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eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands

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Abstract

Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein–ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15–40 % in binding site detection and by 5–35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75–78 %, which can be further improved by 3–4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite.

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Acknowledgments

This study was supported by the Louisiana Board of Regents through the Board of Regents Support Fund [contract LEQSF(2012–15)-RD-A-05] and Oak Ridge Associated Universities (ORAU) through the 2012 Ralph E. Powe Junior Faculty Enhancement Award. Portions of this research were conducted with high performance computational resources provided by Louisiana State University (http://www.hpc.lsu.edu).

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Authors and Affiliations

  1. Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA

    Michal Brylinski & Wei P. Feinstein

  2. Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA

    Michal Brylinski

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  1. Michal Brylinski
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Correspondence to Michal Brylinski.

Appendix

Appendix

Molecular fingerprints are bit strings that represent the structural and chemical features of organic compounds (see Daylight manual for details: http://www.daylight.com/dayhtml/doc/theory/index.pdf). Tanimoto coefficient is the most popular measure to quantify the similarity of two sets of bits (e.g. molecular fingerprints). Classical Tanimoto coefficient (TC) [60] is defined as:

$$ TC = \frac{c}{a + b + c} $$
(2)

where a is the count of bits on in the 1st string but not in the 2nd string, b is the count of bits on in the 2nd string but not in the 1st string, and c is the count of the bits on in both strings.

In addition to the classical Tanimoto coefficient, the overlap between two molecular fingerprints can be measured by the average Tanimoto coefficient (aveTC) [84]:

$$ aveTC = \frac{{TC + TC^{'} }}{2} $$
(3)

where TC′ is the Tanimoto coefficient calculated for bit positions set off rather than set on.

Furthermore, a version of the Tanimoto coefficient for continuous variables (conTC) [85] was developed:

$$ conTC = \frac{{\sum {x_{pi} x_{ci} } }}{{\sum {x_{pi}^{2} + } \sum {x_{ci}^{2} - \sum {x_{pi} x_{ci} } } }} $$
(4)

where x pi is the i-th descriptor of a fingerprint profile and x ci is the i-th descriptor of a query compound. The fingerprint profile is constructed from individual fingerprints for a set of compounds, e.g. template-bound ligands that were used to identify a putative binding site in the target structure.

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Brylinski, M., Feinstein, W.P. eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des 27, 551–567 (2013). https://doi.org/10.1007/s10822-013-9663-5

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