The accurate prediction of protein−ligand binding affinities is crucial for drug discovery. Alchemical free energy calculations have become a popular tool for this purpose. However, the accuracy and reliability of these methods can vary depending on the methodology. In this study, we evaluate the performance of a relative binding free energy protocol based on the alchemical transfer method (ATM), a novel approach based on a coordinate transformation that swaps the positions of two ligands. The results show that ATM matches the performance of more complex free energy perturbation (FEP) methods in terms of Pearson correlation but with marginally higher mean absolute errors. This study shows that the ATM method is competitive compared to more traditional methods in speed and accuracy and offers the advantage of being applicable with any potential energy function.

Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein “hotspots” in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase in the size of the potentially ‘druggable’ human proteome.

The lack of conformational sampling in virtual screening projects can lead to inefficient results because many of the potential drugs may not be able to bind to the target protein during the static docking simulations. Here, we performed ensemble docking for around 2000 United States Food and Drug Administration (FDA)-approved drugs with the RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a target. The representative protein structures were generated by clustering classical molecular dynamics trajectories, which were evolved using three solvent scenarios, namely, pure water, benzene/water and phenol/water mixtures. The introduction of dynamic effects in the theoretical model showed improvement in docking results in terms of the number of strong binders and binding sites in the protein. Some of the discovered pockets were found only for the cosolvent simulations, where the nonpolar probes induced local conformational changes in the protein that lead to the opening of transient pockets. In addition, the selection of the ligands based on a combination of the binding free energy and binding free energy gap between the best two poses for each ligand provided more suitable binders than the selection of ligands based solely on one of the criteria. The application of cosolvent molecular dynamics to enhance the sampling of the configurational space is expected to improve the efficacy of virtual screening campaigns of future drug discovery projects.

Signal transducer activator of transcription 3 (STAT3) is among the most investigated oncogenic transcription factors, as it is highly associated with cancer initiation, progression, metastasis, chemoresistance, and immune evasion. Evidences from both preclinical and clinical studies have demonstrated that STAT3 plays a critical role in several malignancies associated with poor prognosis such as glioblastoma and triple-negative breast cancer, and STAT3 inhibitors have shown efficacy in inhibiting cancer growth and metastasis. Constitutive activation of STAT3 by mutations occurs frequently in tumor cells and directly contributes to many malignant phenotypes. Unfortunately, detailed structural biology studies on STAT3 as well as target-based drug discovery efforts have been hampered by difficulties in the expression and purification of the full-length STAT3 and a lack of ligand-bound crystal structures. Considering these, molecular modeling and simulations offer an attractive strategy for the assessment of the “druggability” of STAT3 dimers and allow investigations of reported activating and inhibiting STAT3 mutants at the atomistic level of detail. In the present study, we focused on the effects exerted by reported STAT3 mutations on the protein structure, dynamics, DNA-binding, and dimerization, thus linking structure, dynamics, energetics, and the biological function. By employing atomistic molecular dynamics and umbrella-sampling simulations to a series of human STAT3 dimers, which comprised wild-type protein and four mutations, we explained the modulation of STAT3 activity by these mutations. Counter-intuitively, our results show that the D570K inhibitory mutation exerts its effect by enhancing rather than weakening STAT3–DNA interactions, which interfere with the DNA release by the protein dimer and thus inhibit STAT3 function as a transcription factor. We mapped the binding site and characterized the binding mode of a clinical candidate napabucasin/BBI-608 at STAT3, which resembles the effect of a D570K mutation. Our results contribute to understanding the activation/inhibition mechanism of STAT3, to explain the molecular mechanism of STAT3 inhibition by BBI-608. Alongside the characterization of the BBI-608 binding mode, we also discovered a novel binding site amenable to bind small-molecule ligands, which may pave the way to design novel STAT3 inhibitors and to suggest new strategies for pharmacological interventions to combat cancers associated with poor prognosis.