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Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure

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  • John A Capra
  • Roman A Laskowski
  • Janet M Thornton
  • Mona Singh
  • Thomas A Funkhouser

Abstract

Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).Author Summary: Protein molecules are ubiquitous in the cell; they perform thousands of functions crucial for life. Proteins accomplish nearly all of these functions by interacting with other molecules. These interactions are mediated by specific amino acid positions in the proteins. Knowledge of these “functional sites” is crucial for understanding the molecular mechanisms by which proteins carry out their functions; however, functional sites have not been identified in the vast majority of proteins. Here, we present ConCavity, a computational method that predicts small molecule binding sites in proteins by combining analysis of evolutionary sequence conservation and protein 3D structure. ConCavity provides significant improvement over previous approaches, especially on large, multi-chain proteins. In contrast to earlier methods which only predict entire binding sites, ConCavity makes specific predictions of positions in space that are likely to overlap ligand atoms and of residues that are likely to contact bound ligands. These predictions can be used to aid computational function prediction, to guide experimental protein analysis, and to focus computationally intensive techniques used in drug discovery.

Suggested Citation

  • John A Capra & Roman A Laskowski & Janet M Thornton & Mona Singh & Thomas A Funkhouser, 2009. "Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-18, December.
  • Handle: RePEc:plo:pcbi00:1000585
    DOI: 10.1371/journal.pcbi.1000585
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    References listed on IDEAS

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    1. Wenxu Tong & Ying Wei & Leonel F Murga & Mary Jo Ondrechen & Ronald J Williams, 2009. "Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-15, January.
    2. Yanay Ofran & Burkhard Rost, 2007. "Protein–Protein Interaction Hotspots Carved into Sequences," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-8, July.
    3. Kai Wang & Jeremy A Horst & Gong Cheng & David C Nickle & Ram Samudrala, 2008. "Protein Meta-Functional Signatures from Combining Sequence, Structure, Evolution, and Amino Acid Property Information," PLOS Computational Biology, Public Library of Science, vol. 4(9), pages 1-13, September.
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    1. Jhih-Wei Jian & Pavadai Elumalai & Thejkiran Pitti & Chih Yuan Wu & Keng-Chang Tsai & Jeng-Yih Chang & Hung-Pin Peng & An-Suei Yang, 2016. "Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-34, August.
    2. Matteo Cagiada & Sandro Bottaro & Søren Lindemose & Signe M. Schenstrøm & Amelie Stein & Rasmus Hartmann-Petersen & Kresten Lindorff-Larsen, 2023. "Discovering functionally important sites in proteins," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. Sérgio Dias & Tiago Simões & Francisco Fernandes & Ana Mafalda Martins & Alfredo Ferreira & Joaquim Jorge & Abel J P Gomes, 2019. "CavBench: A benchmark for protein cavity detection methods," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-16, October.

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