Jian Gao

History Doesn’t Repeat, But Its Patterns Echo: A Parallel Pairwise Negative-Sampling Framework for Temporal Link Prediction

Natural Language-Powered Functional Protein Sequence and Structure Co-Design with Multi-Modal Knowledge Fusion

De novo protein design is pivotal for revolutionizing protein engineering and advancing life sciences. Protein co-design aims to simultaneously create a novel protein sequence and structure with tailored functions, addressing the insufficient consistency between sequence and structure of two-stage design. Current AI-assisted protein co-design approaches primarily rely on protein sequence and structure information. However, they face two major challenges in limited integration of diverse biological knowledge and insufficient understanding of 'sequence-structure-function' relations, hindering the discovery of functional and diverse proteins in de novo design. To address these challenges, we propose a Protein sequence–structure–function Consistency Design model empowered by Natural Language function description, dubbed ProtcdNl, which expands the protein design space and ensures alignment with function-aware framework. Concretely, ProtcdNl contains two core components: i) the triple-coupled collaborative encoder, which achieves implicit alignment via joint latent space constraints, precisely maps natural language functional semantics to geometric protein motifs, and ii) a function-aware equivariant decoder, which endows the model with functional awareness while strictly maintaining the symmetry of molecular dynamics. Extensive experiments on our proposed ProtSSGT corpus demonstrate that ProtcdNl effectively mines the latent associations between functional semantics and protein geometry, achieving the design of novel, diverse proteins with high functional fidelity.