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Congratulations to the amazing team of @williswows @MScheremetjew, @soumyadeep11194, @AHadarovich .and @HongKee and Lena from the Scientific Computing Facility at MPI-CBG! 🎉.

Are you looking for homologous sequences but alignments do not seem to work? SHARK-dive is available as a webserver 🦈: compare any sequences or search against precomputed IDR datasets: @NAR_Open @csbdresden @PoLDresden MPI-CBG

We conclude that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions. Disorder-to-order transitions predicted by AlphaFold3 and motif predictions (e.g. by SHARK-capture) may aid structure-based variant interpretation.

The distance to the second Methionine discriminates between pathogenic and benign variants associated with the N-Methionine site.

Mutations at the N-Methionine site are prevalent. Look at the surprisingly high peak at position 1.

Most mutations in disordered regions are benign but ~12% of pathogenic mutations fall within a disordered region.

The correlation between the disorder predictors is low.

It depends which disorder definition (score) is used. Only 7% of residues are consistently predicted as disordered across five different disorder prediction tools. flDPnn under predicts disorder compared to other methods.

State-of-the-art VEPs tools have lower sensitivity but higher specificity for variants in disordered regions.

Deep learning tools predict variants in disordered regions with lower sensitivity. Just published by FedericaLuppino, @swantjelenz and @williswows @mpicbg @csbdresden @PoLDresden

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News on SHARK-capture. Thanks to @williswows @swantjelenz @soumyadeep11194 @Alberti_Lab_TUD and all co-authors not on X.

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Proteome-wide prediction of motifs in S. cerevisiae are available. Matches to ELM motifs, UniProt motifs, and mutagenesis sites (UniProt) are enriched in SHARK-motifs compared to the random control. An example output: SHARK-capture score plot for Sic1.