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Development of the integrated bioinformatics resource CKMD has enabled structure-based functional annotation and prediction of cancer mutation effects in protein kinases.
To facilitate structure-functional analysis of cancer mutation effects in protein kinases we have generated and stored in CKMD structural models of a large number of protein kinase mutants (Figure S2).
Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis.
Functional mutation effects in NB cells were distinct from PC12.
The mutation effects in three sites were analyzed as follows (Table 4).
The patterns of the mutation effects in various E730 and E734 mutants, like that of the E650 mutations, are not straightforward for making such a conclusion.
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Along the same line, we expect a diminishing return of mutation effect in the Early populations and conversely mutations of high effect in the Late populations [ 63, 64].
It is possible that the assumption of not changing amino acid sequences in our mutation correction model did not fully compensate for the mutation effect in S. pombe and P. falciparum, as they have very low GC contents.
Here, h ¯ = E [ h ] is the average dominance of the mutation distribution and b uv = C[ δu, δv] is the covariance between mutation effect in sex u and v.
Mutated nucleotides are underlined and in bold and the codon of the mutation effects are indicated in bold.
The mutation effects are quantified in terms of empirical P values.
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