Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Smith, Matthew D.; Case, Marshall; Makowski, Emily K.; Tessier, Peter M.

doi:10.1093/bioinformatics/btad446

Cited by 5 publications

(3 citation statements)

References 64 publications

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“…This is supported by a body of evidence from other protein families 51,52 showing the sparseness of functional protein landscapes 53 . We also found that these sequence-binding fitness landscapes were most consistent with one-body or at most twobody interactions, consistent with recent protein engineering literature [54][55][56] . The resulting implication is that sampling of a small percentage of potential variants is sufficient for reconstruction of fitness landscapes.…”

Section: Discussionsupporting

confidence: 89%

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Petersen,

Kirby,

Chrispens

et al. 2024

Preprint

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Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

show abstract

Section: Discussionsupporting

confidence: 89%

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Petersen,

Kirby,

Chrispens

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This is supported by a body of evidence from other protein families 51,52 showing the sparseness of functional protein landscapes 53 . We also found that these sequence-binding fitness landscapes were most consistent with one-body or at most two-body interactions, consistent with recent protein engineering literature [54][55][56] . The resulting implication is that sampling of a small percentage of potential variants is sufficient for reconstruction of fitness landscapes.…”

Section: Discussionsupporting

confidence: 89%

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Petersen,

Kirby,

Chrispens

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

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“…Mutational maps of antibodies can be created as position specific scoring matrices (PSSM) [18], or creating multiple sequence alignments (MSA) of millions of NGS sequences sharing a single germline origin [19,20]. Other studies have demonstrated the feasibility of gene agnostic language models of the human antibody space [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

nanoBERT: A deep learning model for gene agnostic navigation of the nanobody mutational space

Hadsund,

Satława,

Janusz

et al. 2024

Preprint

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Nanobodies are a subclass of immunoglobulins, whose binding site consists of only one peptide chain, bestowing favorable biophysical properties. Recently, the first nanobody therapy was approved, paving the way for further clinical applications of this antibody format. Further development of nanobody-based therapeutics could be streamlined by computational methods. One of such methods is infilling - positional prediction of biologically feasible mutations in nanobodies. Being able to identify possible positional substitutions based on sequence context, facilitates functional design of such molecules. Here we present nanoBERT, a nanobody-specific transformer to predict amino acids in a given position in a query sequence. We demonstrate the need to develop such machine-learning based protocol as opposed to gene-specific positional statistics since appropriate genetic reference is not available. We benchmark nanoBERT with respect to human-based language models and ESM-2, demonstrating the benefit for domain-specific language models. We also demonstrate the benefit of employing nanobody-specific predictions for fine-tuning on experimentally measured thermostability dataset. We hope that nanoBERT will help engineers in a range of predictive tasks for designing therapeutic nanobodies.

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Position-Specific Enrichment Ratio Matrix scores predict antibody variant properties from deep sequencing data

Cited by 5 publications

References 64 publications

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

nanoBERT: A deep learning model for gene agnostic navigation of the nanobody mutational space

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