ABodyBuilder3: Improved and scalable antibody structure predictions
- URL: http://arxiv.org/abs/2405.20863v1
- Date: Fri, 31 May 2024 14:45:11 GMT
- Title: ABodyBuilder3: Improved and scalable antibody structure predictions
- Authors: Henry Kenlay, Frédéric A. Dreyer, Daniel Cutting, Daniel Nissley, Charlotte M. Deane,
- Abstract summary: We introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ImmuneBuilder.
We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings.
We incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties.
- Score: 3.013679260442809
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Accurate prediction of antibody structure is a central task in the design and development of monoclonal antibodies, notably to understand both their developability and their binding properties. In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties.
Related papers
- Towards Generalizable and Interpretable Motion Prediction: A Deep
Variational Bayes Approach [54.429396802848224]
This paper proposes an interpretable generative model for motion prediction with robust generalizability to out-of-distribution cases.
For interpretability, the model achieves the target-driven motion prediction by estimating the spatial distribution of long-term destinations.
Experiments on motion prediction datasets validate that the fitted model can be interpretable and generalizable.
arXiv Detail & Related papers (2024-03-10T04:16:04Z) - DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization [49.85944390503957]
DecompOpt is a structure-based molecular optimization method based on a controllable and diffusion model.
We show that DecompOpt can efficiently generate molecules with improved properties than strong de novo baselines.
arXiv Detail & Related papers (2024-03-07T02:53:40Z) - Inverse folding for antibody sequence design using deep learning [2.8998926117101367]
We propose a fine-tuned folding inverse model that is specifically optimised for antibody structures.
We study the canonical conformations of complementarity-determining regions and find improved encoding of these loops into known clusters.
arXiv Detail & Related papers (2023-10-30T13:12:41Z) - 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) - 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) - Adversarial Attack and Defense of Structured Prediction Models [58.49290114755019]
In this paper, we investigate attacks and defenses for structured prediction tasks in NLP.
The structured output of structured prediction models is sensitive to small perturbations in the input.
We propose a novel and unified framework that learns to attack a structured prediction model using a sequence-to-sequence model.
arXiv Detail & Related papers (2020-10-04T15:54:03Z) - Towards a Theoretical Understanding of the Robustness of Variational
Autoencoders [82.68133908421792]
We make inroads into understanding the robustness of Variational Autoencoders (VAEs) to adversarial attacks and other input perturbations.
We develop a novel criterion for robustness in probabilistic models: $r$-robustness.
We show that VAEs trained using disentangling methods score well under our robustness metrics.
arXiv Detail & Related papers (2020-07-14T21:22:29Z)
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.