing T cells. These off-target peptides include peptides with up to 7 amino acid changes (of 9 possible), which could not be predicted using the recognition motif as determined by alanine scans. Thus, this replacement scan assay determines the “TCR fingerprint” and, when coupled with the algorithm applied to the database of human 9-mer peptides binding to HLA-A*02:01, enables the identification of potential off-target antigens and the tissues where they are expressed. This platform enables both screening of multiple TCRs to identify the best candidate for clinical development and identification of TCR-specific cross-reactive peptide recognition and constitutes an improved methodology for the identification of potential off-target peptides presented on MHC class I molecules.

Neoantigen Dissimilarity to the Self-Proteome Predicts Immunogenicity and Response to Immune Checkpoint Blockade.

Despite improved methods for MHC affinity prediction, the vast majority of computationally predicted tumor neoantigens are not immunogenic experimentally, indicating that high-quality neoantigens are beyond current algorithms to discern. To enrich for neoantigens with the greatest likelihood of immunogenicity, we developed an analytic method to parse neoantigen quality through rational biological criteria across five clinical datasets for 318 cancer patients. We explored four quality metrics, including analysis of dissimilarity to the non-mutated proteome that was predictive of peptide immunogenicity. In patient tumors, neoantigens with high dissimilarity were unique, enriched for hydrophobic sequences, and correlated with survival after PD-1 checkpoint therapy in patients with non-small cell lung cancer independent of predicted MHC affinity. We incorporated our neoantigen quality analysis methodology into an open-source tool, antigen.garnish, to predict immunogenic peptides from bulk computationally predicted neoantigens for which the immunogenic "hit rate" is currently low.

Collection of antimicrobial peptides database and its derivatives: Applications and beyond.

Collection of antimicrobial peptides (CAMP), CAMPSign, and ClassAMP are open-access resources that have been developed to enhance research on antimicrobial peptides (AMPs). Comprehensive information on AMPs and machine learning-based predictive models are made available for users through these resources. As of date, CAMPR3 has 10,247 sequences, 757 structures, and 114 family-specific signatures of AMPs along with associated tools for AMP sequence and structure analysis. CAMPSign uses family-specific sequence conservation, in the form of patterns and hidden Markov models for identification of AMPs. ClassAMP can be used to classify AMPs as antibacterial, antifungal, or antiviral based on sequence information. Here we describe CAMP and its derivatives and illustrate, with a few examples, the contribution of these online resources to the advancement of our current understanding of AMPs.

Retention Time Prediction and Protein Identification.

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in bottom-up proteomics.

Whole Genome Sequencing of the Giant Grouper (Epinephelus lanceolatus) and High-Throughput Screening of Putative Antimicrobial Peptide Genes.

Giant groupers, the largest grouper type in the world, are of economic importance in marine aquaculture for their rapid growth. At the same time, bacterial and viral diseases have become the main threats to the grouper industry. Here, we report a high-quality genome of a giant grouper sequenced by an Illumina HiSeq X-Ten and PacBio Bioscience Sequel platform. A total of 254 putative antimicrobial peptide (AMP) genes were identified, which can be divided into 34 classes according to the annotation of the Antimicrobial Peptides Database (APD3). Their locations in pseudochromosomes were also determined. Thrombin-, lectin-, and scolopendin-derived putative AMPs were the three largest parts. In addition, expressions of putative AMPs were measured by our transcriptome data. Two putative AMP genes (gapdh1 and gapdh2) were involved in glycolysis, which had extremely high expression levels in giant grouper muscle. As it has been reported that AMPs inhibit the growth of a broad spectrum of microbes and participate in regulating innate and adaptive immune responses, genome sequencing of this study provides a comprehensive cataloging of putative AMPs of groupers, supporting antimicrobial research and aquaculture therapy. These genomic resources will be beneficial to further molecular breeding of this economically important fish.

IHPDM: In Silico Human Proteome Digestion Map with Proteolytic Peptide Analysis and Graphical Visualizations

When conducting proteomics experiments to detect missing proteins and protein isoforms in the human proteome, it is desirable to use a protease that can yield more unique peptides with properties amenable for mass spectrometry analysis. Though trypsin is currently the most widely used protease, some proteins can yield only a limited number of unique peptides by trypsin digestion. Other proteases and multiple proteases have been applied in reported studies to increase the number of identified proteins and protein sequence coverage. To facilitate the selection of proteases, we developed a web-based resource, called in silico Human Proteome Digestion Map (iHPDM), which contains a comprehensive proteolytic peptide database constructed from human proteins, including isoforms, in neXtProt digested by 15 protease combinations of one or two proteases. iHPDM provides convenient functions and graphical visualizations for users to examine and compare the digestion results of different proteases. Notably, it also supports users to input filtering criteria on digested peptides, e.g., peptide length and uniqueness, to select suitable proteases. iHPDM can facilitate protease selection for shotgun proteomics experiments to identify missing proteins, protein isoforms, and single amino acid variant peptides.

Prediction of Potential Epitopes for Peptide Vaccine Formulation Against Teschovirus A Using Immunoinformatics.

Teschovirus A belongs to the family Picornaviridae and is a causal agent of the disease Teschovirus encephalomyelitis and other infections that remain asymptomatic. The present study was performed to design epitope-based peptide vaccine against Teschovirus A by identifying the potential T cell and B-cell epitopes from capsid proteins (VP1, VP3 and VP2) of the virus using reverse vaccinology and immunoinformatics approaches. In the current study, hexapeptide T-cell and octapeptide B-cell epitopes were analyzed for immunogenicity, antigenicity and hydrophilicity scores of each epitope. Each potential epitope was further characterized using ExPASy-ProtParam and Antimicrobial Peptide Database (APD3) tools for determining various physical and chemical parameters of the epitope. One linear hexapeptide T-cell epitope, i.e., RPVNDE (epitope position 77–82) and one linear octapeptide B-cell epitope, i.e., AYSRSHPQ (236–243) were identified from the viral capsid protein as they possess the capability to raise effective immunogenic reaction in the host organism against the virus. Pharmaceutical industries could harness the results of this investigation to develop epitope-based peptide vaccines by loading the identified epitopes in combination with targeting signal peptides of T-cells and B-cells and then inserting the combination into virus like particle (vlp) or constructing subunit vaccines for further trial.

The antimicrobial peptide database provides a platform for decoding the design principles of naturally occurring antimicrobial peptides.

This article is written for the 2020 tool issue of Protein Science. It briefly introduces the widely used antimicrobial peptide database, initially online in 2003. After a description of the main features of each database version and some recent additions, the focus is on the peptide design parameters for each of the four unified classes of natural antimicrobial peptides (AMPs). The amino acid signature in AMPs varies substantially, leading to a variety of structures for functional and mechanistic diversity. Also, Nature is a master of combinatorial chemistry by deploying different amino acids onto the same structural scaffold to tune peptide functions. In addition, the single-domain AMPs may be posttranslationally modified, self-assembled, or combined with other AMPs for function. Elucidation of the design principles of natural AMPs will facilitate future development of novel molecules for various applications.

Purification and Production of Novel Angiotensin I-Converting Enzyme (ACE) Inhibitory Bioactive Peptides Derived from Fermented Goat Milk

In the study, five different Lactobacillus cultures i.e. L. rhamnosus (NK2) (KR080695), L. casei (NK9) (KR732325), L. fermentum (M5) (KU366365), L. paracasei (M16) (KU366368), L. fermentum TDS030603 (MTCC 25067) (LF) were studied for their growth behavior, PepX and ACE-inhibitory activities and peptides production from fermented goat milk (Capra aegagrus hircus). The acidity, pH and lactic counts were evaluated for 0, 6, 12, 24 and 48 h at 37 °C. During their growth in heat treated goat milk (90 °C for 10-15 min), M5 significantly showed maximum titratable acidity (3.25%LA) and highest pH reduction (pH 3.10), while M16 exhibited highest lactic counts (9.35 log cfu/ml) up to 12 h of incubation (P < 0.05). LF significantly produced the highest PepX activity (0.667 O.D.) after 48 h of incubation than other cultures (P < 0.05). NK9 also significantly exhibited highest ACE-inhibitory activity (66.99%) after 24 h of incubation than other cultures (P < 0.05). However, M5 and M16 exhibited the second highest X-prolyl-dipeptidyl aminopeptidase (PepX) and angiotensin converting enzyme (ACE) inhibitory activity than other cultures during fermentation of goat milk. Purified peptides from fermented goat milk were isolated and characterized by amino acids profiling by searching on BIOPEP database, Protein information resource (PIR) database and Database of Antihypertensive peptides (AHTPDB). However, L. fermentum (M5) and L. paracasei (M16) could be explored for the production of ACE inhibitory peptides from fermented goat milk. Fermented goat milk produced by Lactobacillus cultures could be a novel source of ACE-inhibitory peptides.

MCtandem: an efficient tool for large-scale peptide identification on many integrated core (MIC) architecture

BackgroundTandem mass spectrometry (MS/MS)-based database searching is a widely acknowledged and widely used method for peptide identification in shotgun proteomics. However, due to the rapid growth of spectra data produced by advanced mass spectrometry and the greatly increased number of modified and digested peptides identified in recent years, the current methods for peptide database searching cannot rapidly and thoroughly process large MS/MS spectra datasets. A breakthrough in efficient database search algorithms is crucial for peptide identification in computational proteomics.ResultsThis paper presents MCtandem, an efficient tool for large-scale peptide identification on Intel Many Integrated Core (MIC) architecture. To support big data processing capability, a novel parallel match scoring algorithm, named MIC-SDP (spectrum dot product), and its two-level parallelization are presented in MCtandem’s design. In addition, a series of optimization strategies on both the host CPU side and the MIC side, which includes pre-fetching, optimized communication overlapping scheme, multithreading and hyper-threading, are exploited to improve the execution performance.ConclusionsFor fair comparisons, we first set up experiments and verified the 28 fold times speedup on a single MIC against the original CPU-based implementation. We then execute the MCtandem for a very large dataset on an MIC cluster (a component of the Tianhe-2 supercomputer) and achieved much higher scalability than in a benchmark MapReduce-based programs, MR-Tandem. MCtandem is an open-source software tool implemented in C++. The source code and the parameter settings are available at https://github.com/LogicZY/MCtandem.

Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, machine learning models (Random Forest (RF), AdaBoost Regressor (ABR), Gradient Boosting Regressor (GBR)), and Deep Learning models were utilized to validate the Docking results, and we obtained an R2 of 0.922 on the training set and 0.804 on the test set in the RF algorithm. For the Deep Learning algorithm, R2 of the training set is 0.90, and R2 of the test set is 0.810. However, these TCM compounds fly away during the molecular dynamics (MD) simulation. We seek another method: peptide design. All peptide database were screened by the Docking process. Modification peptides were optimized the interaction modes, and the affinities were assessed with ZDOCK protocol and Refine Docked protein protocol. The 300 ns MD simulation evaluated the stability of receptor-peptide complexes. The double-site effect appeared on S2, a designed peptide based on a known inhibitor, when complexed with BCL2. S3, a designed peptide referred from endogenous inhibitor P16, competed against cyclin when binding with CDK6. The MDM2 inhibitors S5 and S6 were derived from the P53 structure and stable binding with MDM2. A flexible region of peptides S5 and S6 may enhance the binding ability by changing its own conformation, which was unforeseen. These peptides (S2, S3, S5, and S6) are potentially interesting to treat cancer; however, these findings need to be affirmed by biological testing, which will be conducted in the near future.

FeptideDB: A web application for new bioactive peptides from food protein

BackgroundBioactive peptides derived from food are important sources for alternative medicine and possess therapeutic activity. Several biochemical methods have been achieved to isolate bioactive peptides from food, which are tedious and time consuming. In silico methods are an alternative process to reduce cost and time with respect to bioactive peptide production. In this paper, FeptideDB was used to collect bioactive peptide (BP) data from both published research articles and available bioactive peptide databases. FeptideDB was developed to assist in forecasting bioactive peptides from food by combining peptide cleavage tools and database matching. Furthermore, this application was able to predict the potential of cleaved peptides from ‘enzyme digestion module’ to identify new ACE (angiotensin converting enzyme) inhibitors using an automatic molecular docking approach. ResultsThe FeptideDB web application contains tools for generating all possible peptides cleaved from input protein by various available enzymes. This database was also used for analysis and visualization to assist in bioactive peptide discovery. One module of FeptideDB has the ability to create 3-dimensional peptide structures to further predict inhibitors for the target protein, ACE (angiotensin converting enzyme). ConclusionsFeptideDB is freely available to researchers who are interested in exploring bioactive peptides. The FeptideDB interface is easy to use, allowing users to rapidly retrieve data based on desired search criteria. FeptideDB is freely available at http://www4g.biotec.or.th/FeptideDB/. Ultimately, FeptideDB is a computational aid for assessing peptide bioactivities.

Abstract 568: Decoding shared antigenic epitopes and their cognate TCR genes in melanoma TILs using a library of paired human cell-based pHLA multimers and artificial APCs

Abstract HLA-restricted T cell responses toward immunogenic peptides, whether mutated or non-mutated, can induce antitumor responses in patients with advanced cancer. The fact that potential non-mutated antigens are greater in number than mutated antigens by multiple orders of magnitude and the high polymorphism of HLA genes may have hampered comprehensive analyses of the specificity of antitumor T cell responses toward non-mutated antigens than mutated antigens. Unlike shared antigens, the vast majority of neoantigens are not shared and are unique to each patient. The elucidation of T cell epitopes derived from shared antigens may facilitate the robust development of an efficacious and safe adoptive T cell therapy that is readily available to a larger cohort of cancer patients. It is well established that melanoma tumor-infiltrating T lymphocytes (TILs) contain antitumor T cells that are specific for both non-mutated and mutated antigens. The adoptive transfer of TILs can induce sustained clinical responses in some patients with advanced melanoma. However, a precise and extensive understanding of the shared antigen targets of TILs has been lacking. In this study, TILs were isolated from 8 metastatic melanoma patients, polyclonally expanded in vitro, and their shared antigen specificities for all 45 (25 different) class I alleles were examined. The combination of structure-based analysis using human cell-based peptide/HLA (pHLA) multimers and functional analysis using artificial antigen-presenting cells (APCs) were used to determine antigen-specific T cell responses. We were able to determine the specificity of 12.2 ± 7.2% (mean ± SD, max 25.8%, min 4.6%) of CD8+ T cells in an expanded TIL culture toward 3.0 ± 1.8 (mean ± SD, max 6, min 1) previously known or novel peptides derived from shared antigens. Furthermore, we isolated a number of cognate TCR genes with potent tumor reactivity from the CD8+ T cells. The strategy employed in this study using a library of paired human cell-based pHLA multimers and artificial APCs has enabled us to decipher the antigen specificity of tumor-specific T cells for any given HLA class I allele, regardless of allele frequency and for an infinite number of peptides. It has also allowed us to build a large database of class I-restricted peptides and cognate tumor-reactive TCR genes at an unprecedented scale. This database will be a valuable tool in defining the immune response at the level of each individual cancer patient with precision as well as the identification and validation of biomarkers to aid in patient-based selection of a cancer immunotherapy regimen. Furthermore, this database will help the robust development of novel cancer vaccines and TCR gene therapies for patients with a low mutation burden. Citation Format: Kenji Murata, Kayoko Saso, Linh T. Nguyen, Douglas Millar, Marcus O. Butler, Pamela S. Ohashi, Naoto Hirano. Decoding shared antigenic epitopes and their cognate TCR genes in melanoma TILs using a library of paired human cell-based pHLA multimers and artificial APCs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 568.