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High-Throughput Expression and Purification of Human Solute Carriers for Structural and Biochemical Studies
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To What Extent Can We Extrapolate Proteochemometric Models: A Case Study for the SLC6 Transporter Family
Uday Abu-Shehab1, Gerhard Ecker1
1Department of Pharmaceutical Sciences, University of Vienna, Vienna, Austria.
Molecular Informatics
|July 28, 2026
Summary
Proteochemometrics (PCM) modeling predicts drug activity by combining protein and ligand data. While PCM models for the SLC6 transporter family showed promise, validation challenges suggest a need for advanced methods to fully realize their potential in drug discovery.
Area of Science:
- Computational chemistry
- Drug discovery
- Structural biology
Background:
- Proteochemometrics (PCM) modeling integrates protein and ligand data for predicting biological activity.
- PCM aims to facilitate drug candidate screening across protein families by extrapolating information between targets.
Purpose of the Study:
- To assess the extrapolation capabilities of PCM models from data-rich to data-poor proteins.
- To develop and validate PCM models for the SLC6 transporter family.
- To identify key residues influencing subtype selectivity within the SLC6 transporter family.
Main Methods:
- Development of PCM models for the SLC6 transporter family.
- Feature importance analysis to identify critical residue positions.
- Evaluation of model performance using target stratification and leave-one-transporter-out cross-validation strategies.
Main Results:
- PCM models for the SLC6 transporter family achieved reasonable performance (Q2 up to 0.79).
- Residue position A173 (hSERT) and its homologs (G149 in hNET, G153 in hDAT) were identified as important for subtype selectivity.
- Leave-one-transporter-out validation revealed a significant performance decrease compared to target stratification, indicating potential over-optimism in simpler validation schemes.
Conclusions:
- PCM models show potential for predicting activity within the SLC6 transporter family.
- Careful consideration of validation strategies is crucial to avoid over-optimism in PCM model assessment.
- More sophisticated approaches are needed to fully leverage PCM for identifying novel drug candidates.
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