Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers

The practically infinite chemical and morphological space of polymers makes them pervasive with applications in materials science but challenges the rational discovery of new materials with favorable properties. Polymer informatics aims to accelerate materials design through property prediction and large-scale virtual screening. In this study, a new method (Lieconv-Tg) has been developed to predict glass-transition temperature (Tg) values from repeating units of polymers based on Lieconv, which is equivariant with transformations from any specified Lie group. The introduction of equivariance allows the prediction of molecular properties from their 3D structures, independent of orientation and position. A total of 27,659 homopolymers with Tg values were collected from PolyInfo, and a standard data set containing 7166 polymers (named data set_Tg) was created for training a robust Lieconv-Tg model. Using the 3D coordinates as input, Lieconv-Tg performs better than Edge-Conditioned Convolution (ECC), and the mean absolute error (MAE) is significantly reduced by ∼6 from ∼30 to ∼24 on both the validation set and the test set, and the R2 value for both the validation set and the test set can reach 0.90. Lieconv-Tg is thus used to screen promising candidates from a benchmark database named PI1M with 995,800 generated polymers. However, there are some implausible repeating units in PI1M. To get more reasonable candidates from PI1M, a new filtering method has been accomplished by utilizing Morgan fingerprints at the polymerization points (MF@PP) of repeating units in data set_Tg. The combination of a standard data set, Lieconv-Tg, and a more reasonable screening strategy provides new directions in materials design for polymers

Lewis Acidic Metal–Organic Framework Assisted Ambient Liquid Extraction Mass Spectrometry Imaging for Enhancing the Coverage of Poorly Ionizable Lipids in Brain Tissue

The spatial distribution of lipidomes in tissues is of great importance in studies of living processes, diseases, and therapies. Mass spectrometry imaging (MSI) has become a critical technique for spatial lipidomics. However, MSI of low-abundance or poorly ionizable lipids is still challenging because of the ion suppression from high-abundance lipids. Here, a metal–organic framework (MOF) Zr6O4(OH)4(1,3,5-Tris(4-carboxyphenyl) benzene)2(triflate)6(Zr6OTf-BTB) was prepared and used for selective on-tissue adsorption of phospholipids to reduce ion suppression from them to poorly ionizable lipids. The results show that Zr6OTf-BTB with strong Lewis acidic sites and a large specific surface area (647.9 m2·g–1) could selectively adsorb phospholipids under 1% FA-MeOH. Adsorption efficiencies of phospholipids are 88.4–144.9 times higher than those of other neutral lipids. Moreover, the adsorption capacity and the adsorption kinetic rate constant of the new material to phospholipids are higher than those of Zr6-BTB (242.72 vs 73.96 mg·g–1, 0.0442 vs 0.0220 g·mg–1·min–1). A Zr6OTf-BTB sheet was prepared by a lamination technique for on-tissue phospholipid adsorption from brain tissue. Then, the tissue section on the Zr6OTf-BTB sheet was directly imaged via ambient liquid extraction-MSI with 1% FA-MeOH as the sampling solvent. The results showed that phospholipids could be 100% removed directly on tissue, and the detection coverage of the Zr6OTf-BTB-enhanced MSI method to ceramides (Cers) and hexosylceramides (HexCers) was increased by 5–26 times compared with direct tissue MSI (26 vs 1 and 17 vs 3). The new method provides an efficient and convenient way to eliminate the ion suppression from phospholipids in MSI, largely improving the detection coverage of low-abundance and poorly ionizable lipids

Cd²⁺ as a Ca²⁺ Surrogate in Protein–Membrane Interactions: Isostructural but Not Isofunctional

Due to its favorable spectroscopic properties, Cd²⁺ is frequently used as a probe of Ca²⁺ sites in proteins. We investigate the ability of Cd²⁺ to act as a structural and functional surrogate of Ca²⁺ in protein–membrane interactions. C2 domain from protein kinase Cα (C2α) was chosen as a paradigm for the Ca²⁺-dependent phosphatidylserine-binding peripheral membrane domains. We identified the Cd²⁺-binding sites of C2α using NMR spectroscopy, determined the 1.6 Å crystal structure of Cd²⁺-bound C2α, and characterized metal-ion-dependent interactions between C2α and phospholipid membranes using fluorescence spectroscopy and ultracentrifugation experiments. We show that Cd²⁺ forms a tight complex with the membrane-binding loops of C2α but is unable to support its membrane-binding function. This is in sharp contrast with Pb²⁺, which is almost as effective as Ca²⁺ in driving the C2α-membrane association process. Our results provide the first direct evidence for the specific role of divalent metal ions in mediating protein–membrane interactions, have important implications for metal substitution studies in proteins, and illustrate the potential diversity of functional responses caused by toxic metal ions