Neural message passing for joint paratope-epitope prediction

• URL: http://arxiv.org/abs/2106.00757v1
• Date: Mon, 31 May 2021 16:37:55 GMT
• Title: Neural message passing for joint paratope-epitope prediction
• Authors: Alice Del Vecchio, Andreea Deac, Pietro Li\`o and Petar Veli\v{c}kovi\'c
• Abstract summary: Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them. Prediction of binding sites in an antibody-antigen interaction are known as the paratope and, respectively, and are key to vaccine and synthetic antibody development.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Antibodies are proteins in the immune system which bind to antigens to detect and neutralise them. The binding sites in an antibody-antigen interaction are known as the paratope and epitope, respectively, and the prediction of these regions is key to vaccine and synthetic antibody development. Contrary to prior art, we argue that paratope and epitope predictors require asymmetric treatment, and propose distinct neural message passing architectures that are geared towards the specific aspects of paratope and epitope prediction, respectively. We obtain significant improvements on both tasks, setting the new state-of-the-art and recovering favourable qualitative predictions on antigens of relevance to COVID-19.

Related papers

• Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection [6.949493332885247]
  ProVaccine is a novel deep learning solution that integrates latent vector representations of protein sequences and structures. We also compile the most comprehensive immunogenicity dataset to date, encompassing over 9,500 antigen sequences, structures, and immunogenicity labels from bacteria, viruses, and tumors. Our work provides an effective tool for vaccine design and sets valuable benchmarks for future research.
  arXiv  Detail & Related papers  (2024-10-03T16:33:35Z)
• Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization [51.28231365213679]
  We tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences. We propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens.
  arXiv  Detail & Related papers  (2024-03-25T09:41:49Z)
• 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)
• AI driven B-cell Immunotherapy Design [0.0]
  The effectiveness of antigen neutralisation and elimination hinges upon the strength, sensitivity, and specificity of the paratope-epitope interaction. In recent years, artificial intelligence and machine learning methods have made significant strides, revolutionising the prediction of protein structures and their complexes. This review focuses on the progress of machine learning-based tools and their frameworks in the domain of B-cell immunotherapy design.
  arXiv  Detail & Related papers  (2023-09-03T09:14:10Z)
• 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)
• Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design [134.65287929316673]
  Deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. The computational methods heavily rely on high-quality antibody structure data, which is quite limited. Fortunately, there exists a large amount of sequence data of antibodies that can help model the CDR and alleviate the reliance on structure data.
  arXiv  Detail & Related papers  (2022-10-26T15:31:36Z)
• Antibody Representation Learning for Drug Discovery [7.291511531280898]
  We present results on a novel SARS-CoV-2 antibody binding dataset and an additional benchmark dataset. We compare three classes of models: conventional statistical sequence models, supervised learning on each dataset independently, and fine-tuning an antibody specific pre-trained language model. Experimental results suggest that self-supervised pretraining of feature representation consistently offers significant improvement in over previous approaches.
  arXiv  Detail & Related papers  (2022-10-05T13:48:41Z)
• AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation [53.43922443725598]
  We present AntBO: a Combinatorial optimisation algorithm enabling efficient in silico design of the CDRH3 region. To benchmark AntBO, we use the Absolut! software suite as a black-box oracle because it can score the target specificity and affinity of designed antibodies in silico. In under 200 protein designs, AntBO can suggest antibody sequences that outperform the best binding sequence drawn from 6.9 million experimentally obtained CDRH3s.
  arXiv  Detail & Related papers  (2022-01-29T12:03:04Z)
• Deciphering antibody affinity maturation with language models and weakly supervised learning [10.506336354512145]
  We introduce AntiBERTy, a language model trained on 558M natural antibody sequences. We find that within repertoires, our model clusters antibodies into trajectories resembling affinity maturation. We show that models trained to predict highly redundant sequences under a multiple instance learning framework identify key binding residues in the process.
  arXiv  Detail & Related papers  (2021-12-14T23:05:01Z)
• Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts [80.69440684790925]
  DeepRelations is a physics-inspired deep relational network with intrinsically explainable architecture. It shows superior interpretability to the state-of-the-art. It boosts the AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test, compound-unique, protein-unique, and both-unique sets.
  arXiv  Detail & Related papers  (2019-12-29T00:14:07Z)

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.