Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Year: 2021
Type: article
Abstract: , a novel transformer-based generative framework for designing protein sequences conditioned on a specific target fold. To model the complex sequence-structure relationship, Fold2Seq jointly learns a sequence embedding using a transformer and a fold embedding from the density of secondary structural elements in 3D voxels. On test sets with single, high-resolution and complete structure inputs for individual folds, our experiments demonstrate improved or comparable performance of Fold2Seq in terms of speed, coverage, and reliability for sequence design, when compared to existing state-of-the-art methods that include data-driven deep generative models and physics-based RosettaDesign. The unique advantages of fold-based Fold2Seq, in comparison to a structure-based deep model and RosettaDesign, become more evident on three additional real-world challenges originating from low-quality, incomplete, or ambiguous input structures. Source code and data are available at https://github.com/IBM/fold2seq.
Source: PubMed
Authors: Cao, Yue, Das, Payel, Chenthamarakshan, Vijil, Chen, Pin-Yu, Melnyk, Igor, Shen, Yang
Institutions: IBM (United States), Texas A&M University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Award: G556791467
OpenAlex ID: W3125817081