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Related Concept Videos

The Significance of Membrane Transport01:44

The Significance of Membrane Transport

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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Primary Active Transport01:29

Primary Active Transport

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 not...
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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
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Eukaryotic cells have different membrane-bound organelles with distinct protein requirements. The process by which proteins are targeted to a specific organelle is called protein sorting.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

Kernel-based feature selection techniques for transport proteins based on star graph topological indices.

Carlos Fernandez-Lozano1, Marcos Gestal, Nieves Pedreira-Souto

  • 1Computer Science Faculty, University of A Coruña, 15071 A Coruña, Spain.

Current Topics in Medicinal Chemistry
|July 30, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a computational model to predict protein transporter functions, reducing expensive experimental testing. The Quantitative Structure Activity Relationship (QSAR) model accurately identifies transporter proteins from their primary structure.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Drug Metabolism

Background:

  • Predicting molecular transporter function is crucial for drug metabolism.
  • Experimental methods for identifying transporter proteins are costly and time-consuming.
  • There is a need for efficient theoretical models to predict transporter proteins.

Purpose of the Study:

  • To develop a fast and cost-effective computational model for predicting protein transporter function.
  • To establish a Quantitative Structure Activity Relationship (QSAR) model using protein primary structure.
  • To identify key topological indices for classifying transporter proteins.

Main Methods:

  • Representing protein primary structure as molecular Star graphs.
  • Utilizing topological indices as input features for classification.
  • Employing Support Vector Machine Recursive Feature Elimination for feature selection.
  • Training and evaluating classification models on a dataset of 2,503 protein chains.

Main Results:

  • A QSAR model was developed using 20 selected topological attributes.
  • The model achieved a true positive rate of 83% for identifying transporter proteins.
  • The model demonstrated a false positive rate of 16.7%.

Conclusions:

  • The developed QSAR model provides an efficient method for predicting protein transporter function.
  • Computational approaches can significantly aid in the discovery and characterization of transporter proteins.
  • This method offers a valuable alternative to expensive experimental screening in drug metabolism research.