Formal Estimation of Errors in Computed Absolute Interaction Energies of Protein−Ligand Complexes
A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions,...
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Published in: | Journal of chemical theory and computation Vol. 7; no. 3; pp. 790 - 797 |
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Main Authors: | , , , , , , , , |
Format: | Journal Article |
Language: | English |
Published: |
United States
American Chemical Society
08-03-2011
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Subjects: | |
Online Access: | Get full text |
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Summary: | A largely unsolved problem in computational biochemistry is the accurate prediction of binding affinities of small ligands to protein receptors. We present a detailed analysis of the systematic and random errors present in computational methods through the use of error probability density functions, specifically for computed interaction energies between chemical fragments comprising a protein−ligand complex. An HIV−II protease crystal structure with a bound ligand (indinavir) was chosen as a model protein−ligand complex. The complex was decomposed into 21 interacting fragment pairs, which were studied using a number of computational methods. The chemically accurate complete basis set coupled cluster theory (CCSD(T)/CBS) interaction energies were used as reference values to generate our error estimates. In our analysis, we observed significant systematic and random errors in most methods, which was surprising, especially for parametrized classical and semiempirical quantum mechanical calculations. After propagating these fragment-based error estimates over the entire protein−ligand complex, our total error estimates for many methods are large compared to the experimentally determined free energy of binding. Thus, we conclude that statistical error analysis is a necessary addition to any scoring function attempting to produce reliable binding affinity predictions. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 USDOE Office of Science (SC) AC05-00OR22725 |
ISSN: | 1549-9618 1549-9626 1549-9626 |
DOI: | 10.1021/ct100563b |