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Chemometrics‐based identification, characterization, and modeling of the intermolecular interaction between human osteosarcoma SH3 domain and its peptide ligands
Author(s) -
Han Shijie,
Liu Qian,
Dong Jun,
Yuan Zeg
Publication year - 2017
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2892
Subject(s) - osteosarcoma , sh3 domain , peptide , computational biology , osteoclast , chemistry , binding selectivity , biology , biochemistry , cancer research , proto oncogene tyrosine protein kinase src , in vitro , receptor
Human osteosarcoma is the most common type of bone tumor in children and teens that exhibits binding capability for a wide spectrum of proline‐rich proteins via dophilin osteoclast–stimulating factor SH3 domain. Here, we described a chemometrics‐based approach to modeling and prediction of osteosarcoma‐binding partners. A statistical regression strategy was proposed to correlate between the structural information and binding affinity of SH3‐ligand association. The strategy was trained and validated using a set of SH3‐binding peptides. It is shown that different residues of peptide ligands contribute independently to the stability and specificity of domain‐peptide interaction, where the core motif PxxP is necessary but not sufficient for peptide binding to SH3 domain, and many other factors as well as amino acid types may also play a marginal role in the binding. The sequence pattern and residue distribution are crucial for the high affinity and selectivity in domain‐peptide recognition. Subsequently, the model was employed to perform virtual high‐throughput screening against the gene expression profile of osteosarcoma. Consequently, a variety of PxxP‐containing sequences were identified as osteoclast‐stimulating factor SH3‐binding candidates that might be the potential hubs of osteosarcoma signaling network.