Computational Antibody Papers

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  • 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.

  • 2024-07-23

    Atomically accurate de novo design of single-domain antibodies

    • binding prediction
    • protein design
    • Novel method to design antibodies, validated on VHHs.
    • The protocol includes an antibody-fine tuned RFdiffusion to create the coordinates for the backbone followed by creating the sequence using ProteinMPNN (not antibody-fine-tuned). Filtering by RoseTTafold2 can be used.
    • Fine-tuning RFDiffusion includes giving the coordinates of the antigen, framework of the antibody and information on the epitope. Only the CDR coordinates and orientation of the framework/CDRs are designed.
    • They fine-tuned RoseTTafold2 with the aim to filter out the RFdiffusion designs.
    • The fine-tuned RoseTTafold2 can accurately distinguish correct and incorrect ab-ag pairs, but when hot-spot information is provided (aka local docking).
    • The best designs range from micromolar and high nanomolar and roughly 1 in 100 designs works as intended : https://www.nature.com/articles/d41586-024-00846-7

  • 2024-07-09

    Robust deep learning–based protein sequence design using ProteinMPNN

    • protein design
    • generative methods
    • ProteinMPNN is a framework that receives a backbone and generates the most probable sequence that could fit it. This is firmly for protein design where you have a binding interface or a structure that you ‘need to fit’.
    • Using distances is better than using dihedral angles: This resulted in a sequence recovery increase from 41.2% (baseline model) to 49.0% (experiment 1), see Table 1 below; interatomic distances evidently provide a better inductive bias to capture interactions between residues than dihedral angles or N-Ca-C frame orientations
    • We found that training models on backbones to which Gaussian noise (std=0.02Å) had been added improved sequence recovery on confident protein structure models generated by AlphaFold (average pLDDT>80.0) from UniRef50, while the sequence recovery on unperturbed PDB structures decreased.
    • They employed a scaffold made by Rosetta, that was supposed to house a peptide recognizing some protein. The rosetta designs failed, but when they used ProteinMPNN to get sequences for it, they bound even better than the original bare-peptide.

  • 2024-02-26

    Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures

    • protein design
    • generative methods
    • structure prediction
    • Diffusion-based antibody-antigen binding site structural co-design
    • Sampling of antibody sequence and structure directly conditional on the antigen structure.
    • Model receives antigen structure and antibody framework in complex. Then CDRs are randomly initialized with AA types, orientations and positions.
    • The advantage over GANs and VAEs should be that it generates candidates iteratively so filters can be applied on the fly to the sampling process.
    • Diffusion probabilistic models learn to generate data via denoising samples from a prior distribution
    • They predict the amino acid type, ca coordinate and orientation in SO(3)
    • In addition to the joint design of sequences and structures, we can constrain partial states for other design tasks. For example, by fixing the backbone structure (positions and orientations) and sampling only sequences, we can do fix-backbone sequence design.
    • We cluster antibodies in the database according to CDR-H3 sequences at 50% sequence identity.
    • RMSD: is the Cα root-mean-square deviation (RMSD) between the generated structure and the original structure with only antibody frameworks aligned - however here higher RMSD means that the generated structure is more diverse.
    • However they also checked how accurate they are in RMSD when they fix sequences (so structure gets modified). Here for H3 they achieve 3.246 A.
    • AAR: is the amino acid recovery rate measured by the sequence identity between the reference CDR sequences and the generated sequences
    • They compared to RosettaAntibodyDesign by IMP (percentage of CDRs with better energy than the original cdr, AAR and Ca RMSD)
    • They optimize the antibody by perturbing it for several steps (forward diffusion) and then denoise it (going backwards) to find antibodies with better IMP but they also look at RMSD and Seq id.