Bioinformatics AdvancesBioinformaticsPeer-reviewed journal article
Pathogenicity Patterns in Cytochrome P450 Family
Anna ŠpačkováiD, Nina KadášováiD, Ivana Hutařová VařekováiD, Karel BerkaiD
Abstract
MotivationCytochrome P450 proteins play a crucial role in human metabolism, from the production of hormones to drug metabolism. While multiple commonly known variants have known effects on the individual cytochrome P450 protein performance, the pathogenicity information is usually experimentally limited to only a few mutations. Current pathogenicity prediction software allows one to extend the scope to virtually mutate all amino acids with missense mutations. In this work, we do a comprehensive exploration that unveils pathogenicity patterns in the human cytochrome P450 family. Pathogenicity analysis was conducted across proteins using SIFT and AlphaMissense algorithms. ResultsOur findings indicate a progressive increase in pathogenicity along protein tunnels-identified via MOLE-toward the cofactor binding site, underscoring the essential role of cofactor interactions in enzymatic function. Notably, tunnel integrity emerges as a critical factor, with even single amino acid alterations potentially disrupting molecular guidance to active sites. These insights highlight the fundamental role of structural pathways in preserving cytochrome P450 functionality, with implications for understanding disease-associated variants and drug metabolism. AvailabilityData and source code can be found at https://github.com/annaspac/P450_pathogenicity_codes Contactanna.spackova@upol.cz, karel.berka@upol.cz O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/646180v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@19f1b1aorg.highwire.dtl.DTLVardef@ac7f29org.highwire.dtl.DTLVardef@d06a6dorg.highwire.dtl.DTLVardef@fb342b_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: protein sequence
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: protein structure prediction
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
- FWCI
- 0.27
- Field percentile
- top 43%
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