A simple workflow to identify novel small linear motif (SLiM)-mediated interactions with AlphaFold

Short linear motifs (SLiMs) are highly compact interaction modules embedded within disordered protein regions and are increasingly recognized for their central role in maintaining cellular homeostasis. Due to their small size, degeneracy and transient binding, SLiMs remain difficult to detect both experimentally and computationally. Here, we show that AlphaFold (AF), used via ColabFold, offers a practical and accessible alternative for in-silico screening of new SLiMs targeting a protein of interest. Unlike previous studies that evaluated AlphaFold2 (AF2) using structure-derived benchmarks, we extend this by assessing both AF2 and AF3, using a structure-independent benchmark of 26 interactions absent from PDB homology, and showing that MiniPAE is the most suited AlphaFold metric for SLiM screening. We also generated an unbalanced dataset with a large excess of non-binders mimicking real-world blind screening, revealing a critical limitation in AlphaFold's specificity for SLiM detection. To circumvent this constraint, we propose both a SLiM screening strategy and an adaptative scoring threshold. For greater accessibility, we provide a streamlined and cost-effective AF analysis workflow requiring no local installation or computation. To overcome challenges associated with SLiM validation, we also introduce a highly sensitive detection method based on proximity labeling in living cells. This workflow was used to identify and experimentally validate 13 new SLiMs that mediate binding to ribosomal protein S6 kinase A3 (RPS6KA3 or RSK2). By leveraging ColabFold and MiniPAE available through Colab notebooks, our approach provides a scalable and widely accessible strategy for identifying functional SLiMs in proteins of interest. MiniPAE can be accessed at https://github.com/martinovein/MiniPAE.