The widespread use of next-generation sequencing has led to a surge in the number of identified variants with uncertain effects on protein function. These variants pose a significant challenge in diagnostics and hinder patient treatment strategies. Numerous variant effect predictors (VEPs) are available to assess variant impact, but they primarily rely on sequence-derived information. The recent development of AlphaFold2 has raised questions about whether information retrieved from wild-type or predicted structures of missense variants can improve the predictive power of these algorithms. While the AlphaFold Protein Structure Database serves as a valuable resource for wild-type protein structures, a large-scale collection of missense variant structures is not available, limiting current efforts to wild-type conformations and a handful of modeled variants. To address this limitation, we developed DeltaMut, a comprehensive database containing over 77,000 protein structures, including 65,000 pathogenic and neutral missense variants. All structural models were generated using ParaFold, a high-performance computing-optimized implementation of AlphaFold2. The large-scale and systematic generation of variant protein structures distinguish DeltaMut as a unique resource for both expansive statistical studies and detailed, case-specific investigations of variant-induced structural changes. Furthermore, the DeltaMut database is freely accessible without registration.
Qorri, E., Adam, K., Takacs, B., Shemesh, S., Buzafalvi, D., Varga, V., Pekker, E., Pinter, L., Hegedüs, Z., Csanyi, B., Haracska, L.
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