Computational Antibody Papers

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generative methods
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2024
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  • 2024-07-30

    In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding

    • protein design
    • generative methods
    • Inverse folding method for redesign of binding antibodies from crystal structure.
    • IgDesign draws from LM-design which introduces structural conditioning of language models. They fine-tune ProteinMPNN on Sabdab to get an antibody specific IgMPNN. The structural embeddings from IgMPNN are then used in ESM2-3B. The method receives coordinates antigen & antibody but no CDRs, as these are designed.
    • They experimentally validated the protocol on 8 antigens with co-complex in the PDB, some of which are given in the poster presenting the method: https://www.absci.com/antibody-inverse-folding/
    • They design either the CDR-h3 only or all the heavy chain CDRs. They accuracy of designs to still bind after the CDR-H3 redesign is in the ballpark of 20/30% with two outliers with 70% and 90%. Success rate of redesign of all CDRs of heavy chain is lower but in some cases even comparable to the success rate of CDR-H3 redesign alone.

  • 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.

  • 2024-07-09

    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.

  • 2024-03-13

    Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization

    • language models
    • generative methods
    • Proposing a Bayesian scheme to optimally select generated antibodies from a previously introduced language model (GLM).
    • They use the 1B GLM-AB model from BioMap. Training involves variation on MLM that masks entire spans of sequence.
    • The entire point is how to ‘select’ better antibodies according to some unknown ‘fitness function’. If you only get a few experimental data points at a time to evaluate f, you’d better make them count. Their combination of bayesian scheme and a language model optimizes how the ‘next generated sequence points’ are picked so that best approximation to f is reached.
    • They use (computational simulation) Absolut! framework rather than wet lab data.

  • 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.