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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Updated: Feb 17, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Learning epistatic interactions from sequence-activity data to predict enantioselectivity.

Julian Zaugg1, Yosephine Gumulya2, Alpeshkumar K Malde2

  • 1School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, 4072, Australia. julian.zaugg@uqconnect.edu.au.

Journal of Computer-Aided Molecular Design
|December 14, 2017
PubMed
Summary

Predicting enzyme selectivity is key for efficient synthesis. This study shows that while linear models work well, incorporating epistatic interactions using higher-order models significantly improves the prediction of mutations that enhance enzyme selectivity.

Keywords:
Aspergillus nigerBioinformaticsEpoxide hydrolaseFitnessMachine learningNon-additiveSupport vector machine

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Area of Science:

  • Biocatalysis
  • Computational Chemistry
  • Protein Engineering

Background:

  • Enzymes with high selectivity are crucial for cost-effective synthesis of enantiopure compounds.
  • Mutations near the enzyme's active site can improve selectivity, but epistatic interactions between residues complicate predictions.
  • Epistatic interactions, where mutation effects are non-additive, significantly influence enzyme selectivity.

Purpose of the Study:

  • To develop predictive models for enzyme enantioselectivity based on mutations.
  • To investigate the role of epistatic interactions in enzyme selectivity.
  • To compare the performance of linear and higher-order models in predicting mutation effects on enzyme selectivity.

Main Methods:

  • Support vector machine regression models were used to map mutations to enantioselectivities.
  • 136 variants of epoxide hydrolase from Aspergillus niger (AnEH) were analyzed.
  • Linear and polynomial (degree=2) models were compared using k-fold cross-validation.

Main Results:

  • Linear models achieved a mean Pearson coefficient (r) of 0.84, while polynomial degree=2 models reached 0.91.
  • Models incorporating epistatic interactions showed improved prediction accuracy compared to those on interaction-minimized sequences.
  • Testing on additional AnEH mutants confirmed that higher-order models (r=0.92) outperform linear models (r=0.86).

Conclusions:

  • Linear models provide a good baseline for predicting enzyme selectivity.
  • Representing epistatic interactions in predictive models significantly enhances the identification of selectivity-boosting mutations.
  • Higher-order kernel functions in machine learning models are effective in capturing epistatic effects for improved enzyme engineering.