Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions

Despite the success of pretrained natural language processing (NLP) models in various fields, their application in computational biology has been hindered by their reliance on biological sequences, which ignores vital three-dimensional (3D) structural information incompatible with the sequential architecture of NLP models. Here we present a topological transformer (TopoFormer), which is built by integrating NLP models and a multiscale topology technique, the persistent topological hyperdigraph Laplacian (PTHL), which systematically converts intricate 3D protein–ligand complexes at various spatial scales into an NLP-admissible sequence of topological invariants and homotopic shapes. PTHL systematically transforms intricate 3D protein–ligand complexes into NLP-compatible sequences of topological invariants and shapes, capturing essential interactions across spatial scales. TopoFormer gives rise to exemplary scoring accuracy and excellent performance in ranking, docking and screening tasks in several benchmark datasets. This approach can be utilized to convert general high-dimensional structured data into NLP-compatible sequences, paving the way for broader NLP based research. Transformers show much promise for applications in computational biology, but they rely on sequences, and a challenge is to incorporate 3D structural information. TopoFormer, proposed by Dong Chen et al., combines transformers with a mathematical multiscale topology technique to model 3D protein–ligand complexes, substantially enhancing performance in a range of prediction tasks of interest to drug discovery.