Mimicking Intermolecular Interactions of Tight Protein–Protein Complexes for Small-Molecule Antagonists

Tight protein–protein interactions (Kd1000 Å2) are highly challenging to disrupt with small molecules. Historically, the design of small molecules to inhibit protein–protein interactions has focused on mimicking the position of interface protein ligand side chains. Here, we explore mimicry of the pairwise intermolecular interactions of the native protein ligand with residues of the protein receptor to enrich commercial libraries for small-molecule inhibitors of tight protein–protein interactions. We use the high-affinity interaction (Kd=1 nm) between the urokinase receptor (uPAR) and its ligand urokinase (uPA) to test our methods. We introduce three methods for rank-ordering small molecules docked to uPAR: 1) a new fingerprint approach that represents uPA′s pairwise interaction energies with uPAR residues; 2) a pharmacophore approach to identify small molecules that mimic the position of uPA interface residues; and 3) a combined fingerprint and pharmacophore approach. Our work led to small molecules with novel chemotypes that inhibited a tight uPAR⋅uPA protein–protein interaction with single-digit micromolar IC50 values. We also report the extensive work that identified several of the hits as either lacking stability, thiol reactive, or redox active. This work suggests that mimicking the binding profile of the native ligand and the position of interface residues can be an effective strategy to enrich commercial libraries for small-molecule inhibitors of tight protein–protein interactions

Digitalization, resource misallocation and low-carbon agricultural production: evidence from China

With the rapid development of digital technologies such as artificial intelligence, big data and cloud computing, China’s agricultural production is entering a new era characterized by digitalization. Based on provincial panel data of China from 2013 to 2020, this paper adopts the system GMM and mediating effects model to systematically examine the impact of digitalization on low-carbon agricultural production from the perspective of resource misallocation. The results indicate that digitalization can significantly curb agricultural carbon emissions and thus promote low-carbon agricultural production, and this finding still holds after the robustness test. The heterogeneity analysis indicates that the inhibiting effect of digitalization on agricultural carbon emissions is most pronounced in the eastern region relative to the central and western regions (the regression coefficients are −0.400 and −0.126 respectively). Further mechanism analysis suggests that digitalization can reduce agricultural carbon emissions by correcting the widespread capital and labor misallocation in agricultural factor markets. The findings of this study provide significant policy implications for low-carbon agricultural production in China