A Generalized Mathematical model to understand the capacity fading in lithium ion batteries-Effects of solvent and lithium transport

A general mathematical model to study capacity fading in lithium ion batteries is developed. The model assumes that the formation of the Solid Electrolyte Interphase (SEI) layer is the primary reason behind the capacity fading in lithium ion batteries. Previous models have assumed that either the solvent or the lithium plays a key role in the film formation reaction which drives the capacity fading in lithium ion batteries. The current model postulates that the solvent species and lithium ions could play a limiting role in the capacity fade in a lithium ion battery. The model studies the concentration profiles of the solvent species and lithium ions at the electrode/film interphase as a function of diffusion and migration parameters. Model predictions are found to fit experimental data very well

A generalized mathematical model to understand the capacity fading in lithium ion batteries - Effects of solvent and lithium transport

A general mathematical model to study capacity fading in lithium ion batteries is developed. The model assumes that the formation of the Solid Electrolyte Interphase (SEI) layer is the primary reason behind the capacity fading in lithium ion batteries. Previous models have assumed that either the solvent or the lithium plays a key role in the film formation reaction which drives the capacity fading in lithium ion batteries. The current model postulates that the solvent species and lithium ions could play a limiting role in the capacity fade in a lithium ion battery. The model studies the concentration profiles of the solvent species and lithium ions at the electrode/film interphase as a function of diffusion and migration parameters. Model predictions are found to fit experimental data very well

diskin-lab-chop/AutoGVP: Release v0.4.1

<h2>What's Changed</h2> <ul> <li>add clinvar cols to abridged output by @rjcorb in https://github.com/diskin-lab-chop/AutoGVP/pull/200</li> <li>rm address_conflicting_interpretations() function and command by @rjcorb in https://github.com/diskin-lab-chop/AutoGVP/pull/202</li> <li>Update README.md - figure commit by @jharenza in https://github.com/diskin-lab-chop/AutoGVP/pull/198</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/diskin-lab-chop/AutoGVP/compare/v.0.4.0...v0.4.1</p&gt

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

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

Drugit: Crowd-sourcing molecular design of non-peptidic VHL binders

Given the role of human intuition in current drug design efforts, crowd-sourced \u27citizen scientist\u27 games have the potential to greatly expand the pool of potential drug designers. Here, we introduce ‘Drugit\u27, 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