We present a specific machine learning model to predict the stability of missense mutation in TP53 using the example of a combination of several physical experiments in which the unfolding of P53 mutations was studied depending on the denaturant concentration.
Mathematical Modeling of p53 Mutations
List of calculated and experimental values to which machine learning methods and clustering will be applied to develop a method for predicting the stability of mutant proteins
3.275
2.695
3.085
3.345
3.265
3.405
4.145
3.125
2.095
2.285
2.615
2.165
2.555
2.54
2.61
3.55
TdH (Entropy change)
0.728543
0.940725
0.305402
1.015973
0.61455
1.742776
0.667285
0.833603
1.050555
2.416545
1.251111
3.248702
2.084351
2.237484
2.804485
0.61455