ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction.

ABSTRACT: The solute carrier transporter family 6 (SLC6) is of key interest for their critical role in the transport of small amino acids or amino acid-like molecules. Their dysfunction is strongly associated with human diseases such as including schizophrenia, depression, and Parkinson's disease. Linking single point mutations to disease may support insights into the structure-function relationship of these transporters. This work aimed to develop a computational model for predicting the potential pathogenic effect of single point mutations in the SLC6 family. Missense mutation data was retrieved from UniProt, LitVar, and ClinVar, covering multiple protein-coding transcripts. As encoding approach, amino acid descriptors were used to calculate the average sequence properties for both original and mu

SUBMITTER: Huang J 

PROVIDER: S-EPMC11034476 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature