BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning

Prihoda, David; Maamary, Jad; Waight, Andrew B.; Juan, Veronica; Fayadat‐Dilman, Laurence; Svozil, Daniel; Bitton, Danny A.

doi:10.1080/19420862.2021.2020203

Cited by 101 publications

(125 citation statements)

References 63 publications

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“…AntiBERTa is based on the RoBERTa architecture, 39 as it allows a more direct comparison to established BERT-based protein language models, such as ProtBERT and Sapiens. 29 , 36 …”

Section: Resultsmentioning

confidence: 99%

“…AntiBERTa is pre-trained using a self-supervised MLM task, like other transformer-based protein LMs. 26 , 29 , 36 Briefly, 15% amino acids within the input BCR sequence are randomly perturbed, and the model determines the correct amino acid in place of these masked positions. This task encourages the model to develop a contextual understanding of the BCR sequence.…”

Section: Resultsmentioning

confidence: 99%

“…To date, two LMs have been developed for BCRs and antibodies: DeepAb 13 and Sapiens. 36 DeepAb is a bidirectional long short-term memory (LSTM) network that is pre-trained on 100k paired BCR sequences from the Observed Antibody Space. 37 , 38 As sequence embeddings from DeepAb naturally separate into distinct structural clusters, they can help to produce structural predictions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deciphering the language of antibodies using self-supervised learning

Leem¹,

Mitchell²,

Farmery³

et al. 2022

Patterns

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“…AntiBERTa is based on the RoBERTa architecture, 39 as it allows a more direct comparison to established BERT-based protein language models, such as ProtBERT and Sapiens. 29 , 36 …”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deciphering the language of antibodies using self-supervised learning

Leem¹,

Mitchell²,

Farmery³

et al. 2022

Patterns

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“…Examples of categorical predictions include predicting the source of an antibody (e.g. murine or human) from its amino acid sequence [ 24 ] or the incidence of amino acids at specific positions in protein sequence [ 25 ]. On the other hand, continuous predictions aim to capture values such as aggregation propensity [ 26 ] or orientation angles of residues in a structure [ 27 ].…”

Section: Encoding Antibody Antigen Sequence and Structure For Machine...mentioning

confidence: 99%

“…Another computer method that intends to accelerate the process of humanization is BioPhi [ 24 ], an antibody design interface with automated methods that capture the diversity of natural human antibody repertoires. By combining adaptive immune repertoire sequencing and antibody engineering, BioPhi integrates two data-driven methods—novel humanization (Sapiens) and humanness evaluation methods (OASis).…”

Section: Developability—deimmunization Using Large-scale Ngs Data And...mentioning

confidence: 99%

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

Wilman¹,

Wróbel²,

Bielska³

et al. 2022

Briefings in Bioinformatics

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Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.

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On the humanization of VHHs: Prospective case studies, experimental and computational characterization of structural determinants for functionality

Fernández‐Quintero,

Guarnera,

Musil

et al. 2024

Protein Science

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The humanization of camelid‐derived variable domain heavy chain antibodies (VHHs) poses challenges including immunogenicity, stability, and potential reduction of affinity. Critical to this process are complementarity‐determining regions (CDRs), Vernier and Hallmark residues, shaping the three‐dimensional fold and influencing VHH structure and function. Additionally, the presence of non‐canonical disulfide bonds further contributes to conformational stability and antigen binding. In this study, we systematically humanized two camelid‐derived VHHs targeting the natural cytotoxicity receptor NKp30. Key structural positions in Vernier and Hallmark regions were exchanged with residues from the most similar human germline sequences. The resulting variants were characterized for binding affinities, yield, and purity. Structural binding modes were elucidated through crystal structure determination and AlphaFold2 predictions, providing insights into differences in binding affinity. Comparative structural and molecular dynamics characterizations of selected variants were performed to rationalize their functional properties and elucidate the role of specific sequence motifs in antigen binding. Furthermore, systematic analyses of next‐generation sequencing (NGS) and Protein Data Bank (PDB) data was conducted, shedding light on the functional significance of Hallmark motifs and non‐canonical disulfide bonds in VHHs in general. Overall, this study provides valuable insights into the structural determinants governing the functional properties of VHHs, offering a roadmap for their rational design, humanization, and optimization for therapeutic applications.

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BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning

Cited by 101 publications

References 63 publications

Deciphering the language of antibodies using self-supervised learning

Deciphering the language of antibodies using self-supervised learning

Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery

On the humanization of VHHs: Prospective case studies, experimental and computational characterization of structural determinants for functionality

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