Spacio-Linear Screening for Ligand-Docking Cavities in Protein Structures: SLAM Algorithm

Identifying structurally similar ligand-binding sites in unrelated proteins can facilitate drug repurposing, reveal off-target effects, and deepen our understanding of protein function. A number of tools were developed for structural screening, but many of them suffer from limited sensitivity and scalability. Using a data bank of crystallized protein structures, we aimed to discover novel protein targets for a ligand by leveraging a known ligand-binding query protein with a resolved structure. Here, we present SLAM (Spacio-Linear Alignment of Macromolecules), a novel alignment-based algorithm that detects local 3D similarities between ligand-binding cavities or protein-exposed surfaces of query and target proteins. SLAM encodes spatial substructure neighborhoods into short linear sequences of physicochemically annotated atoms, then applies pairwise sequence alignment combined with distance-correlation scoring to identify high-fidelity structural matches. Benchmarking using the Kahraman-36 dataset demonstrated that SLAM outperforms the state-of-the-art ProBiS algorithm in true-positive rate for predicting ligand-docking compatibility. Furthermore, SLAM identifies candidate ligands that may inhibit functionally critical domains of CRISPR-Cas proteins and predicts novel binding partners of toxic per- and polyfluoroalkyl Substance (PFAS) compounds (PFOA, PFOS) with plausible mechanistic links to toxicity. In conclusion, SLAM is a robust computationally efficient and flexible structural screening tool capable of detecting subtle physicochemical compatibilities between protein surfaces, promising to accelerate target discovery in pharmacology and elucidate protein–ligand interactions in environmental toxicology.