Intelligent Design of Antithrombotic Peptide Targeting Collagen.

Abstract

Binding of blood components to collagen was proved to be a key step in thrombus formation. Intelligent Design of Protein Matcher (IDProMat), a neural network model, was then developed based on the principle of seq2seq to design an antithrombotic peptide targeting collagen. The encoding and decoding of peptide sequence data and the interaction patterns of peptide chains at the interface were studied, and then, IDProMat was applied to the design of peptides to cover collagen. The 99.3% decrease in seq2seq loss and 58.3% decrease in MLP loss demonstrated that IDProMat learned the interaction patterns between residues at the binding interface. An efficient peptide, LRWNSYY, was then designed using this model. Validations on its binding on collagen and its inhibition of platelet adhesion were obtained using docking, MD simulations, and experimental approaches.