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Hekkelman, M.

Publications and source records attributed to Hekkelman, M..

2 recordsLinked to original sources

SLFN11 restricts escape from telomere crisis to prevent alternative lengthening of telomeres

The tRNA nuclease SLFN11 is epigenetically silenced in [~]50% of treatment-naive tumours and is the strongest predictor of chemoresistance but why it is frequently inactivated in cancer is unknown. To acquire immortality, cancer cells can activate alternative lengthening of telomeres (ALT), typically accompanied by ATRX loss. Here, we implicate SLFN11 in sensing telomere replication stress, triggering eradication of ATRX deficient cells prior to ALT establishment. Whereas progressive telomere shortening of cells lacking telomerase and ATRX leads to telomere crisis and cell death, SLFN11 loss confers tolerance to PML-BLM dependent ALT intermediates, permitting emergence of ALT survivors. We propose that during tumorigenesis SLFN11 inactivation is selected as means to tolerate endogenous replication stress following telomere crisis, leading to the development of therapy resistant tumours before treatment.

molecular biology↗

AlphaBridge: tools for the analysis of predicted macromolecular complexes

Artificial intelligence (AI)-powered protein structure prediction has transformed how scientists explore macromolecular function. AI-based prediction of macromolecular complexes is increasingly used for evaluating the likelihood of proteins forming complexes with other proteins, nucleic acids, lipids, sugars, or small-molecule ligands. Efficient tools are needed to evaluate these predicted models. We introduce an approach based on combining the confidence metrics of AlphaFold3 to enable clustering of sequence motifs participating in binary interactions and subsequently in 3D interfaces of complexes. Interaction interfaces within confidence limits are finally visualised in 2D using chord diagrams and network graphs. The analysis and visualisation are implemented in a web tool, which links them with interactive graphics and summary tables of predicted interfaces and intermolecular interactions, including confidence scores. Finally, we demonstrate real-life examples of how AlphaBridge is used for providing an efficient way to assess and validate predicted protein complexes and interfaces. The reproducible, objective and automated procedures we present provide a straightforward critical assessment of structure prediction of biomolecular complexes, that should be consulted before conducting more resource-intensive analyses.

bioinformatics↗