Towards deep learning sequence-structure co-generation for protein design
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today's models focus on generating either sequences or structures, emerging co-generation methods promise more accurate and controllable protein design, ideally achieved by modeling both modalities simultaneously. Here we review recent advances in deep generative models for protein design, with a particular focus on sequence-structure co-generation methods. We describe the key methodological and evaluation principles underlying these methods, highlight recent advances from the literature, and discuss opportunities for continued development of sequence-structure co-generation approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningProtein DesignMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching
Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating fully atomistic structures jointly with the underlying amino acid sequenc…
Protein DesignTaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
Designing protein sequences with specific biological functions and structural stability is crucial in biology and chemistry. Generative models already demonstrated their capabilities for reliable protein design. However,…
Protein DesignTowards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion
Generative models of macromolecules carry abundant and impactful implications for industrial and biomedical efforts in protein engineering. However, existing methods are currently limited to modeling protein structures o…
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences. In this paper, we exploit the rich bio…
DiversityDrug DesignInductive BiasLanguage Modelling+1Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
Despite the exciting progress in target-specific de novo protein binder design, peptide binder design remains challenging due to the flexibility of peptide structures and the scarcity of protein-peptide complex structure…