Prediction of hot spots towards drug discovery by protein sequence embedding with 1D convolutional neural network.

ABSTRACT: Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, which artificially extract the corresponding features of amino acid residues from sequence, structure, evolution, energy, and other information to train and test machine learning models. The process is cumbersome, time-consuming and laborious to some extent. This paper proposes a novel idea that develops a pre-trained protein sequence embedding model combined with a one-dimensional convolutional neural network, called Embed-1dCNN, to predict protei

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10506709 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature