Mutational fitness landscapes reveal genetic and structural improvement pathways for a vaccine-elicited HIV-1 broadly neutralizing antibody.

Vaccine-based elicitation of broadly neutralizing antibodies holds great promise for preventing HIV-1 transmission. However, the key biophysical markers of improved antibody recognition remain uncertain in the diverse landscape of potential antibody mutation pathways, and a more complete understanding of anti-HIV-1 fusion peptide (FP) antibody development will accelerate rational vaccine designs. Here we survey the mutational landscape of the vaccine-elicited anti-FP antibody, vFP16.02, to determine the genetic, structural, and functional features associated with antibody improvement or fitness. Using site-saturation mutagenesis and yeast display functional screening, we found that 1% of possible single mutations improved HIV-1 envelope trimer (Env) affinity, but generally comprised rare somatic hypermutations that may not arise frequently in vivo. We observed that many single mutations in the vFP16.02 Fab could enhance affinity >1,000-fold against soluble FP, although affinity improvements against the HIV-1 trimer were more measured and rare. The most potent variants enhanced affinity to both soluble FP and Env, had mutations concentrated in antibody framework regions, and achieved up to 37% neutralization breadth compared to 28% neutralization of the template antibody. Altered heavy- and light-chain interface angles and conformational dynamics, as well as reduced Fab thermal stability, were associated with improved HIV-1 neutralization breadth and potency. We also observed parallel sets of mutations that enhanced viral neutralization through similar structural mechanisms. These data provide a quantitative understanding of the mutational landscape for vaccine-elicited FP-directed broadly neutralizing antibody and demonstrate that numerous antigen-distal framework mutations can improve antibody function by enhancing affinity simultaneously toward HIV-1 Env and FP.

Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients.

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions.

Multimodal emotion recognition has gained much traction in the field of affective computing, human-computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Putative Biosynthesis of Talarodioxadione & Talarooxime from Talaromyces stipitatus.

Polyesters containing 2,4-dihydroxy-6-(2-hydroxypropyl)benzoate and 3-hydroxybutyrate moieties have been isolated from many fungal species. Talaromyces stipitatus was previously reported to produce a similar polyester, talapolyester G. The complete genome sequence and the development of bioinformatics tools have enabled the discovery of the biosynthetic potential of this microorganism. Here, a putative biosynthetic gene cluster (BGC) of the polyesters encoding a highly reducing polyketide synthase (HR-PKS) and nonreducing polyketide synthase (NR-PKS), a cytochrome P450 and a regulator, was identified. Although talapolyester G does not require an oxidative step for its biosynthesis, further investigation into the secondary metabolite production of T. stipitatus resulted in isolating two new metabolites called talarodioxadione and talarooxime, in addition to three known compounds, namely 6-hydroxymellein, 15G256α and transtorine that have never been reported from this organism. Interestingly, the biosynthesis of the cyclic polyester 15G256α requires hydroxylation of an inactive methyl group and thus could be a product of the identified gene cluster. The two compounds, talarooxime and transtorine, are probably the catabolic metabolites of tryptophan through the kynurenine pathway. Tryptophan metabolism exists in almost all organisms and has been of interest to many researchers. The biosynthesis of the new oxime is proposed to involve two subsequent N-hydroxylation of 2-aminoacetophenone.

A practical approach towards causality mining in clinical text using active transfer learning.

OBJECTIVE: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge. METHODS: A practical approach based on term expansion, phrase generation, BERT based phrase embedding and semantic matching, semantic enrichment, expert verification, and model evolution has been used to construct a comprehensive causality mining framework. This active transfer learning based framework along with its supplementary services, is able to extract and enrich, causal relationships and their corresponding entities from clinical text. RESULTS: The multi-model transfer learning technique when applied over multiple iterations, gains substantial performance improvements. We also present a comparative analysis of the presented techniques with their common alternatives, which demonstrate the correctness of our approach and its ability to capture most causal relationships. CONCLUSION: The presented framework has provided cutting-edge results in the healthcare domain. However, the framework can be tweaked to provide causality detection in other domains, as well. SIGNIFICANCE: The presented framework is generic enough to be utilized in any domain, healthcare services can gain massive benefits due to the voluminous and various nature of its data. This causal knowledge extraction framework can be used to summarize clinical text, create personas, discover medical knowledge, and provide evidence to clinical decision making.

A fuzzy TOPSIS based analysis toward selection of effective security requirements engineering approach for trustworthy healthcare software development.

BACKGROUND: Today's healthcare organizations want to implement secure and quality healthcare software as cyber-security is a significant risk factor for healthcare data. Considering security requirements during trustworthy healthcare software development process is an essential part of the quality software development. There are several Security Requirements Engineering (SRE) methodologies, framework, process, standards available today. Unfortunately, there is still a necessity to improve these security requirements engineering approaches. Determining the most suitable security requirements engineering method for trustworthy healthcare software development is a challenging process. This study is aimed to present security experts' perspective on the relative importance of the criteria for selecting effective SRE method by utilizing the multi-criteria decision making methods. METHODS: The study was planned and conducted to identify the most appropriate SRE approach for quality and trustworthy software development based on the security expert's knowledge and experience. The hierarchical model was evaluated by using fuzzy TOPSIS model. Effective SRE selection criteria were compared in pairs. 25 security experts were asked to response the pairwise criteria comparison form. RESULTS: The impact of the recognized selection criteria for effective security requirements engineering approaches has been evaluated quantitatively. For each of the 25 participants, comparison matrixes were formed based on the scores of their responses in the form. The consistency ratios (CR) were found to be smaller than 10% (CR = 9.1% < 10%). According to pairwise comparisons result; with a 0.842 closeness coefficient (Ci), STORE methodology is the most effective security requirements engineering approach for trustworthy healthcare software development. CONCLUSIONS: The findings of this research study demonstrate various factors in the decision-making process for the selection of a reliable method for security requirements engineering. This is a significant study that uses multi-criteria decision-making tools, specifically fuzzy TOPSIS, which used to evaluate different SRE methods for secure and trustworthy healthcare application development.

Report- Role of prostaglandin H synthase in activation of acrylonitrile to cyanide.

The present study aimed at investigating the in-vitro oxidation of acrylonitrile (ACN) to cyanide (CN-) by prostaglandin H synthase (PHS). Detection of CN- is considered a marker for free radical intermediates involved in ACN-induced toxicity. First, most favorable circumstances for ACN oxidation were characterized: pH (4.5), temperature (37ºC) and time of incubation (60 min.). In addition, the concentrations of ACN, PHS and H2O2 in incubation mixtures were assessed for further reaction characterization. The reaction maximum velocity (Vmax) was calculated to be 582.75 pmol CN-/mL/min and the Michaelis-Menten constant (Km) was 149.25 µmol ACN. Adding PHS inhibitors; resveratrol, quercetin, indomethacin or troloc-C to the reaction mixtures significantly reduced the rate of ACN oxidation. In conclusion, the present study demonstrates the ability of PHS to oxidize ACN to CN- and provides a clue for the explanation of ACN target toxicity.

Variations in Essential Oil Compositions of Lavandula pubescens (Lamiaceae) Aerial Parts Growing Wild in Yemen.

Lavandula pubescens Decne. is one of five Lavandula species growing wild in Yemen. The plant is used in Yemeni traditional medicine, and the essential oil tends to be rich in carvacrol. In this work, L. pubescens was collected from eight different locations in Yemen, the essential oils obtained by hydrodistillation, and the oils analyzed by gas chromatography/mass spectrometry (GC/MS). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to differentiate between the L. pubescens samples. The essential oils were rich in carvacrol (60.9 - 77.5%), with lesser concentrations of carvacrol methyl ether (4.0 - 11.4%), caryophyllene oxide (2.1 - 6.9%), and terpinolene (0.6 - 9.2%). The essential oil compositions in this study showed very high similarity, but it was possible to discern two separate groups based on minor components, in particular the concentrations of terpinolene, carvacrol methyl ether, m-cymen-8-ol, and caryophyllene oxide.

A qualitative and quantitative analysis of radiation dose and image quality of computed tomography images using adaptive statistical iterative reconstruction.

Image quality is a key issue in radiology, particularly in a clinical setting where it is important to achieve accurate diagnoses while minimizing radiation dose. Some computed tomography (CT) manufacturers have introduced algorithms that claim significant dose reduction. In this study, we assessed CT image quality produced by two reconstruction algorithms provided with GE Healthcare's Discovery 690 Elite positron emission tomography (PET) CT scanner. Image quality was measured for images obtained at various doses with both conventional filtered back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR) algorithms. A stan-dard CT dose index (CTDI) phantom and a pencil ionization chamber were used to measure the CT dose at 120 kVp and an exposure of 260 mAs. Image quality was assessed using two phantoms. CT images of both phantoms were acquired at tube voltage (kV) of 120 with exposures ranging from 25 mAs to 400 mAs. Images were reconstructed using FBP and ASIR ranging from 10% to 100%, then analyzed for noise, low-contrast detectability, contrast-to-noise ratio (CNR), and modulation transfer function (MTF). Noise was 4.6 HU in water phantom images acquired at 260 mAs/FBP 120 kV and 130 mAs/50% ASIR 120 kV. The large objects (fre-quency < 7 lp/cm) retained fairly acceptable image quality at 130 mAs/50% ASIR, compared to 260 mAs/FBP. The application of ASIR for small objects (frequency >7 lp/cm) showed poor visibility compared to FBP at 260 mAs and even worse for images acquired at less than 130 mAs. ASIR blending more than 50% at low dose tends to reduce contrast of small objects (frequency >7 lp/cm). We concluded that dose reduction and ASIR should be applied with close attention if the objects to be detected or diagnosed are small (frequency > 7 lp/cm). Further investigations are required to correlate the small objects (frequency > 7 lp/cm) to patient anatomy and clinical diagnosis.

Genetic, molecular, and biochemical basis of fungal tropolone biosynthesis.

A gene cluster encoding the biosynthesis of the fungal tropolone stipitatic acid was discovered in Talaromyces stipitatus (Penicillium stipitatum) and investigated by targeted gene knockout. A minimum of three genes are required to form the tropolone nucleus: tropA encodes a nonreducing polyketide synthase which releases 3-methylorcinaldehyde; tropB encodes a FAD-dependent monooxygenase which dearomatizes 3-methylorcinaldehyde via hydroxylation at C-3; and tropC encodes a non-heme Fe(II)-dependent dioxygenase which catalyzes the oxidative ring expansion to the tropolone nucleus via hydroxylation of the 3-methyl group. The tropA gene was characterized by heterologous expression in Aspergillus oryzae, whereas tropB and tropC were successfully expressed in Escherichia coli and the purified TropB and TropC proteins converted 3-methylorcinaldehyde to a tropolone in vitro. Finally, knockout of the tropD gene, encoding a cytochrome P450 monooxygenase, indicated its place as the next gene in the pathway, probably responsible for hydroxylation of the 6-methyl group. Comparison of the T. stipitatus tropolone biosynthetic cluster with other known gene clusters allows clarification of important steps during the biosynthesis of other fungal compounds including the xenovulenes, citrinin, sepedonin, sclerotiorin, and asperfuranone.

The biosynthesis and catabolism of the maleic anhydride moiety of stipitatonic acid.

A series of directed knockout experiments, combined with an in vitro assay of pathway components, has elucidated for the first time the chemical steps involved in the biosynthesis of the tropolone class of fungal maleic anhydrides. The pathway involves the stepwise oxidation of aldehyde and methyl carbon atoms to form a 1,2-dicarboxylate. A hydrolase-catalyzed interconversion of this and the corresponding maleic anhydride, followed by decarboxylation of the diacid leads to the pathway's final product of stipitatic acid.

Design and Analysis of Multi-Analyte Detection Based Biosensor in the Visible to Near-Infrared (VNIR) Region.

This manuscript introduces a highly sensitive dual-core photonic crystal fiber (PCF) based multi-analyte surface plasmon resonance (SPR) sensor, possessing the ability to detect multiple analytes at once. A chemically stable thin plasmonic substance of gold (Au) layer, holding a thickness of 30 nm, is employed to the outer portion of the stated design that manifests a negative real permittivity. Moreover, an ultra-thin film of aluminum oxide (Al2O3) , having a thickness of 10 nm, is inserted into the exterior of the gold film to calibrate the resonance wavelength as well as magnify the coupling strength. The performance of the sensor is rigorously explored employing the finite element method (FEM), where numerical investigation confirms that the intended sensor model exhibits a peak amplitude sensitivity (AS) of 2606 RIU-1 , as well as a highest wavelength sensitivity (WS) of 20,000 nm/RIU. The achieved outcomes affirm that the sensor design can be conceivably applied in numerous biological; as well as biochemical analyte refractive index (RI) detection to realize the relevant significant applications in the visible to near-infrared (VNIR) region of 0.5 to [Formula: see text].

AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features.

Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Serum amyloid-A protein and serum rheumatoid factor as serological surrogate markers for smoking risk factor in Saudi population.

Tobacco smoking represents major national and international health hazard that interferes with wide range of physiological functions and biomarkers. In the current study we have investigated the influence of tobacco smoking on some biological markers such as serum amyloid protein-A, rheumatoid factor, serum glucose level and lipid profile in Saudi population. The fore mentioned parameters were investigated in a total of 275 cases in 3 different age categories (less than 20 years old, 20-40 years old and older than 40 years old). Long term survey was adopted in all cases; yet, lightly smoking and heavily smoking groups were compared to never smoking healthy population. Results obtained showed significant increase in serum amyloid protein-A and rheumatoid factor in correlation to the degree of smoking nonetheless in the age category older than 40 years old. Serum glucose, triglyceride, and total cholesterol was not affected by smoking in all studied age categories; however serum LDL-cholesterol was elevated and serum HDL-cholesterol was depressed in correlation to the degree of smoking in all age categories. In conclusion, tobacco smoking represents major cardiovascular risk factor in Saudi population in all age categories and serum amyloid protein-A and rheumatoid factor might be used as a serological surrogate marker for such risk.

Bioinformatic Analysis of Natively Paired VH:VL Antibody Repertoires for Antibody Discovery.

Next-generation DNA sequencing (NGS) of human antibody repertoires has been extensively implemented to discover novel antibody drugs, to analyze B-cell developmental features, and to investigate antibody responses to infectious diseases and vaccination. Because the antibody repertoire encoded by human B cells is highly diverse, NGS analyses of antibody genes have provided a new window into understanding antibody responses for basic immunology, biopharmaceutical drug discovery, and immunotherapy. However, many antibody discovery protocols analyze the heavy and light chains separately due to the short-read nature of most NGS technologies, whereas paired heavy and light chain data are required for complete antibody characterization. Here, we describe a computational workflow to process millions of paired antibody heavy and light chain DNA sequence reads using the Illumina MiSeq 2x300 NGS platform. In this workflow, we describe raw NGS read processing and initial quality filtering, the annotation and assembly of antibody clonotypes relating to paired heavy and light chain antibody lineages, and the generation of complete heavy+light consensus sequences for the downstream cloning and expression of human antibody proteins.

StackDPP: Stacking-Based Explainable Classifier for Depression Prediction and Finding the Risk Factors among Clinicians.

Mental health is a major concern for all classes of people, but especially physicians in the present world. A challenging task is to identify the significant risk factors that are responsible for depression among physicians. To address this issue, the study aimed to build a machine learning-based predictive model that will be capable of predicting depression levels and finding associated risk factors. A raw dataset was collected to conduct this study and preprocessed as necessary. Then, the dataset was divided into 10 sub-datasets to determine the best possible set of attributes to predict depression. Seven different classification algorithms, KNN, DT, LGBM, GB, RF, ETC, and StackDPP, were applied to all the sub-datasets. StackDPP is a stacking-based ensemble classifier, which is proposed in this study. It was found that StackDPP outperformed on all the datasets. The findings indicate that the StackDPP with the sub-dataset with all the attributes gained the highest accuracy (0.962581), and the top 20 attributes were enough to gain 0.96129 accuracy by StackDPP, which was close to the performance of the dataset with all the attributes. In addition, risk factors were analyzed in this study to reveal the most significant risk factors that are responsible for depression among physicians. The findings of the study indicate that the proposed model is highly capable of predicting the level of depression, along with finding the most significant risk factors. The study will enable mental health professionals and psychiatrists to decide on treatment and therapy for physicians by analyzing the depression level and finding the most significant risk factors.

Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses.

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large. The available virus detection techniques take a long time, are costly, and complex and some of them generates false negative or false positive that might cost patients their lives. The biosensor technique is one of the best qualified to address this difficult challenge. In this systematic review, we have summarized recent advancements in biosensor-based detection of these pandemic viruses including COVID-19. Biosensors are emerging as efficient and economical analytical diagnostic instruments for early-stage illness detection. They are highly suitable for applications related to healthcare, wearable electronics, safety, environment, military, and agriculture. We strongly believe that these insights will aid in the study and development of a new generation of adaptable virus biosensors for fellow researchers.

One-Step Solution Deposition of Tin-Perovskite onto a Self-Assembled Monolayer with a DMSO-Free Solvent System.

We show for the first time DMSO-free tin-based perovskite solar cells with a self-assembled hole selective contact (MeO-2PACz). Our method provides reproducible and hysteresis-free devices with MeO-2PACz, having the best device PCE of 5.8 % with a VOC of 638 mV.

Large-scale antibody immune response mapping of splenic B cells and bone marrow plasma cells in a transgenic mouse model.

Molecular characterization of antibody immunity and human antibody discovery is mainly carried out using peripheral memory B cells, and occasionally plasmablasts, that express B cell receptors (BCRs) on their cell surface. Despite the importance of plasma cells (PCs) as the dominant source of circulating antibodies in serum, PCs are rarely utilized because they do not express surface BCRs and cannot be analyzed using antigen-based fluorescence-activated cell sorting. Here, we studied the antibodies encoded by the entire mature B cell populations, including PCs, and compared the antibody repertoires of bone marrow and spleen compartments elicited by immunization in a human immunoglobulin transgenic mouse strain. To circumvent prior technical limitations for analysis of plasma cells, we applied single-cell antibody heavy and light chain gene capture from the entire mature B cell repertoires followed by yeast display functional analysis using a cytokine as a model immunogen. We performed affinity-based sorting of antibody yeast display libraries and large-scale next-generation sequencing analyses to follow antibody lineage performance, with experimental validation of 76 monoclonal antibodies against the cytokine antigen that identified three antibodies with exquisite double-digit picomolar binding affinity. We observed that spleen B cell populations generated higher affinity antibodies compared to bone marrow PCs and that antigen-specific splenic B cells had higher average levels of somatic hypermutation. A degree of clonal overlap was also observed between bone marrow and spleen antibody repertoires, indicating common origins of certain clones across lymphoid compartments. These data demonstrate a new capacity to functionally analyze antigen-specific B cell populations of different lymphoid organs, including PCs, for high-affinity antibody discovery and detailed fundamental studies of antibody immunity.

Cell activation-based screening of natively paired human T cell receptor repertoires.

Adoptive immune therapies based on the transfer of antigen-specific T cells have been used successfully to treat various cancers and viral infections, but improved techniques are needed to identify optimally protective human T cell receptors (TCRs). Here we present a high-throughput approach to the identification of natively paired human TCRα and TCRß (TCRα:ß) genes encoding heterodimeric TCRs that recognize specific peptide antigens bound to major histocompatibility complex molecules (pMHCs). We first captured and cloned TCRα:ß genes from individual cells, ensuring fidelity using a suppression PCR. We then screened TCRα:ß libraries expressed in an immortalized cell line using peptide-pulsed antigen-presenting cells and sequenced activated clones to identify the cognate TCRs. Our results validated an experimental pipeline that allows large-scale repertoire datasets to be annotated with functional specificity information, facilitating the discovery of therapeutically relevant TCRs.