Computational Antibody Papers
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2024
TitleKey points
2024-11-14
On the humanization of VHHs: Prospective case studies,experimental and computational characterization of structural determinants for functionality
- developability
- databases
- Authors perform humanization of VHHs and generate experimental data confirming their designs.
- The protocol involves grafting CDRs1-3 and then systematically modifying Hallmars/Verniers and others to make them more human.
- Positions 49 and 50 (e.g., E49G, H50L in VHH1): These were generally well-tolerated, allowing for humanization without major impact on binding affinity or stability.
- Position 52 (e.g., S52W in VHH2): In some cases, changing this residue even improved affinity.
- Position 42: Humanizing residue F42 to a more human-like amino acid (e.g., F42V) in VHH2 led to a significant reduction in binding affinity. This residue plays a key role in stabilizing the CDR3 loop through interactions with other regions, making it essential for maintaining the bioactive conformation.
- Position 52 (in some contexts): In VHH1, the mutation G52W led to a loss of binding due to steric clashes, demonstrating that this position can be critical depending on the structural context.
- Measured binding affinities, expression yields, and purities of humanized variants. Crystal structures confirmed effects of humanization on binding; non-canonical disulfides stabilize CDR3
2024-11-14
An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody
- developability
- databases
- Novel humanization method, employing diffusion.
- The model first learns the diffusion of a human sequence (with CDRs intact). The framework residues are diffused back. The network is then fine-tuned on mouse sequences.
- There are two flavors of the model - one nanobody, another antibody.
- They curate a great dataset from patents with over 300 sequences of paired humanized/native seuqences.
- They demonstrate in silico and in vitro that their designs make sense.
- Authors expand the existing IMGT-mab-db with knowledge graph querying via user-friendly interface.
- As of February 2024, IMGT/mAb-KG contains 139,629 triplets, 1,867 concepts, 114 properties, and links 21,842 entities. It includes detailed information on approximately 1,500 mAbs, 500 targets, and 500 clinical indications.
- It is linked to various external resources, such as Thera-SAbDab, PharmGKB, PubMed, and HGNC, making it a valuable tool for researchers and developers working on therapeutic mAbs
- It is accessible via https://www.imgt.org/mAb-KG/
2024-07-23
Baselining the Buzz Trastuzumab-HER2 Affinity, and Beyond
- databases
- protein design
- generative methods
- binding prediction
- Novel dataset of 0.5m anti-trastuzumab antibodies together with benchmarking of affinity classification methods.
- They generated a dataset of ~500,000 anti-Her2 trastuzumab variants by modifying the CDR-H3. The binding affinity is divided into high/medium/low with reasonably even split (178,160, 196,392, and 171,732 respectively).
- They split their dataset into positives and negatives by putting medium and low binders into the negative set.
- They contrast their dataset with this from Mason et al. (~39k variants vs theirs 0.5m), to show that in a small (relatively) number of cases, binders in one set can be labeled as negative in another.
- They test that the predictor developed by Mason et al. for binding/non binding classification works as intended on the novel 0.5m dataset. Likewise their model trained on Mason data and tested on their ds (and vice versa) has predictive power. It does but to a much lesser extent than training on data from the same experiment.
- As methods to benchmark they used FLAML https://arxiv.org/abs/1911.04706, CNN and EGNN.
- CNN and FLAML are top performers, but CNN performs well on the small data (signal starting with ~170 sequences).
- Performance drops radically when train/val splits are done with respect to the clonotype.
- THey tested AbLang, ProteinMPNN, ESM and Blosum on their ability to generate binding trastuzumab variants. As control they also randomly generated sequences. They observed the following percentages of sequences exhibiting CNN-HER2-max binding probabilities greater than 90%: 13% for Random, 26% for BLOSUM, 27% for AbLang (when masking all ten residues simultaneously), 29% for AbLang (when masking one residue at a time), 19% for ProteinMPNN, and 30% for ESM (when masking one residue at a time), respectively.