Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q² = 0.778, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.709)) and CoMSIA (Comparative Molecular Similarity Index Analysis (q² = 0.764, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.713)) yielded good statistical and predictive properties. Based on the corresponding contour maps, seven novel tetrahydroquinoline derivatives were designed. For more information, three of the compounds (D1, D4, and Z17) and the template molecule 18x were explored with molecular dynamics simulations, binding free energy calculations by MM/PBSA method as well as the ADME (absorption, distribution, metabolism, and excretion) prediction. The results suggested that D1, D4, and Z17 performed better than template molecule 18x due to the introduction of the amino and hydrophobic groups, especially for the D1 and D4, which will provide guidance for the design of LSD1 inhibitors.

Free energy calculations play a crucial role in simulating chemical processes, enzymatic reactions, and drug design. However, assessing the reliability and convergence of these calculations remains a challenge. This study focuses on single-step free-energy calculations using thermodynamic perturbation. It explores how the sample distributions influence the estimated results and evaluates the reliability of various convergence criteria, including Kofke's bias measure Π and the standard deviation of the energy difference ΔU, σ_ΔU. The findings reveal that for Gaussian distributions, there is a straightforward relationship between Π and σ_ΔU, free energies can be accurately approximated using a second-order cumulant expansion, and reliable results are attainable for σ_ΔU up to 25 kcal mol⁻¹. However, interpreting non-Gaussian distributions is more complex. If the distribution is skewed towards more positive values than a Gaussian, converging the free energy becomes easier, rendering standard convergence criteria overly stringent. Conversely, distributions that are skewed towards more negative values than a Gaussian present greater challenges in achieving convergence, making standard criteria unreliable. We propose a practical approach to assess the convergence of estimated free energies.

The stimulator of interferon genes (STING) plays a significant role in immune defense and protection against tumor proliferation. Many cyclic dinucleotide (CDN) analogues have been reported to regulate its activity, but the dynamic process involved when the ligands activate STING remains unclear. In this work, all-atom molecular dynamics simulations were performed to explore the binding mode between human STING (hSTING) and four cyclic adenosine–inosine monophosphate analogs (cAIMPs), as well as 2′,3′-cGMP-AMP (2′,3′-cGAMP). The results indicate that these cAIMPs adopt a U-shaped configuration within the binding pocket, forming extensive non-covalent interaction networks with hSTING. These interactions play a significant role in augmenting the binding, particularly in interactions with Tyr167, Arg238, Thr263, and Thr267. Additionally, the presence of hydrophobic interactions between the ligand and the receptor further contributes to the overall stability of the binding. In this work, the conformational changes in hSTING upon binding these cAIMPs were also studied and a significant tendency for hSTING to shift from open to closed state was observed after binding some of the cAIMP ligands.

Alchemical free-energy perturbation (FEP) is an accurate and thermodynamically stringent way to estimate relative energies for the binding of small ligands to biological macromolecules. It has repeatedly been pointed out that a single simulation normally stays near the starting point in phase space and therefore underestimates the uncertainty of the results. Therefore, it is better to run an ensemble of independent simulations. Traditionally, such an ensemble has been generated by using different starting velocities. We argue that it is better to use also other random choices made during the setup of the simulations, in particular the solvation of the solute. We show here that such solvent-induced independent simulations (SIS) sometimes give a larger standard deviation and slightly different results for the binding of 42 ligands to five different proteins, viz. human N-terminal bromodomain 4, the Leu99Ala mutant of T4 lysozyme, dihydrofolate reductase, blood-clotting factor Xa, and ferritin. SIS does not involve any increase in the time consumption. Therefore, we strongly recommend the use of SIS (in addition to different velocities) to start independent simulations. Other random or uncertain choices in the setup of the simulated systems, e.g., the selection of residues with alternative conformations or positions of added protons, may also be used to enhance the variation in independent simulations.