Insights into pathological mutations in insulin-like growth factor I through in silico screening and molecular dynamics simulation
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Insulin-like growth factor I (IGF-I) is an anabolic growth hormone indispensable for cell growth, proliferation, differentiation, and other metabolic processes. Three missense mutations in IGF-I have been identified to be disease-related, while more mutations are waiting for phenotype annotation. However, there is no previous work regarding effective and accurate identification of pathological mutations of IGF-I, neither regarding the effects of mutations on the protein structure and dynamics. In this study, we first predicted potential deleterious mutations present in IGF-I using 16 in silico tools. Then, these mutations were further evaluated through multiple bioinformatics methods including conservation analysis, physicochemical characterization, and molecular dynamics simulation. After rigorous screening, five mutations (T4M, V17M, V44M, R50W, and M59R) were finally selected, of which two have been previously reported to be deleterious. These mutations locate at conserved regions and change the residue size locally. In the conventional simulations, the mutations destabilized the overall IGF-I structure by destroying two important hydrogen bonds within the key region of “C-neck.” This finding was further confirmed by the thermal unfolding simulations and the free-energy calculations, where the mutants were associated with faster and greater loss of helix and lower energy barriers in comparison with the wild-type protein. The rigorous phenotype prediction and comprehensive structural analysis of missense mutations will not only pave the way of screening for harmful mutations in IGF-I but also provide new prospects for the rational design of IGF-I analogues and tailored medicine.
KeywordsPhenotype prediction Structural analysis Hydrogen bond network Structural stability Free-energy calculation
Radius of gyration
Insulin-like growth factor I
Root mean square deviation
Root mean square fluctuation
Solvent accessible surface area
Conceptualization, G.L., H.L., and H.X.; methodology, G.L., S.Z., and Y.W.; formal analysis, G.L., S.Z., and H.L.; resources, Y.W. and X.F.; writing-original draft preparation, G.L. and S.Z.; writing-review and editing, H.L. and H.X.; funding acquisition, G.L.
This work was financially supported by the Natural Science Foundation of Guangdong Province (grant number 2015A030310106), the National Natural Science Foundation of China (grant number 31500591), and the fund from Guangzhou Institute of Pediatrics/Guangzhou Women and Children’s Medical Center (grant number IP-2019-017). All simulations were supported by the National Super Computer Center in Guangzhou.
Compliance with ethical standards
The authors declare that they have no competing interests.
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