STRUCTURE-BASED COMPUTATIONAL MODELING OF PROTEIN-LIGAND INTERACTIONS - APPLIED TO PROTEINS INVOLVED IN HUMAN DISEASES
type: CreativeWork
description: Protein structure-based virtual screening of large chemical libraries is commonly performed against static X-ray and NMR structures. However, proteins are dynamic macromolecules occupying different conformational spaces. Consequently, different protein structures of the same protein could exhibit different performances in ligand discovery. In this thesis, we show that X-ray holo structures of proteins perform better than NMR holo structures in virtual ligand screening, and we find that among different features, hydrogen bonds in combination with hydrophobic contacts contribute the most to the virtual screening performance of both types of structures. Furthermore, to address protein conformational flexibility in virtual screening, we show that protein structural models generated by side-chain prediction methods can perform better in virtual ligand screening than X-ray and NMR structures. Finally, protein structure-based virtual screening was performed against an important cancer target, human kidney-type glutaminase, which identified novel binders that manifest inhibition potential.
abstract: Protein structure-based virtual screening of large chemical libraries is commonly performed against static X-ray and NMR structures. However, proteins are dynamic macromolecules occupying different conformational spaces. Consequently, different protein structures of the same protein could exhibit different performances in ligand discovery. In this thesis, we show that X-ray holo structures of proteins perform better than NMR holo structures in virtual ligand screening, and we find that among different features, hydrogen bonds in combination with hydrophobic contacts contribute the most to the virtual screening performance of both types of structures. Furthermore, to address protein conformational flexibility in virtual screening, we show that protein structural models generated by side-chain prediction methods can perform better in virtual ligand screening than X-ray and NMR structures. Finally, protein structure-based virtual screening was performed against an important cancer target, human kidney-type glutaminase, which identified novel binders that manifest inhibition potential.
author: SRDAN MASIREVIC
keywords: Docking, Ligand Discovery, Computational biology, Protein flexibility, Virtual screening, Drug Discovery
date published: 2021-11-10
date modified: 2026-07-24T03:32:04Z
provider: National University of Singapore
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: National University of Singapore