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ABC Transporters: Importer01:27

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ATP-binding cassette or ABC transporters are a class of ATP-driven pumps that hydrolyze ATP to move solutes across the membrane. They can be grouped into importers and exporters. While exporters are present in all domains of life, importers exist only in bacteria and some plants.
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ATP-binding cassette or ABC transporter is the largest superfamily of integral membrane proteins. The transporters have transmembrane-binding domains (TMDs) and nucleotide-binding domains (NBDs). The TMDs are specific to their substrates, whereas the NBDs are similar to engines that complete ATP hydrolysis to complete the substrate transport. They can be full transporters consisting of two TMDs and NBDs, half transporters with one TMD and NBD, while some encoded with a single TMD or NBD are...
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Cleaning, disinfection, and sterilization are the methods that help to break the infection chain and prevent disease.
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Biochemical and Structural Characterization of the Carbohydrate Transport Substrate-binding-protein SP0092
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Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background.

Artem Sokolov1, Stephanie Ashenden2,3, Nil Sahin4,5

  • 1Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States.

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Summary

Predicting drug interactions with ATP Binding Cassette (ABC)-transporter substrates is crucial for drug development. Machine learning models analyzing drug chemical structures effectively predict substrate-likeness, aiding in safer drug design.

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Area of Science:

  • Pharmacology
  • Computational Chemistry
  • Biotechnology

Background:

  • ATP Binding Cassette (ABC)-transporter gene mutations impact drug bioavailability and toxicity.
  • Predicting drug substrate-likeness for ABC-transporters is vital for efficient drug development.
  • Traditional methods rely on curated membrane transfer assay data.

Purpose of the Study:

  • To explore the relationship between drug chemical structure and ABC-transporter substrate-likeness.
  • To develop a predictive model for identifying ABC-transporter substrates using machine learning.
  • To offer an alternative method for investigating ABC-transporter substrate-likeness.

Main Methods:

  • Utilized a dataset of 376 drugs and their efficacy in an engineered yeast strain lacking ABC-transporter genes (ABC-16).
  • Represented drug chemical structures using substructure keys.
  • Applied and compared various machine learning methods, including Gradient-Boosted Random Forest.

Main Results:

  • Gradient-Boosted Random Forest models achieved an Area Under the Curve (AUC) of 0.723.
  • The model demonstrated significant agreement with new, prospective experimental data.
  • Identified novel chemical substructures associated with ABC-transporter substrates.

Conclusions:

  • Machine learning models can effectively predict ABC-transporter substrate-likeness based on chemical structure.
  • This approach provides a valuable alternative to traditional experimental methods.
  • Findings contribute to a better understanding of drug-ABC transporter interactions.