A new benchmark illustrates that integration of geometric constraints inferred from enzyme reaction chemistry can increase enzyme active site modeling accuracy

Steve J. Bertolani, Justin Siegel

Research output: Contribution to journalArticle

Abstract

Enzymes play a critical role in a wide array of industrial, medical, and research applications and with the recent explosion of genomic sequencing, we now have sequences for millions of enzymes for which there is no known structure. In order to utilize modern computational design tools for constructing inhibitors or engineering novel catalysts, the ability to accurately model enzymes is critical. A popular approach for modeling enzymes are comparative modeling techniques which can often accurately predict the global structural features. However, achieving atomic accuracy of an active site remains a challenge and is an issue when trying to utilize the molecular details for designing inhibitors or enhanced catalysts. Here we explore integrating knowledge about the required geometric orientation of conserved catalytic residues into the comparative modeling process in order to improve modeling accuracy. In order to investigate the utility of adding this information, we first carefully construct a benchmark set of reference structures to use. Consistent with previous findings, our benchmark demonstrates that the geometry between catalytic residues across an enzyme family is conserved and does not tend to deviate by more than 0.5Å. We then find that by integrating these geometric constraints during modeling, we can double the number of atomic level accuracy models (<1Å RMSD to the crystal structure ligand) within our benchmarking data-set, even for targets with templates as low as 20-30% sequence identity. Catalytic residues within an enzyme family are highly conserved and can often be readily identified through comparative sequence analysis to a known structure within the enzyme family. Therefore utilizing this readily available information has the potential to significantly improve drug design and enzyme engineering efforts for which there is no known structure for the enzyme of interest.

Original languageEnglish (US)
Article numbere0214126
JournalPloS one
Volume14
Issue number4
DOIs
StatePublished - Apr 1 2019

Fingerprint

reaction chemistry
Benchmarking
enzymatic reactions
active sites
Catalytic Domain
Enzymes
enzymes
catalysts
engineering
Catalysts
Explosions
Drug Design
explosions
crystal structure
Sequence Analysis
Biomedical Research
sequence analysis
Crystal structure

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

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title = "A new benchmark illustrates that integration of geometric constraints inferred from enzyme reaction chemistry can increase enzyme active site modeling accuracy",
abstract = "Enzymes play a critical role in a wide array of industrial, medical, and research applications and with the recent explosion of genomic sequencing, we now have sequences for millions of enzymes for which there is no known structure. In order to utilize modern computational design tools for constructing inhibitors or engineering novel catalysts, the ability to accurately model enzymes is critical. A popular approach for modeling enzymes are comparative modeling techniques which can often accurately predict the global structural features. However, achieving atomic accuracy of an active site remains a challenge and is an issue when trying to utilize the molecular details for designing inhibitors or enhanced catalysts. Here we explore integrating knowledge about the required geometric orientation of conserved catalytic residues into the comparative modeling process in order to improve modeling accuracy. In order to investigate the utility of adding this information, we first carefully construct a benchmark set of reference structures to use. Consistent with previous findings, our benchmark demonstrates that the geometry between catalytic residues across an enzyme family is conserved and does not tend to deviate by more than 0.5{\AA}. We then find that by integrating these geometric constraints during modeling, we can double the number of atomic level accuracy models (<1{\AA} RMSD to the crystal structure ligand) within our benchmarking data-set, even for targets with templates as low as 20-30{\%} sequence identity. Catalytic residues within an enzyme family are highly conserved and can often be readily identified through comparative sequence analysis to a known structure within the enzyme family. Therefore utilizing this readily available information has the potential to significantly improve drug design and enzyme engineering efforts for which there is no known structure for the enzyme of interest.",
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