In a screen designed to identify novel inducers of autophagy, we discovered that STAT3 inhibitors potently stimulate the autophagic flux. Accordingly, genetic inhibition of STAT3 stimulated autophagy in vitro and in vivo, while overexpression of STAT3 variants, encompassing wild-type, nonphosphorylatable, and extranuclear STAT3, inhibited starvation-induced autophagy. The SH2 domain of STAT3 was found to interact with the catalytic domain of the eIF2α kinase 2 EIF2AK2, best known as protein kinase R (PKR). Pharmacological and genetic inhibition of STAT3 stimulated the activating phosphorylation of PKR and consequent eIF2α hyperphosphorylation. Moreover, PKR depletion inhibited autophagy as initiated by chemical STAT3 inhibitors or free fatty acids like palmitate. STAT3-targeting chemicals and palmitate caused the disruption of inhibitory STAT3-PKR interactions, followed by PKR-dependent eIF2α phosphorylation, which facilitates autophagy induction. These results unravel an unsuspected mechanism of autophagy control that involves STAT3 and PKR as interacting partners.

Machine learning encompasses tools and algorithms that are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.

Two factors provide key contributions to the stability of thermophilic proteins relative to their mesophilic homologues: electrostatic interactions of charged residues in the folded state and the dielectric response of the folded protein. The dielectric response for proteins in a "thermophilic series" globally modulates the thermal stability of its members, with the calculated dielectric constant for the protein increasing from mesophiles to hyperthermophiles. This variability results from differences in the distribution of charged residues on the surface of the protein, in agreement with structural and genetic observations. Furthermore, the contribution of electrostatic interactions to the stability of the folded state is more favorable for thermophilic proteins than for their mesophilic homologues. This leads to the conclusion that electrostatic interactions play an important role in determining the stability of proteins at high temperatures. The interplay between electrostatic interactions and dielectric response also provides further rationalization for the enhanced stability of thermophilic proteins with respect to cold-denaturation. Taken together, the distribution of charged residues and their fluctuations have been shown to be factors in modulating protein stability over the entire range of biologically relevant temperatures.

To address the question of whether established or experimental anticancer chemotherapeutics can exert their cytotoxic effects by autophagy, we performed a highcontent screen on a set of cytotoxic agents. We simultaneously determined parameters of autophagy, apoptosis and necrosis on cells exposed to B1400 compounds. Many agents induced a 'pure' autophagic, apoptotic or necrotic phenotype, whereas less than 100 simultaneously induced autophagy, apoptosis and necrosis. A systematic analysis of the autophagic flux induced by the most potent 80 inducers of GFP-LC3 puncta among the NCI panel agents showed that 59 among them truly induced autophagy. The remaining 21 compounds were potent inducers of apoptosis or necrosis, yet failed to stimulate an autophagic flux, which were characterized as microtubule inhibitors. Knockdown of ATG7 was efficient in preventing GFP-LC3 puncta, yet failed to attenuate cell death by the agents that induce GFP-LC3 puncta. Thus there is not a single compound that would induce cell death by autophagy in our screening, underscoring the idea that cell death is rarely, if ever, executed by autophagy in human cells.

One of the major applications of generative models for drug discovery targets the lead-optimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de novo drug design. To tackle this issue, we introduce a new algorithm, named SAMOA (Scaffold Constrained Molecular Generation), to perform scaffold-constrained in silico molecular design. We build on the well-known SMILES-based Recurrent Neural Network (RNN) generative model, with a modified sampling procedure to achieve scaffold-constrained generation. We directly benefit from the associated reinforcement learning methods, allowing to design molecules optimized for different properties while exploring only the relevant chemical space. We showcase the method's ability to perform scaffold-constrained generation on various tasks: designing novel molecules around scaffolds extracted from SureChEMBL chemical series, generating novel active molecules on the Dopamine Receptor D2 (DRD2) target, and finally, designing predicted actives on the MMP-12 series, an industrial lead-optimization project.

A novel class of heat shock protein 90 (Hsp90) inhibitors was developed after a low throughput screen (LTS) of a focused library containing approximately 21K compounds selected by virtual screening. The initial [1-{3-H-imidazo[4-5-c]pyridin-2-yl}-3,4-dihydro-2H-pyrido[2,1-a]isoindole-6-one] (1) compound showed moderate activity (IC(50) = 7.6 μM on Hsp82, the yeast homologue of Hsp90). A high-resolution X-ray structure shows that compound 1 binds into an "induced" hydrophobic pocket, 10-15 Å away from the ATP/resorcinol binding site. Iterative cycles of structure-based drug design (SBDD) and chemical synthesis led to the design and preparation of analogues with improved affinity. These optimized molecules make productive interactions within the ATP binding site as reported by other Hsp90 inhibitors. This resulted in compound 8, which is a highly potent inhibitor in biochemical and cellular assays (K(d) = 0.35 nM on Hsp90; IC(50) = 30 nM on SKBr3 mammary carcinoma cells) and in an in vivo leukemia model.

The Aurora family of serine/threonine kinases is essential for mitosis. Their crucial role in cell cycle regulation and aberrant expression in a broad range of malignancies have been demonstrated and have prompted intensive search for small molecule Aurora inhibitors. Indeed, over 10 of them have reached the clinic as potential anticancer therapies. We report herein the discovery and optimization of a novel series of tricyclic molecules that has led to SAR156497, an exquisitely selective Aurora A, B, and C inhibitor with in vitro and in vivo efficacy. We also provide insights into its mode of binding to its target proteins, which could explain its selectivity.

Phosphoinositide 3-kinases (PI3Ks) are involved in important cellular functions and represent desirable targets for drug discovery efforts, especially related to oncology; however, the four PI3K subtypes (α, β, γ, and δ) have highly similar binding sites, making the design of selective inhibitors challenging. A series of inhibitors with selectivity toward the β subtype over δ resulted in compound 3(S), which has entered a phase I/Ib clinical trial for patients with advanced PTEN-deficient cancer. Interestingly, X-ray crystallography revealed that the modifications making inhibitor 3(S) and related compounds selective toward the β-isoform do not interact directly with either PI3Kβ or PI3Kδ, thereby confounding rationalization of the SAR. Here, we apply explicit solvent molecular dynamics and solvent thermodynamic analysis using WaterMap in an effort to understand the unusual affinity and selectivity trends. We find that differences in solvent energetics and water networks, which are modulated upon binding of different ligands, explain the experimental affinity and selectivity trends. This study highlights the critical role of water molecules in molecular recognition and the importance of considering water networks in drug discovery efforts to rationalize and improve selectivity.