Benchmarking antigen-aware inverse folding methods for antibody design.
Janusz, B.; Chomicz, D.; Wrobel, S.; Dudzic, P.; Polasa, A.; Martin, K.; Darnell, S.; Comeau, S. R.; Krawczyk, K.
Show abstract
Computational antibody design has seen many recent advances pioneered via the use of language models and advanced structure prediction tools. Developing a de novo antibody against a specific antigen requires structural awareness that most language models lack. A prominent class of machine learning methods combining the best of language model and structural worlds is inverse folding. This approach aims to predict a sequence that would fit a given structure. Such methods are now increasingly used to predict alternate sequences given a structure of a binder. It is known that, just like language models, such methods have certain predictive power in identifying binders. Here we performed a set of tests to reveal where, if at all, such methods provide value in the realistic setting of antibody discovery.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- ParaSurf: A Surface-Based Deep Learning Approach for Paratope-Antigen Interaction Prediction 96%
- Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors 95%
- Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces 95%
Similar papers in this journal
Similar papers in this journal
- PLMFit : Benchmarking Transfer Learning with Protein Language Models for Protein Engineering 94%
- CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug-Target Affinity 93%
- MutateX: an automated pipeline for in-silico saturation mutagenesis of protein structures and structural ensembles 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.