Computational Antibody Papers

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2024
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  • 2024-10-24

    Pretrainable geometric graph neural network for antibody affinity maturation

    • binding prediction
    • protein design
    • GearBind - Novel framework to predict the effect of mutations on an antibody-antigen complex
    • The architecture is graph-based, trained in a contrastive fashion on real atoms and their surroundings versus randomly samples (from rotamer libraries) atoms within the same environment. They use the real proteins from CATH for this purpose. The random points are serving as ‘negatives’ for contrastive learning whereas the real ones as positives.
    • The method shows improvement on previous datasets: SKEMPI and the Absci HER2 dataset.
    • The authors demonstrated the effectiveness of the method by performing in silico affinity maturation on two existing binders.

  • 2024-08-28

    Unsupervised evolution of protein and antibody complexes with a structure-informed language model

    • generative methods
    • protein design
    • Authors demonstrate that using inverse folding, one can affinity mature antibodies, confirmed experimentally.
    • Authors employ ESM-IF as the inverse folding algorithm.
    • They take two existing antibodies, bebletovimab and BD55-5840, both instrumental in COVID-19.
    • They introduce all possible single point mutations to the Vh and Vl regions (about 4300). They pick the best perplexity for experimental characterization.
    • The best perplexity ones have many framework mutations (bebletovimab 10/14 and BD55 5840 3/6). There was only one mutation to CDR-H3 in Bebletovimab.
    • Inverse folding mother achieves much better performance when antigen is used as well.

  • 2024-08-15

    De novo antibody design with SE(3) diffusion

    • protein design
    • IgDiff - antibody-specific diffusion method to generate antibody-like coordinates.
    • The method is the result of fine-tuning FrameDiff on ~150,000 antibody models (ABB2) from OAS.
    • The method supports several design scenarios, such as generating the whole Vh/Vl, just the CDRs, CDR-H3 or light chain redesign.
    • Several antibody coordinates that were generated using IgDiff and whose sequence was predicted using AbMPNN were successfully synthesized in the lab.

  • 2024-07-30

    AntiFold: Improved antibody structure design using inverse folding

    • generative methods
    • protein design
    • Inverse folding method specific to antibodies.
    • They employed ESM-IF as a base model for fine tuning.
    • They made one pass through ABodyBuilder2 models of paired OAS sequences (~150k sequences) and then ~2000 crystal structures.
    • They tested whether shotgun masking (random residues) is better than span-masking. Though shotgun performed better in general, span-masking is better in case the the entire CDRs are obscured (realistic case for design).
    • AntiFold improves upon author’s earlier ab-specific inverse folding method AbMPNN (fine-tuned ProteinMPNN), 43 % vs 60% sequence recovery on CDR-H3.
    • Authors took a handful of native structures, sampled sequences using different methods and modeled them using ABodyByuilder2 to see if the sampled sequences maintain the same fold. AntiFold achieves better (0.67) RMSD to the original backbone than AbMPNN (0.74) and ESM-IF (0.75).

  • 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.

  • 2024-07-23

    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.