# Deep Mutational Scanning in Immunology: Techniques and Applications

**Authors:** Chengwei Shao, Siyue Jia, Yue Li, Jingxin Li

PMC · DOI: 10.3390/pathogens14101027 · Pathogens · 2025-10-10

## TL;DR

This paper reviews deep mutational scanning techniques and their use in studying immune-related proteins to understand how mutations affect immune system function.

## Contribution

The paper provides a comprehensive overview of deep mutational scanning methods and their immunological applications, highlighting current limitations and future directions.

## Key findings

- Deep mutational scanning evaluates mutations' effects on immune proteins using saturation mutagenesis and high-throughput sequencing.
- Current limitations include data biases, library coverage issues, and system-induced signals that affect result accuracy.
- The technology has potential for broader immunological research but requires further development to overcome existing constraints.

## Abstract

Mutations may cause changes in the structure and function of immune-related proteins, thereby affecting the operation of the immune system. Deep mutational scanning combines saturation mutagenesis, functional selection, and high-throughput sequencing to evaluate the effects of mutations on a large scale and with high resolution. By systematically and comprehensively analyzing the impact of mutations on the functions of immune-related proteins, the immune response mechanism can be better understood. However, each stage in deep mutation scanning has its limits, and the approach remains constrained in several ways. These include data and selection biases that affect the robustness of effect estimates, insufficient library coverage and editability leading to uneven representation of sites and alleles, system-induced biased signals that deviate phenotypes from their true physiological state, and imperfect models and statistical processing that limit extrapolation capabilities. Therefore, this technology still needs further development. Herein, we summarize the principles and methods of deep mutational scanning and discuss its application in immunological research. The aim is to provide insights into the broader application prospects of deep mutational scanning technology in immunology.

## Full-text entities

- **Genes:** PTEN (phosphatase and tensin homolog) [NCBI Gene 5728] {aka 10q23del, BZS, CWS1, DEC, GLM2, MHAM}, TP53 (tumor protein p53) [NCBI Gene 7157] {aka BCC7, BMFS5, LFS1, P53, TRP53}, ACE2 (angiotensin converting enzyme 2) [NCBI Gene 59272] {aka ACEH}, VEGFA (vascular endothelial growth factor A) [NCBI Gene 7422] {aka L-VEGF, MVCD1, VEGF, VPF}, TRBV20OR9-2 (T cell receptor beta variable 20/OR9-2 (non-functional)) [NCBI Gene 6962] {aka CDR3, TCRBV20S2, TCRBV2O, TCRBV2S2O}, ANGPT2 (angiopoietin 2) [NCBI Gene 285] {aka AGPT2, ANG2, LMPHM10}, PDCD1 (programmed cell death 1) [NCBI Gene 5133] {aka ADMIO4, AIMTBS, CD279, PD-1, PD1, SLEB2}, HLA-C (major histocompatibility complex, class I, C) [NCBI Gene 3107] {aka D6S204, HLA-JY3, HLAC, HLC-C, MHC, PSORS1}, ERVK-6 (endogenous retrovirus group K member 6, envelope) [NCBI Gene 64006] {aka ERVK6, HERV-K(C7), HERV-K108, K-Rev, c-orf, cORF}, TNF (tumor necrosis factor) [NCBI Gene 7124] {aka DIF, IMD127, TNF-alpha, TNFA, TNFSF2, TNLG1F}, BRCA1 (BRCA1 DNA repair associated) [NCBI Gene 672] {aka BRCAI, BRCC1, BROVCA1, FANCS, IRIS, PNCA4}, TNFRSF1A (TNF receptor superfamily member 1A) [NCBI Gene 7132] {aka CD120a, FPF, TBP1, TNF-R, TNF-R-I, TNF-R55}, HLA-A (major histocompatibility complex, class I, A) [NCBI Gene 3105] {aka HLAA}, EGFR (epidermal growth factor receptor) [NCBI Gene 1956] {aka ERBB, ERBB1, ERRP, HER1, NISBD2, NNCIS}, CD4 (CD4 molecule) [NCBI Gene 920] {aka CD4mut, IMD79, Leu-3, OKT4D, T4}, TACSTD2 (tumor associated calcium signal transducer 2) [NCBI Gene 4070] {aka EGP-1, EGP1, GA733-1, GA7331, GP50, M1S1}, PDCD1LG2 (programmed cell death 1 ligand 2) [NCBI Gene 80380] {aka B7DC, Btdc, CD273, PD-L2, PDCD1L2, PDL2}
- **Diseases:** -related diseases (MESH:D000077733), genetic diseases (MESH:D030342), autoimmune disease (MESH:D001327), neurotoxicity (MESH:D020258), cancer (MESH:D009369), age-related macular degeneration (MESH:D008268), injury to (MESH:D014947), inflammation (MESH:D007249), influenza (MESH:D007251), organ damage (MESH:D000092124), infected (MESH:D007239), immune diseases (MESH:D007154), toxicity (MESH:D064420), viral infection (MESH:D014777)
- **Chemicals:** infliximab (MESH:D000069285), water (MESH:D014867), adalimumab (MESH:D000068879), hydrogen (MESH:D006859), cetuximab (MESH:D000068818), DMS (-), disulfide (MESH:D004220)
- **Species:** Human immunodeficiency virus 1 (no rank) [taxon 11676], Pseudovirus (genus) [taxon 186672], H1N1 subtype (serotype) [taxon 114727], Severe acute respiratory syndrome coronavirus 2 (no rank) [taxon 2697049], Mus musculus (house mouse, species) [taxon 10090], Saccharomyces cerevisiae (baker's yeast, species) [taxon 4932], H3N2 subtype (serotype) [taxon 119210], Zika virus (no rank) [taxon 64320], Influenza A virus (no rank) [taxon 11320], Homo sapiens (human, species) [taxon 9606]
- **Mutations:** E484K
- **Cell lines:** Jurkat — Homo sapiens (Human), Childhood T acute lymphoblastic leukemia, Cancer cell line (CVCL_0065)

## Full text

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## Figures

1 figure with captions in the complete paper: https://tomesphere.com/paper/PMC12567119/full.md

## References

101 references — full list in the complete paper: https://tomesphere.com/paper/PMC12567119/full.md

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Source: https://tomesphere.com/paper/PMC12567119