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Sulfation and α-amino acid conjugation are two critical biotransformation reactions in drug metabolism. Sulfation, a phase II biotransformation reaction, involves adding a polar sulfate group to a drug, enhancing its water solubility and promoting excretion. This process can either co-occur with or occur independently of glucuronidation. Nonmicrosomal sulfotransferase enzymes catalyze the process. The reaction involves 3'-phosphoadenosine-5'-phosphosulfate or PAPS coenzyme...
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Solving inequalities graphically involves using a visual approach to determine where a mathematical expression meets a specific condition, such as being greater than or less than another value. By examining the position of a graph relative to the x-axis or another graph, it becomes possible to identify the range of x-values that satisfy the inequality. This method provides an intuitive understanding of solution intervals by showing where the inequality holds true.Graphical solutions to...
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Graphical methods provide an intuitive and visual means of solving equations by representing functions on the coordinate plane. These methods are especially helpful for estimating solutions, analyzing complex expressions, or understanding the behavior of functions.To solve an equation graphically, it must first be expressed in the form y = f(x). The solution to the original equation corresponds to the x-values where the graph intersects the x-axis, meaning where f(x) = 0.For example, the linear...
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Residue-specific Incorporation of Noncanonical Amino Acids into Model Proteins Using an Escherichia coli Cell-free Transcription-translation System
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Detecting Amino Acid Coevolution with Bayesian Graphical Models.

Mariano Avino1, Art F Y Poon2

  • 1Department of Pathology and Laboratory Medicine, Western University, London, Canada. mavino@uwo.ca.

Methods in Molecular Biology (Clifton, N.J.)
|October 10, 2018
PubMed
Summary
This summary is machine-generated.

Understanding protein evolution requires analyzing coevolving residues. This study introduces a machine learning method in HyPhy software to identify networks of coevolving residues from sequence alignments.

Keywords:
Bayesian graphical modelHyPhyamino acid coevolutionepistasishepatitis C virus

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Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Computational biology

Background:

  • Comparative protein studies reveal functional and structural constraints on evolution.
  • Deleterious mutations can become permissible due to compensatory substitutions at other sites.
  • Detecting coevolving residues often involves analyzing correlated substitutions in molecular phylogenies.

Purpose of the Study:

  • To introduce a novel machine learning method for identifying coevolving residues.
  • To implement this method within the HyPhy phylogenetic software package.

Main Methods:

  • Utilized Bayesian graphical models, a machine learning approach.
  • Applied the method to sequence alignments for phylogenetic analysis.
  • Integrated the method into the open-source HyPhy software.

Main Results:

  • Successfully extracted a network of coevolving residues from sequence data.
  • Demonstrated the utility of Bayesian graphical models for this task.

Conclusions:

  • The developed method provides a powerful tool for uncovering residue coevolution.
  • This approach enhances our understanding of protein evolutionary constraints and functional relationships.