Computational Antibody Papers
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2024-10-08
Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen
- developability
- generative methods
- Authors demonstrate that using scores from DeepAb one can sort mutations in an antibody that improve affinity and a series of other properties.
- The authors used the DeepAb structure prediction mode model to rank mutations based on their impact on structure prediction confidence, leading to the design of 200 novel anti-hen egg lysozyme (HEL) antibody variants.
- Single-point mutations from a deep mutational scanning (DMS) dataset (Warszawski et al.) were combined into multi-mutation variants (up to 7 mutations), and these variants were selected based on DeepAb scores for experimental testing.
- The designed variants were expressed and tested for thermostability, colloidal stability, and binding affinity to HEL.
- Large percentage of the variants showed improved thermostability (91%) and affinity (94%), with 10% showing significant increases in binding affinity.
- A subset of 27 high-performing variants was further tested for developability characteristics, including nonspecific binding, aggregation propensity, and self-association, ensuring their practical usability.
2024-09-04
p-IgGen: A Paired Antibody Generative Language Model
- generative methods
- language models
- developability
- Novel generative model for antibody sequences that supports Vh/Vl pairing and generation of developable sequences.
- Three models were created, IgGen (unpaired model), p-IgGen (unpaired fine-tuned on pairs) and developable p-IgGen (paired fine-tuned on developable sequences).
- They used ca. 250m unpaired sequences and 1.8m paired sequences for training.
- The model is based on GPT-2 but with rotary position embedding.
- Developable sequences were defined as structural models of the 1.8m that had good TAP metrics (900,000 in total).
- The model is much smaller than many of the models out there, (17m params), so it is more lightweight in training and application.
- The model performs better on immunogenicity prediction than other models but worse on expression prediction.
2024-09-04
Antibody Humanization via Protein Language Model and Neighbor Retrieval
- language models
- developability
- Novel humanization protocol employing language models and large-scale repertoire data.
- Human OAS and germline sequences are embedded using ESM2.
- K-nearest neighbors algorithm is then used to introduce mutations into the ESM-2 embedded query sequence coming from closest functional neighbors in the ESM2-embedded OAS+germlinse space.
- Results of humanized abs are validated experimentally via ELISA.
2024-08-28
AntiBARTy Diffusion for Property Guided Antibody Design
- language models
- generative methods
- developability
- Novel language model AntiBARTy with demonstration of how to use it to diffuse novel antibodies with favorable solubility properties.
- The core model is a BART-based transformer, with 16m parameters.
- It was firstly trained on all human heavy and light chains from OAS (254m heavies and 342m lights <- yes, more lights). This was followed by fine tuning on the higher quality paired data from OAS.
- The diffusion model was based on U-net (CNN used for segmentation of medical images), totaling 3m parameters.
- They define low and high solubility classes as predicted by protein-sol on paired OAS, with roughly 20k samples for each class.
- Overall, one can sample from multivariate to get a vector in Antibarty latent space and use it to get an antibody sequence that is either high or low protein-sol predicted solubility.
- Authors employ patent data to develop a model (selfPAD) of humanness that achieves state of the art in immunigenicity prediction.
- They employ data from PAD which at the time were roughly 290k sequences from 16,000 patent families.
- They recognize the noisiness inherent to the patent data and employ a training procedure to train a latent representation of patent sequences that is associated with function - in this case the target of the sequence.
- In the first stage of training they employ contrastive learning, with sequence for the same target trained to be ‘closer’ in latent space and those against different targets to be ‘farther away’.
- In the second stage, they perform fine tuning on humanness detection.
- They tested their method on humanness prediction, ADA prediction and agreement with humanization choices. Taking all the tests together their method achieves the best performance.
- They introduce six molecular descriptors (MolDesk) that perform better than the therapeutic antibody profiler (TAP) and MOE.
- The method used for calculating hydrophobicity and the specific scale employed can have a notable influence on the evaluation of antibody developability characteristics, as highlighted by Waibl et al. in 2022.
- They use APBS (Adaptive Poisson-Boltzmann Solver) to calculate electrostatic potential of the molecules.
- They calculate the values generated by a conformational ensemble using MD.
- Their calculation of hydrophobicity showed sensitivity to the hydrophobicity definition.
- Their measure of CDR negative charge outperforms other methods on viscosity experimental dataset.
- They used a dataset of 64 antibodies with determined clearance and showed that hydrophobicity base descriptors do not discriminate between fast clearance and slow clearance whereas CDR_APBS_pos does.
- They tested the effect of the model on the calculation of properties - they achieve broadly similar results.
- After molecular dynamic simulations of the models, the results between the models are blurred, showing the importance of conformational sampling.
- Their final set of metrics: “These descriptors include CDR_APBS_neg, a significant factor influencing viscosity and colloidal stability (Figure 3), CDR_APBS_pos, a major driver of PK clearance and polyspecificity (Figure 4), CDR_HPATCH_WW, which plays a key role in SEC, HMW, and HIC (Figure 5), CDR_HPATCH_BM which correlated the best with HIC, as well as total CDR length and APBS charge asymmetry (APBS_CAP) as two additional descriptors to align with the type of descriptors considered in the TAP metrics”
- They compared TAP and MolDesk on the basis of whether molecules progressed or regressed in the tests. MolDesk performed better on showing less critical flags for the approved and progressed molecules.
2024-07-16
Human-likeness of antibody biologics determined by back-translation and comparison with large antibody variable gene repertoires
- developability
- numbering/annotation
- They create a tool to do back translation and provide single nucleotide frequencies (SNFs) for antibody variable regions from large-scale NGS study.
- They use the Soto et al. dataset, which was ~326m antibody sequences.
- They create position and gene specific scoring matrix (pgssm) using the gene and CDR-H3 length matches.
- They can back-translate amino acid sequences to obtain plausible codons.
- Their PGSSMVJ score is the average of single nucleotide frequencies.
- Their naturalness score distinguishes between human and non-human sequences.
- They benchmarked the back-translation on 181k sequences from GenBank.
2024-07-16
Computational optimization of antibody humanness and stability by systematic energy-based ranking
- developability
- They employed Rosetta to show that energy-based ranking of constructs is superior to homology based approaches.
- In certain instances, the most successful humanized design achieved through experimentation originates from human framework genes that lack significant similarity to the corresponding animal antibodies. This observation underscores the potential efficacy of an energy-based humanization approach over the conventional homology-based humanization method.
- They make all the V-J combinations that are free of liabilities such as n-glycosylation and extra cysteines, making a set of frameworks.
- Mouse CDRs are grafted onto the artificial frameworks, modeled and energy minimized using Rosetta.
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.
2024-06-25
Identifying developability risks for clinical progression of antibodies using high-throughput in vitro and in silico approaches
- developability
- They benchmarked a range of experimental and computational measures to see which ones correlated with therapeutics moving it through the clinical trials.
- Table 2 - cheat sheet of experimental methods and what they do.
- Some of the experimental results are highly correlated with one another (The retention time on the FcRn column was highly correlated with the affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS), polyspecificity reagent (PSR), clone self-interaction using bio-layer interferometry (CSI) and cross-interaction chromatography (CIC) assays, which constituted one of the polyspecificity clusters in our prior work)
- For each experimental assay they update the 90% intervals wrt to their previous experimental recommendations.
- Table 4 shows the descriptors that need to be calculated for developability assessments.
- Some of the in silico metrics also correlate with one another, forming conceptual groups (e.g. charge calculations)
- They notice that there is a slight trend for mabs that progress in trials to have less violations of their experimental descriptors than those that were regressed.