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.
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Acknowledgments
This work was supported by NIH U54GM087519, KU GRF2301048, and XSEDE MCB070009.
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Lee, H.S., Im, W. (2017). G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures. In: Kihara, D. (eds) Protein Function Prediction. Methods in Molecular Biology, vol 1611. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7015-5_8
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DOI: https://doi.org/10.1007/978-1-4939-7015-5_8
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