The rational discovery of multipurpose inhibitors of the ornithine decarboxylase.

Metabolic reprograming is a hallmark of cancer, and the polyamine metabolic network is dysregulated in many cancers. Ornithine decarboxylase (ODC) is a rate-limiting enzyme for polyamine synthesis in the polyamine metabolic network. In many cancer cells, ODC is over-expressed, so this enzyme has been an attracting anti-cancer drug target. In the catalysis axis (pathway), ODC converts ornithine to putrescine. Meanwhile, ODC's activity is regulated by protein-protein interactions (PPIs), including the ODC-OAZ1-AZIN1 PPI axis and its monomer-dimer equilibrium. Previous studies showed that when ODC's activity is inhibited, the PPIs might counteract the inhibition efficiency. Therefore, we proposed that multipurpose inhibitors that can simultaneously inhibit ODC's activity and perturb the PPIs would be very valuable as drug candidates and molecular tools. To discover multipurpose ODC inhibitors, we established a computational pipeline by combining positive screening and negative screening. We used this pipeline for the forward screening of multipurpose ligands that might inhibit ODC's activity, block ODC-OAZ1 interaction and enhance ODC non-functional dimerization. With a combination of different experimental assays, we identified three multipurpose ODC inhibitors. At last, we showed that one of these inhibitors is a promising drug candidate. This work demonstrated that our computational pipeline is useful for discovering multipurpose ODC inhibitors, and multipurpose inhibitors would be very valuable. Similar with ODC, there are a lot of proteins in human proteome that act as both enzymes and PPI components. Therefore, this work is not only presenting new molecular tools for polyamine study, but also providing potential insights and protocols for discovering multipurpose inhibitors to target more important protein targets.

Optimized multilateral crop trade patterns can effectively mitigate phosphorus imbalance among the involved countries.

Phosphorus imbalance for cropland can greatly influence environmental quality and productivity of agricultural systems. Resolving cropland phosphorus imbalance may be possible with more efficient multilateral crop trade within the involved trading countries; however, the driving mechanisms are unclear. This study calculates phosphorus budgets in China and five central Asian countries and proposes two optimal multilateral crop trade models to mitigate the phosphorus imbalance. Results show that the current trading pattern between China and Central Asia is causing a phosphorus imbalance intensification. Phosphorus surpluses in China and Uzbekistan are 41.7 and 8.9 kg/ha, while Kazakhstan, Kyrgyzstan, Tajikistan, and Turkmenistan exhibit phosphorus deficits with the negative value of -0.7, -1.2, -0.8, and -0.8 kg/ha, respectively. However, under the optimal multilateral crop trade patterns, phosphorus budget of China and Central Asia will become balanced. Phosphorus imbalance intensification for China is reduced to -2525 and -2472 kt under the single- and bilevel-objective-based crop trades. In Kyrgyzstan, it will drop 61.5 % and 50.0 % and change to 321 and 417 kt under the two optimal crop trades. Moreover, changes of phosphorus imbalance mitigations for other central Asian countries range from 11.9 % to 28.2 %. This provides a scientific basis when establishing policies for strengthening optimal multilateral crop trading across the world to promote global phosphorus management.

3D-quantitative structure-activity relationship and docking studies of coumarin derivatives as tissue kallikrein 7 inhibitors.

OBJECTIVES: Kallikrein 7 (KLK7) is a secreted serine protease that plays important roles in skin desquamation and tumour progression, which makes it an attracting drug target. To guide the design of KLK7 inhibitors, a series of coumarin-based inhibitors were used to perform 3D-quantitative structure-activity relationship analysis. METHODS: 3D conformations of 37 inhibitors were generated and used to construct CoMFA and CoMSIA models. Then a complex model between the inhibitors and KLK7 was built with molecular docking. KEY FINDINGS: With the training set, the CoMFA and CoMSIA models achieved q2 values of 0.521 and 0.498, and r2 values of 0.942 and 0.983, respectively. With the testing set, the predicted r2 values were 0.663 and 0.669, respectively, for CoMFA and CoMSIA. 3D contour maps from these two models identified steric and hydrophobic interactions as the most important molecular features of these inhibitors. Furthermore, molecular docking study was performed to understand the binding modes between these compounds and KLK7, in which the critical steric and hydrophobic interactions between the inhibitors and KLK7 were confirmed. CONCLUSIONS: Steric and hydrophobic interactions are critical in the efficient binding of KLK7 inhibitors. Our analysis would provide a meaningful guideline for the rational design of novel KLK7 inhibitors.