期刊
COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
卷 26, 期 4, 页码 769-777出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1386207325666220520102316
关键词
Mutations; Alzheimer; neurodegenerative disorder; disease-causing; discrimination; web server
This study constructed a comprehensive dataset of Alzheimer's disease-causing and neutral mutations, and developed a machine learning algorithm based on Bayes network to discriminate between these two types of mutations using sequence information alone. The developed method achieved an accuracy of 89% for independent test set, which is 13% higher than available generic methods. This method is freely available as a web server at https://web.iitm.ac.in/bioinfo2/alzdisc/.
Background: Alzheimer's disease (AD) is the most common neurodegenerative disorder that affects the neuronal system and leads to memory loss. Many coding gene variants are associated with this disease and it is important to characterize their annotations. Methods: We collected the Alzheimer's disease-causing and neutral mutations from different databases. For each mutation, we computed the different features from protein sequence. Further, these features were used to build a Bayes network-based machine-learning algorithm to discriminate between the disease-causing and neutral mutations in AD. Results: We have constructed a comprehensive dataset of 314 Alzheimer's disease-causing and 370 neutral mutations and explored their characteristic features such as conservation scores, position-specific scoring matrix (PSSM) profile, and the change in hydrophobicity, different amino acid residue substitution matrices and neighboring residue information for identifying the disease-causing mutations. Utilizing these features, we have developed a disease-specific tool named Alz-disc, for discriminating the disease-causing and neutral mutations using sequence information alone. The performance of the present method showed an accuracy of 89% for independent test set, which is 13% higher than available generic methods. This method is freely available as a web server at https://web.iitm.ac.in/bioinfo2/alzdisc/. Conclusions: This study is useful to annotate the effect of new variants and develop mutation specific drug design strategies for Alzheimer's disease.
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