Advanced Composite Solid Electrolytes for Lithium Batteries: Filler Dimensional Design and Ion Path Optimization.

Composite solid electrolytes are considered to be the crucial components of all-solid-state lithium batteries, which are viewed as the next-generation energy storage devices for high energy density and long working life. Numerous studies have shown that fillers in composite solid electrolytes can effectively improve the ion-transport behavior, the essence of which lies in the optimization of the ion-transport path in the electrolyte. The performance is closely related to the structure of the fillers and the interaction between fillers and other electrolyte components including polymer matrices and lithium salts. In this review, the dimensional design of fillers in advanced composite solid electrolytes involving 0D-2D nanofillers, and 3D continuous frameworks are focused on. The ion-transport mechanism and the interaction between fillers and other electrolyte components are highlighted. In addition, sandwich-structured composite solid electrolytes with fillers are also discussed. Strategies for the design of composite solid electrolytes with high room temperature ionic conductivity are summarized, aiming to assist target-oriented research for high-performance composite solid electrolytes.

Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.

Insight into how mutations affect protein stability is crucial for protein engineering, understanding genetic diseases, and exploring protein evolution. Numerous computational methods have been developed to predict the impact of amino acid substitutions on protein stability. Nevertheless, comparing these methods poses challenges due to variations in their training data. Moreover, it is observed that they tend to perform better at predicting destabilizing mutations than stabilizing ones. Here, we meticulously compiled a new dataset from three recently published databases: ThermoMutDB, FireProtDB, and ProThermDB. This dataset, which does not overlap with the well-established S2648 dataset, consists of 4038 single-point mutations, including over 1000 stabilizing mutations. We assessed these mutations using 27 computational methods, including the latest ones utilizing mega-scale stability datasets and transfer learning. We excluded entries with overlap or similarity to training datasets to ensure fairness. Pearson correlation coefficients for the tested tools ranged from 0.20 to 0.53 on unseen data, and none of the methods could accurately predict stabilizing mutations, even those performing well in anti-symmetric property analysis. While most methods present consistent trends for predicting destabilizing mutations across various properties such as solvent exposure and secondary conformation, stabilizing mutations do not exhibit a clear pattern. Our study also suggests that solely addressing training dataset bias may not significantly enhance accuracy of predicting stabilizing mutations. These findings emphasize the importance of developing precise predictive methods for stabilizing mutations.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength.

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

[Detection of IgG protein in human urine based on vertical flow paper microfluidic chip].

The kidney is the body's most important organ and the protein components in urine could be detected for diagnosing certain diseases. The amount of IgG protein in urine could be used to determine the degree of kidney function damage. IgG protein in human urine was detected by vertical flow paper-based microfluidic chip, double-antibody sandwich immunoreaction, and cell phone image processing. The results showed that using an IgG antibody concentration of 500 µg/mL and a gold standard antibody concentration of 100 µg/mL, the image signal showed a good linear relationship in the range of IgG concentration of 0.2-3.2 µg/mL, with R2=0.973 3 achieved. A complete set of detection devices were designed and the detection method showed good non-specificity.