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DNA damage and repair are widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity. Partial Order Optimum Likelihood (POOL), a machine learning method that uses input features from Theoretical Microscopic Titration Curve Shapes (THEMATICS), was used to identify amino acid residues most likely involved in catalytic activity. The μ4 value, a metric obtained from POOL and THEMATICS that serves as a measure of the degree of coupling between one ionizable amino acid and its neighbors, was then used to identify which protein mutations are likely to impact the biochemical activity. Bioinformatic tools SIFT, PolyPhen-2, and FATHMM predicted most of these variants to be deleterious to function. Along with computational and bioinformatic predictions, we characterized the catalytic activity and stability of seventeen cancer-associated DNA pol kappa variants. We identified pol kappa variants R48I, H105Y, G147D, G154E, V177L, R298C, E362V, and R470C as having lower activity relative to wild-type pol kappa; the pol kappa variants T102A, H142Y, R175Q, E210K, Y221C, N330D, N338S, K353T, and L383F were identified as similar in catalytic efficiency to WT pol kappa. We observed that POOL predictions can be used to predict which variants have decreased activity. Predictions from bioinformatic tools like SIFT, PolyPhen-2 and FATHMM are based on sequence comparisons and therefore are complementary to POOL but are less capable of predicting biochemical activity. These bioinformatic and computational tools can be used to identify SNP variants with deleterious effects and altered biochemical activity from a large data set.
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Computational chemistry methods to investigate the effects caused by DNA variants linked with disease
• Most frequently occurring human DNA variants are single nucleotide variants resulting in a change of DNA base. • Their effect on the wild type characteristics of the corresponding protein can be predicted by the means of computational chemistry. • Dysfunctional protein may have predominant effect on a particular organ in the human body.
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- Award ID(s):
- 1725573
- PAR ID:
- 10201297
- Date Published:
- Journal Name:
- Journal of Theoretical and Computational Chemistry
- Volume:
- 19
- Issue:
- 06
- ISSN:
- 0219-6336
- Page Range / eLocation ID:
- 1930001
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S0219633619300015
