H-KLGGALQAK-OH

Ref. 3D-PP43331

Produktinformation

• Name: H-KLGGALQAK-OH
• Synonyme: NH2-Lys-Leu-Gly-Gly-Ala-Leu-Gln-Ala-Lys-OH

Beschreibung

Peptide H-KLGGALQAK-OH is a Research Peptide with significant interest within the field academic and medical research. This peptide is available for purchase at Cymit Quimica in multiple sizes and with a specification of your choice. Recent citations using H-KLGGALQAK-OH include the following: DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis Y Zhao , B He , F Xu, C Li , Z Xu, X Su, H He, Y Huang - Science , 2023 - science.orghttps://www.science.org/doi/abs/10.1126/sciadv.abo5128 TEINet: a deep learning framework for prediction of TCR-epitope binding specificity Y Jiang , M Huo, S Cheng Li - Briefings in Bioinformatics, 2023 - academic.oup.comhttps://academic.oup.com/bib/article-abstract/24/2/bbad086/7076118 Human thymopoiesis produces polyspecific CD8+ alpha/beta T cells responding to multiple viral antigens V Quiniou, P Barennes, V Mhanna, P Stys - Elife, 2023 - elifesciences.orghttps://elifesciences.org/articles/81274 Unseen Epitope-TCR Interaction Prediction based on Amino Acid Physicochemical Properties R Raha, Y Ding , Q Liu , FX Wu - 2022 IEEE International , 2022 - ieeexplore.ieee.orghttps://ieeexplore.ieee.org/abstract/document/9995066/ Enhancing TCR specificity predictions by combined pan-and peptide-specific training, loss-scaling, and sequence similarity integration MF Jensen, M Nielsen - Elife, 2024 - elifesciences.orghttps://elifesciences.org/articles/93934 NetTCR 2.2-Improved TCR specificity predictions by combining pan-and peptide-specific training strategies, loss-scaling and integration of sequence similarity MF Jensen, M Nielsen - bioRxiv, 2023 - biorxiv.orghttps://www.biorxiv.org/content/10.1101/2023.10.12.562001.abstract TCRpcDist: estimating TCR physico-chemical similarity to analyze repertoires and predict specificities MAS Perez, J Chiffelle , S Bobisse, F Mayol-Rullan - bioRxiv, 2023 - biorxiv.orghttps://www.biorxiv.org/content/10.1101/2023.06.15.545077.abstract ATM-TCR: TCR-epitope binding affinity prediction using a multi-head self-attention model M Cai, S Bang , P Zhang, H Lee - Frontiers in immunology, 2022 - frontiersin.orghttps://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.893247 Clonally focused public and private T cells in resected brain tissue from surgeries to treat children with intractable seizures JW Chang, SD Reyes, E Faure-Kumar - Frontiers in , 2021 - frontiersin.orghttps://www.frontiersin.org/articles/10.3389/fimmu.2021.664344/full Clonally Focused Public and Private T Cells in Resected Brain Tissue From Surgeries to JW Chang, SD Reyes, E Faure-Kumar - 2021 - scholar.archive.orghttps://scholar.archive.org/work/jxj6trqn2fczfi74ebpofb43fa/access/wayback/https://escholarship.org/content/qt4qh2m4x9/qt4qh2m4x9.pdf?t=qzpyuu TPBTE: A model based on convolutional Transformer for predicting the binding of TCR to epitope J Wu, M Qi , F Zhang, Y Zheng - Molecular Immunology, 2023 - Elsevierhttps://www.sciencedirect.com/science/article/pii/S0161589023000536 Machine Learning Prediction of TCR-Epitope Binding J Faust, YS Song - 2022 - eecs.berkeley.eduhttps://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-216.pdf DECODE: a computational pipeline to discover T cell receptor binding rules I Papadopoulou, AP Nguyen , A Weber - , 2022 - academic.oup.comhttps://academic.oup.com/bioinformatics/article-abstract/38/Supplement_1/i246/6617535 Predicting TCR sequences for unseen antigen epitopes using structural and sequence features H Zhang, H Ji, C Zhang, Z Qiong - 2024 - researchsquare.comhttps://www.researchsquare.com/article/rs-3891946/latest Predicting TCR sequences for unseen antigen epitopes using structural and sequence features H Ji, XX Wang, Q Zhang, C Zhang - Briefings in , 2024 - academic.oup.comhttps://academic.oup.com/bib/article-abstract/25/3/bbae210/7665592 Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells G Croce , S Bobisse, DL Moreno, J Schmidt - Nature , 2024 - nature.comhttps://www.nature.com/articles/s41467-024-47461-8 TCRfp: a new fingerprint-based approach for TCR repertoire analysis F Mayol-Rullan, M Bugnon , MAS Perez, V Zoete - bioRxiv, 2023 - biorxiv.orghttps://www.biorxiv.org/content/10.1101/2023.12.19.572261.abstract TAPIR: a T-cell receptor language model for predicting rare and novel targets E Fast , M Dhar, B Chen - bioRxiv, 2023 - ncbi.nlm.nih.govhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515850/ A comparison of clustering models for inference of T cell receptor antigen specificity D Hudson, A Lubbock, M Basham , H Koohy - ImmunoInformatics, 2024 - Elsevierhttps://www.sciencedirect.com/science/article/pii/S266711902400003X STAPLER: efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data BPY Kwee, M Messemaker , E Marcus , G Oliveira - bioRxiv, 2023 - biorxiv.orghttps://www.biorxiv.org/content/10.1101/2023.04.25.538237.abstract

• Marke: Biosynth