In:
Combinatorial Chemistry & High Throughput Screening, Bentham Science Publishers Ltd., Vol. 26, No. 4 ( 2023-04), p. 769-777
Abstract:
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. Method: 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, positionspecific 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.
Type of Medium:
Online Resource
ISSN:
1386-2073
DOI:
10.2174/1386207325666220520102316
Language:
English
Publisher:
Bentham Science Publishers Ltd.
Publication Date:
2023
SSG:
15,3
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