<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Xue J</submitter><funding>Ministry of Science and Technology of the People&amp;apos;s Republic of China</funding><funding>National Natural Science Foundation of China</funding><pagination>13868-13877</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9064048</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>9(24)</volume><pubmed_abstract>The poor predictive accuracy of current computational enzyme design methods has led to low success rates of producing highly active variants that target non-natural substrates. In this report, a quantitative assessment approach based on molecular dynamics (MD) simulations was developed to eliminate false-positive enzyme designs at the computational stage. Taking cephradine hydrolase as an example, the apparent Michaelis binding constant (&lt;i>K&lt;/i> &lt;sub>m&lt;/sub>) and catalytic efficiency (&lt;i>k&lt;/i> &lt;sub>cat&lt;/sub>/&lt;i>K&lt;/i> &lt;sub>m&lt;/sub>) of designed variants were correlated with binding free energies and activation energy barriers, respectively, as calculated by molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) methods wi</pubmed_abstract><journal>RSC advances</journal><pubmed_title>Using molecular dynamics simulations to evaluate active designs of cephradine hydrolase by molecular mechanics/Poisson-Boltzmann surface area and molecular mechanics/generalized Born surface area methods.</pubmed_title><pmcid>PMC9064048</pmcid><funding_grant_id>21476123</funding_grant_id><funding_grant_id>2012AA021204</funding_grant_id><funding_grant_id>21878170</funding_grant_id><pubmed_authors>Zhu Y</pubmed_authors><pubmed_authors>Huang X</pubmed_authors><pubmed_authors>Xue J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Using molecular dynamics simulations to evaluate active designs of cephradine hydrolase by molecular mechanics/Poisson-Boltzmann surface area and molecular mechanics/generalized Born surface area methods.</name><description>The poor predictive accuracy of current computational enzyme design methods has led to low success rates of producing highly active variants that target non-natural substrates. In this report, a quantitative assessment approach based on molecular dynamics (MD) simulations was developed to eliminate false-positive enzyme designs at the computational stage. Taking cephradine hydrolase as an example, the apparent Michaelis binding constant (&lt;i>K&lt;/i> &lt;sub>m&lt;/sub>) and catalytic efficiency (&lt;i>k&lt;/i> &lt;sub>cat&lt;/sub>/&lt;i>K&lt;/i> &lt;sub>m&lt;/sub>) of designed variants were correlated with binding free energies and activation energy barriers, respectively, as calculated by molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) methods wi</description><dates><release>2019-01-01T00:00:00Z</release><publication>2019 Apr</publication><modification>2026-05-30T15:17:31.047Z</modification><creation>2025-04-04T10:01:57.53Z</creation></dates><accession>S-EPMC9064048</accession><cross_references><pubmed>35519543</pubmed><doi>10.1039/c9ra02406a</doi></cross_references></HashMap>