Convergent Sequence Features of Antiviral B Cells

Abu-Shmais, Alexandra A.; Vukovich, Matthew J.; Wasdin, Perry T.; Suresh, Yukthi P.; Rush, Scott A.; Gillespie, Rebecca A.; Sankhala, Rajeshwer S.; Choe, Misook; Joyce, M. Gordon; Kanekiyo, Masaru; McLellan, Jason S.; Georgiev, Ivelin S.

doi:10.1101/2023.09.06.556442

Cited by 2 publications

(1 citation statement)

References 58 publications

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“…In the future, large-scale antigen-specific AIRR data both on the receptor and repertoire level will be necessary to understand immune signals better. More specifically, on the repertoire-level, the dynamics of repertoire structure as a function of antigen exposure require further investigation (Abu-Shmais et al 2023). On the receptor level, we need to better understand the long-range dependencies that govern paratope-epitope interaction, using structural (Akbar et al 2021) and deep-mutational scanning screens (Mason et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Chernigovskaya,

Pavlović,

Kanduri

et al. 2023

Preprint

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Machine-learning methods (ML) have shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML approaches where experimental data is inaccessible or insufficient as of yet. The challenge for simulated data to be useful is the ability to incorporate key features observed in experimental repertoires. These features, such as complex antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a modular software suite, which simulates AIRR data for the development and benchmarking of AIRR-based machine learning. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO simulation supports the development and validation of AIRR-ML methods: (1) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (2) how immune information co-occurring in the same AIRs have an impact on the performance of conventional receptor-level encoding and repertoire-level classification approaches. The LIgO software guides the advancement and assessment of interpretable AIRR-ML methods.

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Section: Discussionmentioning

confidence: 99%

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Chernigovskaya,

Pavlović,

Kanduri

et al. 2023

Preprint

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Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq

Wasdin,

Abu-Shmais,

Irvin

et al. 2023

Preprint

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LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in LIBRA-seq antigen count data is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies. In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages the VRC01 negative control cells included in a recent LIBRA-seq study(Abu-Shmais et al.) to provide a data-driven means for identification of technical noise. We apply this method to a dataset of nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method used in LIBRA-seq, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for future machine learning based approaches to predicting antibody-antigen binding as the corpus of LIBRA-seq data continues to grow.

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Convergent Sequence Features of Antiviral B Cells

Cited by 2 publications

References 58 publications

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq

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