Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery

Jaszczyszyn, Igor; Bielska, Weronika; Gawlowski, Tomasz; Dudzic, Pawel; Satława, Tadeusz; Kończak, Jarosław; Wilman, Wiktoria; Janusz, Bartosz; Wróbel, Sonia; Chomicz, Dawid; Galson, Jacob D.; Leem, Jinwoo; Kelm, Sebastian; Krawczyk, Konrad

doi:10.3389/fmolb.2023.1214424

Cited by 9 publications

(10 citation statements)

References 58 publications

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“…Given the relatively small magnitude of CDRH3 fluctuations and the current limitations of structure prediction methods, it is challenging to detect and accurately represent these small conformational changes in rigid antibody models. The accuracy of current structure prediction tools typically is below the range of these small fluctuations ( 114 ). However, when it comes to studies involving antibody variants, we cannot overlook these fluctuations, as they reflect the effects of mutations.…”

Section: Discussionmentioning

confidence: 99%

“…In the future, generating multiple models with various refinements could yield a conformational ensemble similar to what is obtained from a full-fledged long MD simulation (114,118). Training on dynamic data, such as MD simulation-based conformations, enables the model to capture loop flexibility and variability, resulting in more robust and realistic predictions that can improve currently challenging CDRH3 structure prediction.…”

Section: Challenges In the Computation Of Structure-based Developabil...mentioning

confidence: 99%

“…However, it remains challenging to predict the antibody CDRH3 loop regions, which are of significant interest due to their involvement in antibody-antigen binding (94) as well as developability (20). These loop regions are generally more flexible and less structurally conserved compared to stable secondary structure elements like beta sheets (114). Consequently, predicting the specific conformations of these loops accurately can be difficult, and it becomes even more challenging to determine when a prediction is correct without combining experimental validation and MD simulations (34,114,118).…”

Section: Challenges In the Computation Of Structure-based Developabil...mentioning

confidence: 99%

“…These loop regions are generally more flexible and less structurally conserved compared to stable secondary structure elements like beta sheets (114). Consequently, predicting the specific conformations of these loops accurately can be difficult, and it becomes even more challenging to determine when a prediction is correct without combining experimental validation and MD simulations (34,114,118).…”

Section: Challenges In the Computation Of Structure-based Developabil...mentioning

confidence: 99%

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Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Bashour,

Smorodina,

Pariset

et al. 2023

Preprint

Self Cite

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Designing effective monoclonal antibody (mAb) therapeutics face a significant challenge known as “developability”, which reflects an antibody’s ability to progress through development stages based on its physicochemical properties. While natural antibody repertoires may provide valuable guidance for antibody selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These knowledge gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence- and 46 structure-based DPs of over two million native and human-engineered single-chain antibody sequences. We found lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DPs, and structure DP values varied across the conformational ensemble of antibody structures. Sequence DPs were more predictable than structure-based ones across different machine learning (ML) tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies were localized within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work charts a roadmap to evaluate the plasticity of antibody developability, providing a more rational perspective for therapeutic mAb design.

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Section: Discussionmentioning

confidence: 99%

Section: Challenges In the Computation Of Structure-based Developabil...mentioning

confidence: 99%

Section: Challenges In the Computation Of Structure-based Developabil...mentioning

confidence: 99%

Section: Challenges In the Computation Of Structure-based Developabil...mentioning

confidence: 99%

See 2 more Smart Citations

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Bashour,

Smorodina,

Pariset

et al. 2023

Preprint

Self Cite

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show abstract

“…Significant progress has been made to address this problem via deep learning: some of the new and exciting tools are GearBind (7), PALM and A2binder (8), and DSMBind (9). We point the reader to this review for an excellent overview of some of the tools that have existed for some time, along with a comparison of these tools (10).…”

Section: Introductionmentioning

confidence: 99%

PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

DeRoo,

Terry,

Zhao

et al. 2024

Preprint

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Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor- and time-intensive and costly. To take advantage of the recent advances in protein structure prediction algorithms available to the scientific community, we developed a calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens. We found that this AlphaFold2 pipeline, which we call PAbFold, was able to accurately flag known epitope sequences for several well-known antibody targets (HA / Myc) when the target sequence was broken into small overlapping linear peptides and antibody complementarity determining regions (CDRs) were grafted onto several different antibody framework regions in the single-chain antibody fragment (scFv) format. To determine if this pipeline was able to identify the epitope of a novel antibody with no structural information publicly available, we determined the epitope of a novel anti-SARS-CoV-2 nucleocapsid targeted antibody using our method and then experimentally validated our computational results using peptide competition ELISA assays. These results indicate that the AlphaFold2-based PAbFold pipeline we developed is capable of accurately identifying linear antibody epitopes in a short time using just antibody and target protein sequences. This emergent capability of the method is sensitive to methodological details such as peptide length, AlphaFold2 neural network versions, and multiple-sequence alignment database. PAbFold is available at https://github.com/jbderoo/PAbFold.

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Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Bashour,

Smorodina,

Pariset

et al. 2024

Commun Biol

View full text Add to dashboard Cite

Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence-and 46 structure-based DPs of over two million native and humanengineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.Monoclonal antibodies (mAbs) are widely used therapeutics against cancer, autoimmune, and infectious diseases [1][2][3][4][5] . The global mAb market is forecasted to grow to >$ 300 billion in 2025 6 . Despite their commercial success, mAb discovery remains a resource-and time-consuming process resulting in a costly and lengthy clinical approval, hindering accessibility and affordability 7,8 . A successful mAb molecule should not only show sufficient affinity in its target binding profile but also exhibit a desirable "developability" profile 9 . The term "developability" refers to a combination of intrinsic physicochemical parameters defined as developability parameters (DPs) that relate to biophysical aspects of antibodies and their formulations -including aggregation, solubility, and stability [10][11][12][13][14] . The feasibility of an antibody candidate to successfully progress from discovery to development

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Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery

Cited by 9 publications

References 58 publications

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

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