AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies

• URL: http://arxiv.org/abs/2308.05027v2
• Date: Wed, 6 Mar 2024 17:15:51 GMT
• Title: AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies
• Authors: Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas
• Abstract summary: AbDiffuser is an equivariant and physics-informed diffusion model for antibody 3D structures and sequences. Our approach improves protein diffusion by taking advantage of domain knowledge and physics-based constraints. Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set.
• Score: 44.149969082612486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new representation of protein structure, relies on a novel architecture for aligned proteins, and utilizes strong diffusion priors to improve the denoising process. Our approach improves protein diffusion by taking advantage of domain knowledge and physics-based constraints; handles sequence-length changes; and reduces memory complexity by an order of magnitude, enabling backbone and side chain generation. We validate AbDiffuser in silico and in vitro. Numerical experiments showcase the ability of AbDiffuser to generate antibodies that closely track the sequence and structural properties of a reference set. Laboratory experiments confirm that all 16 HER2 antibodies discovered were expressed at high levels and that 57.1% of the selected designs were tight binders.

Related papers

• Improving Antibody Design with Force-Guided Sampling in Diffusion Models [39.94753945046461]
  We propose a novel approach to enhance the sampling process of diffusion models by integrating force field energy-based feedback. Our model, DiffForce, employs forces to guide the diffusion sampling process, effectively blending the two distributions.
  arXiv  Detail & Related papers  (2024-06-09T15:50:35Z)
• De novo antibody design with SE(3) diffusion [0.3666125285899499]
  We introduce IgDiff, an antibody variable domain diffusion model based on a general protein backbone diffusion framework. We find that IgDiff produces highly designable antibodies that can contain novel binding regions. We compare our model with a state-of-the-art generative backbone diffusion model on a range of antibody design tasks.
  arXiv  Detail & Related papers  (2024-05-13T10:27:17Z)
• Diffusion Language Models Are Versatile Protein Learners [80.51049288791717]
  diffusion protein language model (DPLM) is a versatile protein language model that demonstrates strong generative and predictive capabilities for protein sequences. We first pre-train scalable DPLMs from evolutionary-scale protein sequences within a generative self-supervised discrete diffusion probabilistic framework. After pre-training, DPLM exhibits the ability to generate structurally plausible, novel, and diverse protein sequences for unconditional generation.
  arXiv  Detail & Related papers  (2024-02-28T18:57:56Z)
• A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design [54.30457372514873]
  We propose a hierarchical training paradigm (HTP) for the antibody sequence-structure co-design. HTP consists of four levels of training stages, each corresponding to a specific protein modality. Empirical experiments show that HTP sets the new state-of-the-art performance in the co-design problem.
  arXiv  Detail & Related papers  (2023-10-30T02:39:15Z)
• Protein Design with Guided Discrete Diffusion [67.06148688398677]
  A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. We propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods.
  arXiv  Detail & Related papers  (2023-05-31T16:31:24Z)
• AbODE: Ab Initio Antibody Design using Conjoined ODEs [8.523238510909955]
  We develop a new generative model AbODE that extends graph PDEs to accommodate both contextual information and external interactions. We unravel fundamental connections between AbODE and temporal networks as well as graph-matching networks.
  arXiv  Detail & Related papers  (2023-05-31T14:40:47Z)
• xTrimoABFold: De novo Antibody Structure Prediction without MSA [77.47606749555686]
  We develop a novel model named xTrimoABFold to predict antibody structure from antibody sequence. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss.
  arXiv  Detail & Related papers  (2022-11-30T09:26:08Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design [35.215029426177004]
  We propose a generative model to automatically design antibodies with enhanced binding specificity or neutralization capabilities. Our method achieves superior log-likelihood on the test set and outperforms previous baselines in designing antibodies capable of neutralizing the SARS-CoV-2 virus.
  arXiv  Detail & Related papers  (2021-10-09T18:23:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.