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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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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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A drug's nonlinear kinetics can be influenced by a diverse range of transporter proteins that serve as crucial players in drug distribution. These transporters, found within cells, can enhance or reduce local drug concentrations by facilitating the influx or efflux of drugs. For instance, the expression of xenobiotic transporters can be influenced by factors such as age and gender, potentially impacting the linearity of drug response.
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One example of how cells use the energy contained in electrochemical gradients is demonstrated by glucose transport into cells. The ion vital to this process is sodium (Na+), which is typically present in higher concentrations extracellularly than in the cytosol. Such a concentration difference is due, in part, to the action of an enzyme "pump" embedded in the cellular membrane that actively expels Na+ from a cell. Importantly, as this pump contributes to the high concentration of...
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How Open Data Shapes In Silico Transporter Modeling.

Floriane Montanari1, Barbara Zdrazil2

  • 1Pharmacoinformatics Research Group, Department of Pharmaceutical Chemistry, University of Vienna, A-1090 Vienna, Austria. floriane.montanari@univie.ac.at.

Molecules (Basel, Switzerland)
|March 9, 2017
PubMed
Summary
This summary is machine-generated.

Computational modeling of transmembrane transporters is shifting towards global, qualitative predictions. Careful data curation and model selection are crucial for developing safer medicines by understanding transporter functions and off-target effects.

Keywords:
applicability domaincomputational modelingdata curationmachine learningmulti-label classificationopen datatransport proteins

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Pharmacology

Background:

  • Abundant bioactivity data for transmembrane proteins is available from public sources.
  • Computational ligand-based modeling of transporters has evolved from local quantitative to global qualitative predictive models.
  • Increasing data size and heterogeneity necessitate rigorous data curation, including cutoff setting and applicability domain assessment.

Purpose of the Study:

  • To explore the shift towards global, qualitative predictive models in computational ligand-based transporter modeling.
  • To highlight the importance of data curation and model selection for transmembrane transporter modeling.
  • To discuss the challenges and potential of machine learning in predicting transporter bioactivity and off-target effects.

Main Methods:

  • Utilizing open data sources and medicinal chemistry literature for transmembrane protein data.
  • Applying machine learning algorithms, such as multi-label classification, for simultaneous prediction of multiple targets.
  • Focusing on data curation strategies, including tailored cutoff settings and applicability domain assessment.

Main Results:

  • The trend in computational transporter modeling is moving towards more global and qualitative predictive approaches.
  • Advanced machine learning enables simultaneous prediction for multiple related targets, increasing predictive power.
  • Complex models, while powerful, may suffer from reduced interpretability, necessitating careful consideration.

Conclusions:

  • Transmembrane transporters require specialized modeling approaches due to their unique roles, including potential off-target interactions.
  • Careful selection of modeling techniques and cautious interpretation of results are essential for reliable predictions.
  • Future data availability will enable improved models for function elucidation and the development of safer medicines.