Evaluating the use of absolute binding free energy in the fragment optimization process.

Key to the fragment optimization process is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimization decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman’s r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimization decisions can be supported by the ABFE calculations. Cases that were not supported were often limited by large uncertainties in the free energy estimates, however generally the right direction in ΔΔG is still observed. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better outcomes in ranking binders, improving correlation metrics, although a similar confidence in retrospective synthetic decisions is achieved. Our results indicate that ABFE calculations are currently at the level of accuracy that can be usefully employed to gauge which fragment elaborations are likely to offer the best gains in affinity.

In Silico Studies on Psilocybin Drug Derivatives Against SARS-CoV-2 and Cytokine Storm of Human Interleukin-6 Receptor

Various metabolites identified with therapeutic mushrooms have been found from different sources and are known to have antibacterial, antiviral, and anticancer properties. Over thousands soil growth-based mushroom metabolites have been discovered, and utilized worldwide to combat malignancy. In this study, psilocybin-mushroom that contains the psychedelic compounds such as psilacetin, psilocin, and psilocybine were screened and found to be inhibitors of SARS-CoV-2 Mprotease. It has been found that psilacetin, psilocin, and psilocybine bind to Mprotease with −6.0, −5.4, and −5.8 kcal/mol, respectively. Additionally, the psilacetin was found to inhibit human interleukin-6 receptors to reduce cytokine storm. The binding of psilacetin to Mprotease of SARS-CoV-2 and human interleukin-6 receptors changes the structural dynamics and Gibbs free energy patterns of proteins. These results suggested that psilocybin-mushroom could be utilized as viable potential chemotherapeutic agents for SARS-CoV-2.

Characterizing the conformational free-energy landscape of RNA using single-molecule field-effect transistors

We have developed and used high-time-resolution, single-molecule field-effect transistors (smFETs) to characterize the con-formational free-energy landscape of RNA stem-loops. Stem-loops are some of the most common RNA structural motifs and serve as building blocks for the formation of more complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been recently in-terpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or on the application of mechanical (un)folding forces, they provide a unique and complementary approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 μs time resolution. Our results show that under our experimental conditions, stem-loops fold and unfold over a 1-200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1-200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which relatively extended unfolded con-formations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several mis-folded conformations or, alternatively, the natively folded conformation. Our findings demonstrate how the combination of single-molecule sensitivity and high time resolution makes smFETs unique and powerful tools for characterizing the con-formational free-energy landscape of RNA and highlight the extremely rugged landscape on which even the simplest RNA structural elements fold.