Fig. 2: The design architecture of LassoPred.

Taking a lasso peptide sequence as input, LassoPred first uses its annotator to predict up to three sets of sequence annotations (ring, loop, tail length), and then employs its constructor to transform each set of annotations into a 3D structure. The annotator consists of an isopeptide classifier and a plug classifier, each trained as a machine learning classifier using 47 lasso peptide PDB structures. The constructor includes a scaffold construction module to build an all-glycine lasso peptide scaffold template matching the annotated ring, loop, and tail lengths, a mutant generation module to create a 3D lasso peptide structure matching the input sequence, and an optimization module to refine the generated structures using a molecular mechanics force field.