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Published on: April 4, 2018
Comprehensive In Silico Functional Prediction Analysis of CDKL5 by Single Amino Acid Substitution in the Catalytic
Yuri Yoshimura1, Atsushi Morii1, Yuuki Fujino2
1Graduate School of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu 525-8577, Shiga, Japan.
Abstract:
Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase whose pathological mutations cause CDKL5 deficiency disorder. Most missense mutations are concentrated in the catalytic domain. Therefore, anticipating whether mutations in this region affect CDKL5 function is informative for clinical diagnosis. This study comprehensively predicted the pathogenicity of all 5700 missense substitutions in the catalytic domain of CDKL5 using in silico analysis and evaluating their accuracy. Each missense substitution was evaluated as "pathogenic" or "benign". In silico tools PolyPhen-2 HumDiv mode/HumVar mode, PROVEAN, and SIFT were selected individually or in combination with one another to determine their performance using 36 previously reported mutations as a reference. Substitutions predicted as pathogenic were over 88.0% accurate using each of the three tools. The best performance score (accuracy, 97.2%; sensitivity, 100%; specificity, 66.7%; and Matthew's correlation coefficient (MCC), 0.804) was achieved by combining PolyPhen-2 HumDiv, PolyPhen-2 HumVar, and PROVEAN. This provided comprehensive information that could accurately predict the pathogenicity of the disease, which might be used as an aid for clinical diagnosis.
Insights
Predicting Cyclin-dependent kinase-like 5 (CDKL5) mutations in its catalytic domain can aid diagnosis. Computational tools accurately identified pathogenic missense substitutions, improving CDKL5 deficiency disorder understanding.
Area of Science:
- Genetics
- Bioinformatics
- Molecular Biology
Background:
- Cyclin-dependent kinase-like 5 (CDKL5) is crucial for neurological development.
- Pathological mutations in CDKL5 cause CDKL5 deficiency disorder.
- Missense mutations frequently occur within the CDKL5 catalytic domain, impacting kinase function.
Purpose of the Study:
- To computationally predict the pathogenicity of all possible missense substitutions in the CDKL5 catalytic domain.
- To evaluate the accuracy of in silico tools for predicting mutation effects on CDKL5 function.
- To identify the optimal combination of tools for reliable pathogenicity prediction.
Main Methods:
- In silico analysis of all 5700 possible missense substitutions in the CDKL5 catalytic domain.
- Utilized PolyPhen-2 (HumDiv and HumVar modes), PROVEAN, and SIFT.
- Validated tool performance against 36 known CDKL5 mutations.
Main Results:
- Individual in silico tools achieved over 88.0% accuracy in predicting pathogenic substitutions.
- A combination of PolyPhen-2 (HumDiv and HumVar) and PROVEAN yielded the highest performance (97.2% accuracy, 100% sensitivity, 66.7% specificity, MCC 0.804).
- The combined approach accurately classified missense substitutions, providing comprehensive pathogenicity information.
Conclusions:
- In silico prediction tools can accurately assess the pathogenicity of CDKL5 missense mutations.
- Combining multiple prediction tools enhances diagnostic accuracy for CDKL5 deficiency disorder.
- This computational approach serves as a valuable aid for clinical diagnosis and genetic counseling.
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