Chapter title |
G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures
|
---|---|
Chapter number | 8 |
Book title |
Protein Function Prediction
|
Published in |
Methods in molecular biology, April 2017
|
DOI | 10.1007/978-1-4939-7015-5_8 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7013-1, 978-1-4939-7015-5
|
Authors |
Hui Sun Lee, Wonpil Im |
Editors |
Daisuke Kihara |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 2 | 22% |
Student > Ph. D. Student | 2 | 22% |
Student > Doctoral Student | 1 | 11% |
Professor | 1 | 11% |
Researcher | 1 | 11% |
Other | 1 | 11% |
Unknown | 1 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 5 | 56% |
Computer Science | 1 | 11% |
Agricultural and Biological Sciences | 1 | 11% |
Immunology and Microbiology | 1 | 11% |
Unknown | 1 | 11% |