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Related Concept Videos

Membrane Transporters01:31

Membrane Transporters

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Transporters are essential membrane transport proteins with functions related to cell nutrition, homeostasis, communication, etc. Approximately 7% of all genes in the human genome code for transporters or transporter-related proteins.
Transporters are mainly composed of alpha-helices, built from bundles of ten or more helices traversing the plasma membrane. The solute-binding sites are located midway, where some of the helices are broken or distorted, making space for the binding site through...
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The Significance of Membrane Transport01:44

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The transport of solutes across the cell membrane is essential for metabolic processes, like maintaining cell size and volume, generating the action potential, exchanging nutrients and gases, etc. Membrane transport can be either passive or active. It can be simple diffusion, facilitated, or mediated transport aided by transport proteins such as transporters and channels.
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The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
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Membrane Proteins01:30

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Plasma membranes have integral transmembrane proteins involved in facilitated transport. These proteins are collectively referred to as transport proteins, and they function as either channels for the material or as carriers themselves. Channel proteins have hydrophilic domains exposed to the intracellular and extracellular fluids and a hydrophilic channel through their core that provides a hydrated opening for solutes to pass through the membrane layers. Passage through the channel allows...
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Primary Active Transport01:29

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In contrast to passive transport, active transport involves a substance being moved through membranes in a direction against its concentration or electrochemical gradient. There are two types of active transport: primary active transport and secondary active transport. Primary active transport utilizes chemical energy from ATP to drive protein pumps embedded in the cell membrane. With energy from ATP, the pumps transport ions against their electrochemical gradients—a direction they would...
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Carrier-mediated transport is a pivotal process in drug absorption, particularly for lipid-insoluble drugs, and encompasses facilitated diffusion and active transport. Facilitated diffusion allows drugs to move along their concentration gradient without energy expenditure, while active transport utilizes ATP to drive drug movement against this gradient.
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Related Experiment Video

Updated: Jun 4, 2025

Selection of Transporter-Targeted Inhibitory Nanobodies by Solid-Supported-Membrane SSM-Based Electrophysiology
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Identifying optimal substrate classes of membrane transporters.

Andreas Denger1, Volkhard Helms1

  • 1Center for Bioinformatics, Saarland University, Saarbrücken, Germany.

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Summary

This study introduces an automated pipeline using machine learning to optimize functional annotations for membrane transporters. It significantly reduces complex datasets, enabling better supervised learning for identifying transporter functions and substrates across diverse organisms.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Membrane transporters are crucial for molecular transport across biological membranes, impacting key biological pathways.
  • Accurate identification of membrane transporters and their substrates is vital for biotechnology, pharmacology, and metabolomics.
  • Existing protein functional annotations (e.g., Gene Ontology) are often too complex, redundant, and hierarchical for supervised learning.

Purpose of the Study:

  • To develop an automated pipeline for selecting an optimal subset of functional annotations for membrane transporters.
  • To create machine learning-ready training datasets for predicting transporter function and substrate specificity.
  • To reduce annotation complexity while maintaining biological relevance and class separability.

Main Methods:

  • Development of an automated pipeline employing machine learning to identify a minimal, non-redundant set of functional annotations.
  • Implementation of criteria for class selection: sufficient sample size, minimal redundancy, strong separability, and transport relevance.
  • Application of the pipeline to create training datasets for transmembrane transporters across various organisms (yeast, plants, bacteria, mammals).

Main Results:

  • Reduced 287 functional annotations to 11 GO terms for S. cerevisiae transporters, achieving a median pairwise F1 score of 0.87±0.16.
  • For a multi-organism dataset, reduced 695 annotations to 49 terms with a median F1 score of 0.92±0.10.
  • Even with 67% protein coverage, the pipeline identified 30 GO terms with a high median F1 score of 0.95±0.06.

Conclusions:

  • The automated pipeline effectively reduces complex functional annotation datasets for membrane transporters.
  • The selected subset of annotations is suitable for training predictive models, improving the study of transporter functions and substrates.
  • This approach facilitates large-scale analysis of transporter proteins across diverse biological systems.