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Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction
| Citations: | 5 - 0 self |
BibTeX
@MISC{Liu_comparativemodeling:,
author = {Tianyun Liu and Grace W. Tang and Emidio Capriotti},
title = {Comparative Modeling: The State of the Art and Protein Drug Target Structure Prediction},
year = {}
}
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Abstract
Abstract: The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.
Keyphrases
comparative modeling protein drug target structure prediction comparative modeling method protein target protein kinase family structure prediction large number accurate computational method structure-based drug design theoretical basis available automatic method protein structure relevant application protein mutation structure-guided virtual screening unknown structure computational protein structure prediction protein data bank sequence similarity accurate model protein-coupled receptor significant progress predicted structure homologous structure tremendous number important drug target protein wide range experimental result automatic prediction pipeline
