Functional clustering of B cell receptors using sequence and structural features

Xu, Zichang; Li, Songling; Rozewicki, John; Yamashita, Kazuo; Teraguchi, Shunsuke; Inoue, Takeshi; Shinnakasu, Ryo; Leach, Sarah; Kurosaki, Tomohiro; Standley, Daron M.

doi:10.1039/c9me00021f

Cited by 13 publications

(14 citation statements)

References 36 publications

(35 reference statements)

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“…After superimposing all common epitopes for all pairs of antibodies, the C‐alpha root‐mean‐square deviation (RMSD) was used to cluster the antibodies by single‐linkage hierarchical clustering with a 10.0 Å cutoff. We previously used average‐linkage hierarchical clustering to classify antibodies that target overlapping epitopes based on structural alignments [27] . Here, the antigens in the anti‐SARS‐Cov‐2 RBD antibody dataset were aligned well using sequence information, and appeared to form discrete clusters.…”

Section: Methodsmentioning

confidence: 99%

Improved Antibody‐Specific Epitope Prediction Using AlphaFold and AbAdapt**

Davila

Wiamowski

et al. 2022

ChemBioChem

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Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we systematically assessed the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline. By incorporating more accurate antibody models, we observed improvement in docking, paratope prediction, and prediction of antibody-specific epitopes. We further applied AbAdapt-AF in an anti-receptor binding domain (RBD) antibody complex benchmark and found AbAdapt-AF outperformed three alternative docking methods. Also, AbAdapt-AF demonstrated higher epitope prediction accuracy than other tested epitope prediction tools in the anti-RBD antibody complex benchmark. We anticipate that AbAdapt-AF will facilitate prediction of antigen-antibody interactions in a wide range of applications.

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Section: Methodsmentioning

confidence: 99%

Improved Antibody‐Specific Epitope Prediction Using AlphaFold and AbAdapt**

Davila

Wiamowski

et al. 2022

ChemBioChem

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“…Xu et al 161 used a structure-based clustering of CDRH3 sequences to cluster supposedly phenotypically similar sequences and based on this created a group of sequences that bind the same epitope on human immunodeficiency virus (HIV) or influenza virus. They predicted whether sequences bind the same epitope with SVM.…”

Section: Learnability Of Antibody–antigen Bindingmentioning

confidence: 99%

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Akbar

Bashour

Rawat

et al. 2022

mAbs

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Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

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“…Further, CD27-HLA-DR+ T cells were enriched within inflamed RA joints, rapidly produced IFN-γ and cytolytic factors, and contracted with successful treatment of RA ( 409 ). Furthermore, the repertoires of B cell receptors, which may be collected using single cell-resolution sequencing technology, carry a personal history of antigen exposure for a donor ( 410 ). Single-cell sequencing of plasmablasts derived from RA patients revealed the presence of B cell receptors specific for CCP and other RA-associated autoantigens ( 411 ).…”

Section: Single-cell Analysis In Precision Medicine Of Ramentioning

confidence: 99%

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

et al. 2021

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Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a “trial-and-error” approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.

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Functional clustering of B cell receptors using sequence and structural features

Abstract: We describe a method for clustering BCRs based on sequence and predicted structural features in order to identify groups with similar antigen and epitope binding specificity.

Cited by 13 publications

References 36 publications

Improved Antibody‐Specific Epitope Prediction Using AlphaFold and AbAdapt**

Improved Antibody‐Specific Epitope Prediction Using AlphaFold and AbAdapt**

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

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