Successful Prediction of Substrate-binding Pocket in SLC17 Transporter Sialin

Secondary active transporters from the SLC17 protein family are required for excitatory and purinergic synaptic transmission, sialic acid metabolism, and renal function, and several members are associated with inherited neurological or metabolic diseases. However, molecular tools to investigate their function or correct their genetic defects are limited or absent. Using structure-activity, homology modeling, molecular docking, and mutagenesis studies, we have located the substrate-binding site of sialin (SLC17A5), a lysosomal sialic acid exporter also recently implicated in exocytotic release of aspartate. Human sialin is defective in two inherited sialic acid storage diseases and is responsible for metabolic incorporation of the dietary nonhuman sialic acid N-glycolylneuraminic acid. We built cytosol-open and lumen-open three-dimensional models of sialin based on weak, but significant, sequence similarity with the glycerol-3-phosphate and fucose permeases from Escherichia coli, respectively. Molecular docking of 31 synthetic sialic acid analogues to both models was consistent with inhibition studies. Narrowing the sialic acid-binding site in the cytosol-open state by two phenylalanine to tyrosine mutations abrogated recognition of the most active analogue without impairing neuraminic acid transport. Moreover, a pilot virtual high-throughput screening of the cytosol-open model could identify a pseudopeptide competitive inhibitor showing >100-fold higher affinity than the natural substrate. This validated model of human sialin and sialin-guided models of other SLC17 transporters should pave the way for the identification of inhibitors, glycoengineering tools, pharmacological chaperones, and fluorescent false neurotransmitters targeted to these proteins. Background: Sialin is a clinically relevant lysosomal sialic acid exporter related to vesicular glutamate transporters and other SLC17 transporters. Results: Using synthetic sialic acid analogues, homology modeling, site-directed mutagenesis, and virtual screening, we built and validated a three-dimensional model of sialin. Conclusion: The three-dimensional model successfully predicts small molecule binding to sialin. Significance: The model will help identifying pharmacological tools targeted to SLC17 transporters.