Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning.

ABSTRACT: This research introduces a novel pipeline that couples machine learning (ML), and molecular docking for accelerating the process of small peptide ligand screening through the prediction of peptide-protein docking. Eight ML algorithms were analyzed for their potential. Notably, Light Gradient Boosting Machine (LightGBM), despite having comparable F1-score and accuracy to its counterparts, showcased superior computational efficiency. LightGBM was used to classify peptide-protein docking performance of the entire tetrapeptide library of 160,000 peptide ligands against four viral envelope proteins. The library was classified into two groups, 'better performers' and 'worse performers'. By training the LightGBM algorithm on just 1% of the tetrapeptide library, we successfully classified the rema

SUBMITTER: Codina JR 

PROVIDER: S-EPMC10419121 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature