Title

Computational Active Site Analysis Of Molecular Pathways To Improve Functional Classification Of Enzymes

Keywords

Active site analysis; Computational docking; Enzyme substrate interactions; Protein evolution; Structural biology

Abstract

This study describes a method to computationally assess the function of homologous enzymes through small molecule binding interaction energy. Three experimentally determined X-ray structures and four enzyme models from ornithine cyclo-deaminase, alanine dehydrogenase, and mu-crystallin were used in combination with nine small molecules to derive a function score (FS) for each enzyme-model combination. While energy values varied for a single molecule-enzyme combination due to differences in the active sites, we observe that the binding energies for the entire pathway were proportional for each set of small molecules investigated. This proportionality of energies for a reaction pathway appears to be dependent on the amino acids in the active site and their direct interactions with the small molecules, which allows a function score (FS) to be calculated to assess the specificity of each enzyme. Potential of mean force (PMF) calculations were used to obtain the energies, and the resulting FS values demonstrate that a measurement of function may be obtained using differences between these PMF values. Additionally, limitations of this method are discussed based on: (a) larger substrates with significant conformational flexibility; (b) low homology enzymes; and (c) open active sites. This method should be useful in accurately predicting specificity for single enzymes that have multiple steps in their reactions and in high throughput computational methods to accurately annotate uncharacterized proteins based on active site interaction analysis. © 2008 Wiley-Liss, Inc.

Publication Date

7-1-2008

Publication Title

Proteins: Structure, Function and Genetics

Volume

72

Issue

1

Number of Pages

184-196

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1002/prot.21907

Socpus ID

44949120724 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/44949120724

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