Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models
Abstract: Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. Designing functional ligand-binding proteins is pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, an efficient computational framework targeting the redesign of ligand-binding proteins. Using equivariant diffusion-based generative models, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our evaluations across sequence diversity, structural preservation, and ligand binding affinity underscore ProteinReDiff's potential to advance computational drug discovery and protein engineering. We will release our data and source code upon acceptance.
Submission Length: Long submission (more than 12 pages of main content)
Changes Since Last Submission: We would like to thank the Action Editor and Reviewers for their patience while we were completing the ablation studies. We have extended the ablation section to capture more aspects of the models. We were adding more plots to illustrate our points and showcased some findings along ablation studies. Thank you to all again for your feedback as we're improving our paper!
Assigned Action Editor: ~Adam_Arany1
Submission Number: 2542
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