G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures

Methods Mol Biol. 2017:1611:97-108. doi: 10.1007/978-1-4939-7015-5_8.

Abstract

Recent advances in high-throughput structure determination and computational protein structure prediction have significantly enriched the universe of protein structure. However, there is still a large gap between the number of available protein structures and that of proteins with annotated function in high accuracy. Computational structure-based protein function prediction has emerged to reduce this knowledge gap. The identification of a ligand binding site and its structure is critical to the determination of a protein's molecular function. We present a computational methodology for predicting small molecule ligand binding site and ligand structure using G-LoSA, our protein local structure alignment and similarity measurement tool. All the computational procedures described here can be easily implemented using G-LoSA Toolkit, a package of standalone software programs and preprocessed PDB structure libraries. G-LoSA and G-LoSA Toolkit are freely available to academic users at http://compbio.lehigh.edu/GLoSA . We also illustrate a case study to show the potential of our template-based approach harnessing G-LoSA for protein function prediction.

Keywords: G-LoSA toolkit; Protein local structure comparison; Structure library search; Structure similarity; Structure-based function prediction.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Binding Sites
  • Computational Biology / methods*
  • Databases, Protein
  • Protein Binding
  • Protein Conformation
  • Proteins / analysis*
  • Proteins / chemistry*
  • Software

Substances

  • Proteins