Computational Antibody Papers

Filter by tags
annotation/numbering
Filter by published year
2024
TitleKey points

  • 2024-08-28

    For antibody sequence generative modeling, mixture models may be all you need

    • annotation/numbering
    • Authors introduce AntPack - software for rapid numbering of antibody sequences, germline identification and humanization.
    • Authors use a mixture model (so not ML!) on millions of sequences from NGS.
    • The sequences are pre-numbered to standardize them and then assigned to clusters which offer explainability on germline assignment and residue probability at a given position.
    • The method is very fast in comparison to HMM-based approaches such as ANARCI.
    • Method is available via https://github.com/Wang-lab-UCSD/AntPack

  • 2024-07-30

    Digger: directed annotation of immunoglobulin and T cell receptor V, D, and J gene sequences and assemblies

    • annotation/numbering
    • Novel tool to automatically annotate immunoglobulin (and t-cell) genes from assemblies.
    • They compared the human manual annotations from IMGT to what they identify and they were in close agreement for functional and open reading frame genes.
    • By automating annotation, strict rules can be enforced avoiding manual curation errors.
    • Software is available at: https://github.com/williamdlees/Digger

  • 2024-07-09

    The immunogenicity of human-origin therapeutic antibodies are associated with V gene usage

    • developability
    • annotation/numbering
    • They demonstrate that germline usage and distance from the germline are correlated with ADA.
    • They use OAS as a reference for natural NGS sequences, but they exclude studies that focus on single isotopes such as IgM, IgG.
    • They collected ADA points from therapeutics from IMGT and related these to FDA labels in the first instance and heterogenous search second.
    • If they had multiple values for ADA, they then take the maximum value.
    • There are 14 amino acid differences between the IGHV4-34*10 and IGHV4-34*11 alleles, a 14.4% sequence difference.
    • Healthy donors have similar gene usages as diseased ones.
    • They show that V gene usage has correlation with ADA just as the number of mutations.

  • 2024-07-02

    Insights into next generation sequencing guided antibody selection strategies

    • ngs
    • annotation/numbering
    • Tour de force of impact of NGS sequencing depth and clustering on picking hits from display campaigns. Repository of information for a detailed walk-through of a display campaign.
    • Altogether NGS is big help with respect to random colony picking - better binders can be produced and larger epitope diversity.
    • Campaigns against three (related) antigens, sars-cov-2 trimer protein, monomer s1 and RBD.
    • They define a set of research questions on the relation of NGS statistics and kinetics - e.g. is higher frequency in NGS correlating with higher affinity?
    • Sequences for VH and VL of 200 unique antibodies were synthesized, cloned into expression vectors for mammalian IgG, and subsequently expressed and purified as complete IgG molecules. Out of the 200 antibodies tested, 169 (84.5%) exhibited affinities <1 µM for RBD, S1, or the trimer. The selection of these 200 distinct antibody sequences was based on 57 well-defined clusters, which were identified at the convergence of three target populations (41 clusters), exclusive to either the S1 (1 cluster) or RBD (1 cluster) populations, or originating from 14 clusters derived from the trimer NGS population. The selection criteria considered the most abundant representative per cluster, regardless of whether they intersected with S1 or RBD.
    • Clustering methods used were 100% identity, clonotyping and their own unsupervised clustering (abscan!).
    • Abscan is based on in-house usupervised method
    • The Abscan clustering method typically results in higher diversity, relative to traditional clonotyping.
    • The abundance of the top representative in each AbScan cluster gives best correlation to binding affinity.
    • One of chief advantages of clustering is identifying sequences within cluster of interest with lower number of liabilities.
    • They trained an XGBoost method on NGS statistics etc. to discriminate binders and non-binders (though the dataset is very small)
    • Same cluster = same epitope
    • They employ abundance threshold of 0.005% using concatenated CDRs as a basic way to discriminate binders and non-binders.
    • Abscan can be described (in high level) as follows: They utilize an unsupervised machine learning approach to cluster specific regions of interest, such as HCDR3. This clustering process is based on various sequence-related properties, NGS statistics (including relative abundance and round-to-round enrichment) pertaining to different regions of interest (HCDR3, HCDR3 + LCDR3, concatenated CDRs), and employs diverse algorithms (such as the Elbow method, Ordering Points to Identify the Clustering Structure (OPTICS), physicochemical reduction of the amino acid space, traditional clonotyping, and Levenshtein distance (LD)).