Prediction of Novel Disease-Related Regions in SIGLEC-7 by In Silico and Biochemical Analyses

SIGLECs are well-known receptors that distinguish self from non-self by binding to sialic acid-containing glycoconjugates, thereby regulating normal immune functions. They have also been associated with several diseases, including systemic sclerosis, leukemia, and Alzheimer’s disease. To identify pathogenic regions related to ligand binding in SIGLECs using a novel approach, we employed the in silico Individual Meta Random Forest (InMeRF) program, which predicts disease-related amino acid substitutions. InMeRF predicted a novel three-amino-acid motif (LSI) consisting of highly pathogenic amino acid residues in SIGLEC-7 and other CD33-related SIGLECs. Alanine substitution experiments and point-mutation energy calculations using SIGLEC-7 as a representative model member of the SIGLEC family showed that mutations in the LSI motif altered binding to ganglioside ligands compared with the wild type (WT) and affected structural stability, as reflected by changes in mutation energy. Structural analysis based on the crystal structure of SIGLEC-7 revealed that the LSI motif forms a buried β-strand located beneath the previously identified sialic acid-binding region (Site 2) in CD33-related SIGLEC-7. Taken together, these findings demonstrate the utility of InMeRF for identifying previously unrecognized pathogenic regions and provide new structural and functional insights into the SIGLEC family.