Anna Marabotti1, Eugenio Del Prete2, Bernardina Scafuri3, Angelo Facchiano4. 1. Department of Chemistry and Biology "A. Zambelli", University of Salerno, Fisciano, SA, Italy. amarabotti@unisa.it. 2. CNR-IAC, National Research Council, Institute for Applied Mathematics "Mauro Picone", Naples, Italy. 3. Department of Chemistry and Biology "A. Zambelli", University of Salerno, Fisciano, SA, Italy. 4. CNR-ISA, National Research Council, Institute of Food Science, Avellino, Italy. angelo.facchiano@isa.cnr.it.
Abstract
BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. RESULTS: The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. CONCLUSIONS: We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.
BACKGROUND: Despite decades on developing dedicated Web tools, it is still difficult to predict correctly the changes of the thermodynamic stability of proteins caused by mutations. Here, we assessed the reliability of five recently developed Web tools, in order to evaluate the progresses in the field. RESULTS: The results show that, although there are improvements in the field, the assessed predictors are still far from ideal. Prevailing problems include the bias towards destabilizing mutations, and, in general, the results are unreliable when the mutation causes a ΔΔG within the interval ± 0.5 kcal/mol. We found that using several predictors and combining their results into a consensus is a rough, but effective way to increase reliability of the predictions. CONCLUSIONS: We suggest all developers to consider in their future tools the usage of balanced data sets for training of predictors, and all users to combine the results of multiple tools to increase the chances of having correct predictions about the effect of mutations on the thermodynamic stability of a protein.
Entities:
Keywords:
Predictions; Protein mutations; Protein stability; Rare diseases; Statistical analysis
Authors: Anika Tahsin; Rubaiat Ahmed; Piyash Bhattacharjee; Maisha Adiba; Abdullah Al Saba; Tahirah Yasmin; Sajib Chakraborty; A K M Mahbub Hasan; A H M Nurun Nabi Journal: Comput Biol Med Date: 2022-07-20 Impact factor: 6.698