Ala893Thr

Ala893Thr, formally described as p.Ala893Thr, is a missense variant that replaces the amino acid alanine (Ala) with threonine (Thr) at position 893 of a protein. The notation follows the Human Genome Variation Society (HGVS) guidelines, where “p.” indicates a protein-level change, and the numeric position refers to the standard amino‑acid numbering of the mature polypeptide.

This substitution is found in several genes implicated in human disease. For example, in the BRCA1 gene a p.Ala893Thr variant has been reported in sporadic breast cancer cases, although large‐scale studies have not confirmed it as a high‑penetrance risk allele. In clinical genetics, variants at conserved positions such as 893 are often scrutinized for pathogenicity because they can disrupt protein folding or enzymatic activity. Functional assays in vitro, such as enzyme kinetics or cellular localization studies, are frequently employed to assess the effect of Ala893Thr on protein behavior.

Population frequency data indicate that Ala893Thr is generally rare in healthy control groups, with allele counts usually below 0.01 % in public databases such as gnomAD. A low frequency supports potential deleteriousness, but it does not alone determine pathogenicity. In silico tools—including PolyPhen‑2, SIFT, and MutationTaster—generally predict a deleterious or possibly damaging effect for this substitution, consistent with the introduction of a polar side chain where a non‑polar residue originally resided.

Because of its potential clinical relevance, Ala893Thr is often submitted to variant databases such as ClinVar, where interpretations range from “likely benign” to “uncertain significance” depending on the gene context and the strength of evidence. Laboratories reporting this variant typically include methods such as Sanger sequencing or next‑generation sequencing panels, with confirmatory testing in a second sample where necessary.

In summary, Ala893Thr is a specific amino‑acid change that may impact protein function and disease risk; its evaluation requires integration of population data, computational predictions, functional assays, and clinical correlation.