Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling

mAbs, Год журнала: 2024, Номер 16(1)

Опубликована: Июнь 10, 2024

In silico assessment of antibody developability during early lead candidate selection and optimization is paramount importance, offering a rapid material-free screening approach. However, the predictive power reproducibility such methods depend heavily on molecular descriptors, model parameters, accuracy predicted structure models, conformational sampling techniques. Here, we present set surface descriptors specifically designed for predicting developability. We assess performance these by benchmarking their correlations with an extensive array experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, polyspecificity. Further, investigate sensitivity to methodological nuances, as choice interior dielectric constant, hydrophobicity scales, prediction methods, impact sampling. Notably, observe systematic shifts in distribution depending method used, driving weak across models. Averaging descriptor values over distributions from dynamics mitigates improves consistency different albeit inconsistent improvements data. Based our analysis, propose six risk flags effectiveness potential issues case study molecules.

Язык: Английский

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

Communications Biology, Год журнала: 2024, Номер 7(1)

Опубликована: Июль 31, 2024

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 comprehensive understanding of developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes human-engineered relate one another. These gaps hinder fundamental profile cartography. To chart engineered landscapes, computed 40 sequence- 46 structure-based DPs over two million native single-chain sequences. We find lower redundancy among compared sequence-based DPs. Sequence sensitivity single amino acid substitutions varied by region DP, structure values across conformational ensemble structures. show that sequence are more predictable than ones different machine-learning tasks embeddings, indicating constrained design space. Human-engineered localize within antibodies, suggesting explore mere subspaces one. Our work quantifies developability, providing resource therapeutic design. Analysis 2 reveals form This large-scale analysis allows quantification plasticity, accelerating drug

Язык: Английский

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Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications

Frontiers in Molecular Biosciences, Год журнала: 2024, Номер 11

Опубликована: Март 28, 2024

Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes diverse sequences coming from phage display or animal immunizations. Identification suitable therapeutic candidates is achieved grouping the their similarity and subsequent selection a set antibodies for further tests. Such groupings typically created using sequence-similarity measures alone. Maximizing diversity in selected crucial to reducing number tests molecules with near-identical properties. With advances structural modeling machine learning, can now be grouped across other dimensions, such predicted paratopes three-dimensional structures. Here we benchmarked antibody methods clonotype, sequence, paratope prediction, structure embedding information. results were two tasks: binder detection epitope mapping. We demonstrate no method appears outperform others, while mapping, paratope, clusterings top performers. Most importantly, all propose orthogonal groupings, offering more pools when multiple than any single To facilitate exploring different methods, an online tool-CLAP-available at ( clap.naturalantibody.com ) allows users group, contrast, visualize methods.

Язык: Английский

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PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Опубликована: Янв. 23, 2025

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

Язык: Английский

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Large-scale data mining of four billion human antibody variable regions reveals convergence between therapeutic and natural antibodies that constrains search space for biologics drug discovery

mAbs, Год журнала: 2024, Номер 16(1)

Опубликована: Июнь 6, 2024

The naïve human antibody repertoire has theoretical access to an estimated > 10

Язык: Английский

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Molecular Surface Descriptors to Predict Antibody Developability

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июль 19, 2023

Abstract Understanding the molecular surface properties of monoclonal antibodies (mAbs) is crucial for determining their function, affinity, and developability. Yet, robust methods to accurately represent key structural biophysical features mAbs on are still limited. Here, we introduce MolDesk, a set descriptors specifically designed predicting antibody developability characteristics. We assess performance these by directly benchmarking correlations with an extensive array in vitro vivo data, including viscosity at high concentration, aggregation, hydrophobic interaction chromatography (HIC), human pharmacokinetic (PK) clearance, Heparin retention time, polyspecificity. Additionally, investigate sensitivity methodological nuances, such as choice interior dielectric constant electrostatic potential calculations, residue-level hydrophobicity scales, initial structure models, impact conformational sampling. Based our analysis, propose six silico rules that leverage demonstrate superior ability predict clinical progression therapeutic compared established models like TAP. 1

Язык: Английский

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

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Окт. 30, 2023

Abstract 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 comprehensive understanding of developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes human-engineered relate one another. These gaps hinder fundamental profile cartography. To chart engineered landscapes, computed 40 sequence- 46 structure-based DPs over two million native single-chain sequences. We found lower redundancy among compared sequence-based DPs. Sequence sensitivity single amino acid substitutions varied by region DP, structure values across conformational ensemble structures. were more predictable than ones different machine-learning tasks embeddings, indicating constrained design space. Human-engineered localized within sequence antibodies, suggesting that explore mere subspaces one. Our work quantifies developability, providing resource therapeutic design.

Язык: Английский

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Reviewer #2 (Public Review): PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Опубликована: Июнь 11, 2024

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

Язык: Английский

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eLife assessment: PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Опубликована: Июнь 11, 2024

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

Язык: Английский

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Reviewer #1 (Public Review): PAbFold: Linear Antibody Epitope Prediction using AlphaFold2

Опубликована: Июнь 11, 2024

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

Язык: Английский

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