Distinct effects of zwitterionic molecules on ionic solvation in (ethylene oxide)10: a molecular dynamics simulation study.

Ion-containing polymers play a critical role in various energy and sensing applications. Adjusting ionic solvation is one approach to tune the performance of ion-containing polymers. Small zwitterionic molecule additives have presented their ability to regulate ionic solvation because they possess two charged groups covalently connected together. One remaining question is how the effect of zwitterionic molecules on ionic solvation depends on their own chemical structures, especially the anionic groups. To shed light on this question, we investigate the ionic solvation structure and dynamics in LiTFSI/(ethylene oxide)10 (EO10) with the presence of three distinct zwitterionic molecules (MPC, SB, and CB) using molecular dynamics simulations (MPC: 2-methacryloyloxyethyl phosphorylcholine, SB: sulfobetaine ethylimidazole, CB: carboxybetaine ethylimidazole, and LiTFSI: lithium bis(trifluoromethylsulfonyl)-imide). The simulation systems include two Li+ : O(EO10) molar ratios: 1 : 6 and 1 : 18. The simulation results show that all three zwitterionic molecules reduce the Li+-EO10 coordination number in the order of MPC > CB > SB. In addition, nearly 10% of Li+ exclusively coordinates with MPC molecules, only 2-4% of Li+ exclusively cooridinates with CB molecules, while no Li+ exclusively coordinates with SB molecules. MPC molecules also present the most stable Li+ coordination among the three zwitterionic molecules. Our simulations indicate that zwitterionic molecule additives may benefit a high Li+ concentration environment. At a low Li+ concentration, all three zwitterionic molecules reduce the diffusion coefficient of Li+. However, at a high Li+ concentration, only SB molecules reduce the diffusion coefficient of Li+.

Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns.

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure.

Large protein language models (PLMs) present excellent potential to reshape protein research by encoding the amino acid sequences into mathematical and biological meaningful embeddings. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily depending on 3D structures. To address this issue, we introduce S-PLM, a 3D structure-aware PLM utilizing multi-view contrastive learning to align the sequence and 3D structure of a protein in a coordinate space. S-PLM applies Swin-Transformer on AlphaFold-predicted protein structures to embed the structural information and fuses it into sequence-based embedding from ESM2. Additionally, we provide a library of lightweight tuning tools to adapt S-PLM for diverse protein property prediction tasks. Our results demonstrate S-PLM's superior performance over sequence-only PLMs, achieving competitiveness in protein function prediction compared to state-of-the-art methods employing both sequence and structure inputs.