Co-Mutations and Possible Variation Tendency of the Spike RBD and Membrane Protein in SARS-CoV-2 by Machine Learning.

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2.

Screening of CPT1A-Targeting Lipid Metabolism Modulators Using Mitochondrial Membrane Chromatography.

Carnitine palmitoyltransferase 1A (CPT1A), which resides on the mitochondrial outer membrane, serves as the rate-limiting enzyme of fatty acid ß-oxidation. Identifying the compounds targeting CPT1A warrants a promising candidate for modulating lipid metabolism. In this study, we developed a CPT1A-overexpressed mitochondrial membrane chromatography (MMC) to screen the compounds with affinity for CPT1A. Cells overexpressing CPT1A were cultured, and subsequently, their mitochondrial membrane was isolated and immobilized on amino-silica gel cross-linked by glutaraldehyde. After packing the mitochondrial membrane column, retention components of MMC were performed with LC/MS, whose analytic peaks provided structural information on compounds that might interact with mitochondrial membrane proteins. With the newly developed MMC-LC/MS approach, several Chinese traditional medicine extracts, such as Scutellariae Radix and Polygoni Cuspidati Rhizoma et Radix (PCRR), were analyzed. Five noteworthy compounds, baicalin, baicalein, wogonoside, wogonin, and resveratrol, were identified as enhancers of CPT1A enzyme activity, with resveratrol being a new agonist for CPT1A. The study suggests that MMC serves as a reliable screening system for efficiently identifying modulators targeting CPT1A from complex extracts.

MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction.

Identifying task-relevant structures is important for molecular property prediction. In a graph neural network (GNN), graph pooling can group nodes and hierarchically represent the molecular graph. However, previous pooling methods either drop out node information or lose the connection of the original graph; therefore, it is difficult to identify continuous subtructures. Importantly, they lacked interpretability on molecular graphs. To this end, we proposed a novel Molecular Edge Shrinkage Pooling (MESPool) method, which is based on edges (or chemical bonds). MESPool preserves crucial edges and shrinks others inside the functional groups and is able to search for key structures without breaking the original connection. We compared MESPool with various well-known pooling methods on different benchmarks and showed that MESPool outperforms the previous methods. Furthermore, we explained the rationality of MESPool on some datasets, including a COVID-19 drug dataset.