Computational Antibody Papers

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  • 2024-03-13

    Language models of protein sequences at the scale of evolution enable accurate structure prediction

    • structure prediction
    • language models
    • Introducing ESM-2 and ESMFold. Scaling transformer model parameter size to 15B allows for more precise predictions of structures.
    • They make available an atlas of 617 million predicted structures
    • Learning objective is MLM, masking 15% of protein input.
    • Perplexity ranges from 1 for a perfect model to 20 for a model that makes predictions at random. Intuitively, perplexity describes the number of amino acids the model is uncertain between when it makes a prediction
    • After 270k training steps the 8M parameter model has a perplexity of 10.45, and the 15B model reaches a perplexity of 6.37.
    • The 15B model achieves best perplexity and structural modeling accuracy.
    • For some structures, accuracy of structure prediction jumps from 7.7A at 8m parameters to 7.0A at 35m parameters and to 3.2A at 15m parameters. The 3b model brings it down to 2.8 and 15B model to 2.6. For other structures, good prediction is only achieved at 15B
    • Their structure predictor closely follows AlphaFold2, but instead of evoformer, they use the representation from the ESM-2.

  • 2024-03-13

    xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

    • language models
    • structure prediction
    • 100B parameter protein model fine-tuned and a 1B antibody-specific model.
    • For PLM training they employ data from Uniref90 and ColabFold. After filtering and deduplication they are left with approximately 350m sequences, or 100B tokens.
    • On proteins, xTrimoPGLM-100B outperforms ESM2-15B on 12 of 15 downstream tasks (e.g. thermostability, structure prediction etc.).
    • They train a 1B protein model and then fine tune it on antibodies from OAS
    • Their masking procedure includes span masking not only several residues at a time.
    • They use 678m OAS sequences.
    • They benchmarked the antibody model on naturalness and antibody structure prediction and the Xtrimo-pglm-oas outperformed ESMFold, ALphafold2 and IgFold.

  • 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.