Bisphenol A (BPA) can interact with nuclear receptors and affect the normal function of nuclear receptors in very low doses, which causes BPA to be one of the most controversial endocrine disruptors. However, the detailed molecular mechanism about how BPA interferes the normal function of nuclear receptors is still undiscovered. Herein, molecular dynamics simulations were performed to explore the detailed interaction mechanism between BPA with three typical nuclear receptors, including hERα, hERRγ and hPPARγ. The simulation results and calculated binding free energies indicate that BPA can bind to these three nuclear receptors. The binding affinities of BPA were slightly lower than that of E2 to these three receptors. The simulation results proved that the binding process was mainly driven by direct hydrogen bond and hydrophobic interactions. In addition, structural analysis suggested that BPA could interact with these nuclear receptors by mimicking the action of natural hormone and keeping the nuclear receptors in active conformations. The present work provided the structural evidence to recognize BPA as an endocrine disruptor and would be important guidance for seeking safer substitutions of BPA.

Natural polyphenols are one of the most actively investigated categories of amyloid inhibitors, and resveratrol has recently been reported to inhibit and remodel the human islet amyloid polypeptide (hIAPP) oligomers and fibrils. However, the exact mechanism of its action is still unknown, especially for the full-length hIAPP1-37. To this end, we performed all-atom molecular dynamics simulations for hIAPP1-37 pentamer with and without resveratrol. The obtained results show that the binding of resveratrol is able to cause remarkable conformational changes of hIAPP1-37 pentamer, in terms of secondary structures, order degree, and morphology. By clustering analysis, two possible binding sites of resveratrol on the hIAPP1-37 pentamer were found, located at the grooves of the top and bottom surfaces of β-sheet layer, respectively. After the binding free energy calculation and residue energy decomposition, it can be concluded that the bottom site is the more possible one, and that the nonpolar interactions act as the driving force for the binding of hIAPP1-37 to resveratrol. In addition, Arg11 is the most important residue for the binding of resveratrol. The full understanding of inhibitory mechanism of resveratrol on the hIAPP1-37 oligomer, and the identification of its binding sites on this protein are helpful for the future design and discovery of new amyloid inhibitors.

B-RAF kinase is a clinically validated target implicated in melanoma and advanced renal cell carcinoma (RCC). PLX4720 and TAK-632 are promising inhibitors against B-RAF with different dissociation rate constants (k(off)), but the specific mechanism that determines the difference of their dissociation rates remains unclear. In order to understand the kinetically different behaviors of these two inhibitors, their unbinding pathways were explored by random acceleration and steered molecular dynamics simulations. The random acceleration molecular dynamics (RAMD) simulations show that PLX4720 dissociates along the ATP-channel, while TAK-632 dissociates along either the ATP-channel or the allosteric-channel. The steered molecular dynamics (SMD) simulations reveal that TAK-632 is more favorable to escape from the binding pocket through the ATP-channel rather than the allosteric-channel. The PMF calculations suggest that TAK-632 presents longer residence time, which is in qualitative agreement with the experimental k(off)(k(off) = 3.3 × 10(-2) s(-1) and ΔG(off) = -82.17 ± 0.29 kcal mol(-1) for PLX4720; k(off) = 1.9 × 10(-5) s(-1) and ΔG(off) = -39.73 ± 0.79 kcal mol(-1) for PLX4720). Furthermore, the binding free decomposition by MM/GBSA illustrates that the residues K36, E54, V57, L58, L120, I125, H127, G146 and D147 located around the allosteric binding pocket play important roles in determining the longer residence time of TAK-632 by forming stronger hydrogen bond and hydrophobic interactions. Our simulations provide valuable information to design selective B-RAF inhibitors with long residence time in the future.

The de novo drug design based on SMILES format is a typical sequence-processing problem. Previous methods based on recurrent neural network (RNN) exhibit limitation in capturing long-range dependency, resulting in a high invalid percentage in generated molecules. Recent studies have shown the potential of Transformer architecture to increase the capacity of handling sequence data. In this work, the encoder module in the Transformer is used to build a generative model. First, we train a Transformer-encoder-based generative model to learn the grammatical rules of known drug molecules and a predictive model to predict the activity of the molecules. Subsequently, transfer learning and reinforcement learning were used to fine-tune and optimize the generative model, respectively, to design new molecules with desirable activity. Compared with previous RNN-based methods, our method has improved the percentage of generating chemically valid molecules (from 95.6 to 98.2%), the structural diversity of the generated molecules, and the feasibility of molecular synthesis. The pipeline is validated by designing inhibitors against the human BRAF protein. Molecular docking and binding mode analysis showed that our method can generate small molecules with higher activity than those carrying ligands in the crystal structure and have similar interaction sites with these ligands, which can provide new ideas and suggestions for pharmaceutical chemists.