Multi-Scale Representation Learning on Proteins

Somnath, Vignesh Ram; Bunne, Charlotte; Krause, Andreas

doi:10.48550/arxiv.2204.02337

Cited by 1 publication

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“…One example of a dual methodology is a multiscale graph construction of HOLOPROT ( 120 ), which connects surface to structure and sequence, demonstrates the utility of hierarchical representations for binding and function prediction. Using geometric deep learning and mesh CNN ( 55 , 56 ) embed protein surface patches into fingerprints for fast scanning and binding site identification, eliminating the need for hand-crafted or expensive pre-computed features.…”

Section: Tcr-pmhc Specificity Prediction Methodsmentioning

confidence: 99%

Quantitative approaches for decoding the specificity of the human T cell repertoire

Ghoreyshi,

George

2023

Front. Immunol.

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T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method’s mathematical approach, predictive performance, and limitations.

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Section: Tcr-pmhc Specificity Prediction Methodsmentioning

confidence: 99%