Computational Antibody Papers
Filter by tags
developability
Filter by published year
All
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
Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations
- developability
- binding prediction
- Pilot single-shot computational antibody design, where known binders were taken and new ones computationally generated on their basis, maintaining binding with a good developability profile.
- The pipeline starts with known binders to the SARS-CoV-2 RBD
- Novel binders were generated using a combination of computational approaches, including: Observed Antibody Space (OAS): Paired and unpaired sequences from the OAS dataset to identify antibody candidates within a certain edit distance from the starting antibodies. Inverse Folding Model (AbMPNN):generated new antibody sequences maintaining structural features compatible with binding to the SARS-CoV-2 RBD. ESM: guided the mutation of sequences to retain or improve binding affinity while enhancing developability.
- The developability properties were assessed using Rosetta scoring to evaluate antibody stability and interface energetics, alongside TAP.
- Experimental methods for screening included size-exclusion chromatography (SEC) to assess aggregation propensity and differential scanning fluorimetry (DSF) for thermal stability. Antibodies that passed these criteria were deemed suitable for development.
- Success rate of the method: The pipeline demonstrated a success rate of 54% for generating binding antibodies that retained affinity against escape mutations on the SARS-CoV-2 RBD.
- Authors tested RFDiffusion for the design task but with poor success rate - albeit not the antibody-fine tuned version it appears, that should work better.
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.
2024-11-14
Transfer learning to leverage larger datasets for improved prediction of protein stability changes
- non-antibody stuff
- developability
- Novel method to predict stability of proteins, based on ProteinMPNN.
- The method employs ProteinMPNN embeddings with a stability prediction module to gauge the effect of single point mutations on protein stability.
- The Stability prediction module is composed of the light attention module (that figures out which parts of the embeddings should be upvoted) followed by shallow multi layer perceptron.
- For training/evaluation they employed Megascale and Fireprot datasets with measured protein stability data. Though after much pre-processing because the original datasets either contained many unreliable data points, or there was a risk that the mutations would change the structure too much.
- Ablations show that all the elements of the network are important and bring something to the prediction, with ProteinMPNN having quite some predictive power out-of-the box.
2024-10-08
Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains
- developability
- Novel humanization software, allowing for rapid re-design of both heavy and light chains.
- Unlike other tools such as Hu-mAb and Sapiens, which humanize heavy and light chains separately, Humatch jointly humanizes both chains, improving stability and reducing the risk of immunogenic epitopes between chains.
- Humatch consists of three lightweight Convolutional Neural Networks (CNNs). Each CNN is trained for a specific task: one for heavy chains (CNN-H), one for light chains (CNN-L), and one for assessing natural heavy/light chain pairing (CNN-P). The CNNs are designed to output multiclass predictions for identifying human V-genes and classifying chain pairings.
- The CNNs were trained on data from the Observed Antibody Space (OAS), which includes millions of human and non-human antibody sequences.
- Humatch's performance was measured through precision-recall and ROC-AUC metrics, achieving near-perfect accuracy in classifying human and non-human sequences. Performance was also tested by humanizing 25 precursor antibodies and comparing the mutations with experimentally derived humanized versions, showing high overlap (77-82%) with experimental designs.
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.