Bioisostere Identification by Determining the Amino Acid Binding Preferences of Common Chemical Fragments

To assist in the structural optimization of hit/lead compounds during drug discovery, various computational approaches to identify potentially useful bioisosteric conversions have been reported. Here, the preference of chemical fragments to hydrogen bonds with specific amino acid residues was used to identify potential bioisosteric conversions. We first compiled a data set of chemical fragments frequently occurring in complex structures contained in the Protein Data Bank. We then used a computational approach to determine the amino acids to which these chemical fragments most frequently hydrogen bonded. The results of the frequency analysis were used to hierarchically cluster chemical fragments according to their amino acid preferences. The Euclid distance between amino acid preferences of chemical fragments for hydrogen bonding was then compared to MMP information in the ChEMBL database. To demonstrate the applicability of the approach for compound optimization, the similarity of amino acid preferences was used to identify known bioisosteric conversions of the epidermal growth factor receptor inhibitor gefitinib. The amino acid preference distance successfully detected bioisosteric fragments corresponding to the morpholine ring in gefitinib with a higher ROC score compared to those based on topological similarity of substituents and frequency of MMP in the ChEMBL database

Schematic procedure of the database integration.

<p>Schematic procedure of the database integration.</p

Box plot showing the distribution of IC₅₀ values of 263 compounds with more than three reported IC₅₀ values.

<p>The compounds were sorted by mean values.</p

Box plot of IC₅₀ distribution about each Murcko framework.

<p>(a) IC₅₀ values for 82 Murcko frameworks containing more than 10 compounds sorted by the IC₅₀ values of the most potent inhibitors. (b) Corresponding IC₅₀ values reported before 2009. The horizontal axes of both plots represent each of the 82 Murcko frameworks.</p

Transition in the number of unique compounds and coverage of Murcko frameworks for hERG inhibitors, inactive compounds, and all reported compounds.

<p>Coverage of Murcko frameworks was calculated as the ratio to those of all ChEMBL22 compounds (438,551).</p

Comparison between mean pIC₅₀ values of 209 compounds measured by binding assays and electrostatic assays.

<p>Comparison between mean pIC₅₀ values of 209 compounds measured by binding assays and electrostatic assays.</p

Six Murcko frameworks showing more than 10,000-fold potency differences.

<p>Six Murcko frameworks showing more than 10,000-fold potency differences.</p

Histogram of the number of compounds in each Murcko frameworks.

<p>Histogram of the number of compounds in each Murcko frameworks.</p

Histogram showing the number of assay records for each compound.

<p>The vertical axis representing the frequency is shown in logarithmic scale.</p

Distribution of 6 physicochemical properties showing significant differences.

<p>The distributions of hERG inhibitors and inactive compounds are shown as red and blue lines, respectively.</p