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- W3045100294 endingPage "1979" @default.
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- W3045100294 abstract "Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases." @default.
- W3045100294 created "2020-07-29" @default.
- W3045100294 creator A5008327028 @default.
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- W3045100294 creator A5060232841 @default.
- W3045100294 creator A5078591821 @default.
- W3045100294 creator A5090617021 @default.
- W3045100294 date "2020-01-01" @default.
- W3045100294 modified "2023-10-17" @default.
- W3045100294 title "Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine" @default.
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