Predicting the functional impact of KCNQ1 variants with artificial neural networks.

ABSTRACT: Recent advances in experimental and computational protein structure determination have provided access to high-quality structures for most human proteins and mutants thereof. However, linking changes in structure in protein mutants to functional impact remains an active area of method development. If successful, such methods can ultimately assist physicians in taking appropriate treatment decisions. This work presents three artificial neural network (ANN)-based predictive models that classify four key functional parameters of KCNQ1 variants as normal or dysfunctional using PSSM-based evolutionary and/or biophysical descriptors. Recent advances in predicting protein structure and variant properties with artificial intelligence (AI) rely heavily on the availability of evolutionary features a

SUBMITTER: Phul S 

PROVIDER: S-EPMC9060377 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature