Assessing the Performance of In silico Tools and Molecular Dynamics Simulations for Predicting Pharmacogenetic...

Area of Science:

• Pharmacogenomics
• Computational Biology
• Bioinformatics

Background:

• Non-synonymous single nucleotide polymorphisms (nsSNPs) in pharmacogenes can alter protein function, affecting drug response.
• The predictive accuracy of freely available in silico tools for nsSNP impact on pharmacogene function remains unclear.

Purpose of the Study:

• To evaluate the performance of sequence-based and structure-based in silico tools in predicting the functional impact of nsSNPs in key pharmacogenes.
• To investigate the utility of molecular dynamic (MD) simulations for assessing nsSNP effects in CYP2C9.

Main Methods:

• Assessed seven sequence-based and five structure-based tools using 118 nsSNPs in CYP2C19, CYP2C9, CYP2B6, CYP2D6, and DPYD genes with known functional outcomes.
• Performed MD simulations of CYP2C9 variants (*3 and *8) with S-warfarin to analyze substrate-binding and metabolic site interactions.

Main Results:

• Sequence-based tools showed higher positive predictive value but lower negative predictive value compared to structure-based tools.
• Accuracy did not significantly differ between sequence-based (59%) and structure-based (34%) tools.
• MD simulations revealed that CYP2C9 variants (I359L and R150H) alter S-warfarin binding and positioning, potentially hindering metabolism.

Conclusions:

• Current sequence and structure-based in silico tools have limitations in accurately predicting nsSNP effects on pharmacogene function.
• Molecular dynamic simulations may provide a more reliable method for understanding the functional consequences of nsSNPs in pharmacogenes.