Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Endogenous estrogen and progesterone and their metabolites play a key role in regulating and maintaining the normal pregnancy process. However, the dynamic change of these estrogen and progesterone metabolites’ level across the entire gestational period is not fully revealed. This study systematically measures the temporal changes of estrogen, progesterone, and their metabolites in human urine during normal pregnancy using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). We find that the levels of estrone, estradiol, estriol, 16-epiestriol, 17-epiestriol, 2-methoxyestradiol, and 4-hydroxyestrone gradually increase during pregnancy. The levels of 2-hydroxyestrone, 2-hydroxyestradiol, and 4-hydroxyestradiol rapidly decrease in the early pregnancy and then maintain at lower levels. The levels of 4-methoxyestradiol, 4-methoxyestrone and 2-methoxyestrone peak in the mid-pregnancy and then gradually decrease. The levels of pregnenolone, 17α-hydroxy pregnenolone, 17α-hydroxy progesterone, pregnanolone, epipregnanolone gradually increase during pregnancy. The levels of progesterone, 20α-hydroxy progesterone, 5α-dihydroprogesterone and 5β-dihydroprogesterone first increase in the mid pregnancy and then decrease in the late pregnancy. In sum, this study comprehensively depicts the dynamic change of estrogen, progesterone, and their metabolites in human urine during pregnancy, which lays the foundation for hormone monitoring and early diagnosis of pregnancy complications, and further mechanistic study of the roles of these metabolites in pregnancy.

It has been known for many years that the peroxidase activity of cyclooxygenase 1 and 2 (COX-1 and COX-2) can be reactivated in vitro by the presence of phenol, which serves as a reducing compound, but the underlying mechanism is still poorly understood. In the present study, we use phenol as a model compound to investigate the mechanism by which the peroxidase activity of human COXs is reactivated after each catalytic cycle. Molecular docking and quantum mechanics calculations are carried out to probe the interaction of phenol with the peroxidase site of COXs and the reactivation mechanism. It is found that the oxygen atom associated with the Fe ion in the heme group (i.e., the complex of Fe ion and porphyrin) of COXs can be removed by addition of two protons. Following its removal, phenol can readily bind inside the peroxidase active sites of the COX enzymes, and directly interact with Fe in heme to facilitate electron transfer from phenol to heme. This investigation provides theoretical evidence for several intermediates formed in the COX peroxidase reactivation cycle, thereby unveiling mechanistic details that would aid in future rational design of drugs that target the peroxidase site.