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 increasing 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.
Crystals with penta-twinned structures can be produced from diverse fcc metals, but the mechanisms that control the final product shapes are still not well understood. By using the theory of absorbing Markov chains to account for the growth of penta-twinned decahedral seeds via atom deposition and surface diffusion, we predicted the formation of various types of products: decahedra, nanorods, and nanowires. We showed that the type of product depends on the morphology of the seed and that small differences between various seed morphologies can lead to significantly different products. For the case of uncapped decahedra seeds, we compared predictions from our model to nanowire morphologies obtained in two different experiments and obtained favorable agreement. Possible extensions of our model are indicated.
Given the role of human intuition in current drug design efforts, crowd-sourced 'citizen scientist' games have the potential to greatly expand the pool of potential drug designers. Here, we introduce ‘Drugit', the small molecule design mode of the online ‘citizen science’ game Foldit. We demonstrate its utility for design with a use case to identify novel binders to the von Hippel Lindau E3 ligase. Several thousand molecule suggestions were obtained from players in a series of 10 puzzle rounds. The proposed molecules were then evaluated by in silico methods and by an expert panel and selected candidates were synthesized and tested. One of these molecules, designed by a player, showed dose-dependent shift perturbations in protein-observed NMR experiments. The co-crystal structure in complex with the E3 ligase revealed that the observed binding mode matched in major parts the player’s original idea. The completion of one full design cycle is a proof of concept for the Drugit approach and highlights the potential of involving citizen scientists in early drug discovery.
Predicting the functional impact of KCNQ1 variants of uncertain significance (VUS) can assist physicians in taking appropriate treatment decision for patients with genetic heart rhythm disorder. 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 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, that performs better or comparable with algorithms only on 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 evolutionary features in predicting variant activation voltage and deactivation time. As AI is increasing 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.
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