%0 Journal Article %1 Phul2022-to %A Phul, Saksham %A Kuenze, Georg %A Vanoye, Carlos G %A Sanders, Charles R %A George, Jr, Alfred L %A Meiler, Jens %D 2022 %I Public Library of Science (PLoS) %J PLoS Comput. Biol. %K topic_lifescience %N 4 %P e1010038 %T Predicting the functional impact of KCNQ1 variants with artificial neural networks %V 18 %X 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 and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.

@article{Phul2022-to, 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 and thus fail to directly assess the biophysical underpinnings of a change in structure and/or function. The central goal of this work was to develop an ANN model based on structure and physiochemical properties of KCNQ1 potassium channels that performs comparably or better than algorithms using only on PSSM-based evolutionary features. These biophysical features highlight the structure-function relationships that govern protein stability, function, and regulation. The input sensitivity algorithm incorporates the roles of hydrophobicity, polarizability, and functional densities on key functional parameters of the KCNQ1 channel. Inclusion of the biophysical features outperforms exclusive use of PSSM-based evolutionary features in predicting activation voltage dependence and deactivation time. As AI is increasingly applied to problems in biology, biophysical understanding will be critical with respect to 'explainable AI', i.e., understanding the relation of sequence, structure, and function of proteins. Our model is available at www.kcnq1predict.org.}, added-at = {2024-09-10T11:56:37.000+0200}, author = {Phul, Saksham and Kuenze, Georg and Vanoye, Carlos G and Sanders, Charles R and {George, Jr}, Alfred L and Meiler, Jens}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/249ddbca23b9bdea742be0d6779486928/scadsfct}, copyright = {http://creativecommons.org/licenses/by/4.0/}, interhash = {3ca97d7418187c77a30fc611dae6183c}, intrahash = {49ddbca23b9bdea742be0d6779486928}, journal = {PLoS Comput. Biol.}, keywords = {topic_lifescience}, language = {en}, month = apr, number = 4, pages = {e1010038}, publisher = {Public Library of Science (PLoS)}, timestamp = {2024-09-10T14:02:01.000+0200}, title = {Predicting the functional impact of {KCNQ1} variants with artificial neural networks}, volume = 18, year = 2022 }