Blind Pose Prediction, Scoring, and Affinity Ranking of the CSAR 2014 Dataset

Cyclodextrin based bone regenerative inclusion complex for resveratrol in postmenopausal osteoporosis

Recent preclinical studies have shown that resveratrol (RSV), is a promising remedy for osteoporosis owing to its estrogenic, anti-inflammatory, and antioxidant properties. However, RSV has met limited success due to its poor oral bioavailability and inefficient systemic delivery. In this study, we prepared the inclusion complex of RSV with sulfo-butyl ether β-cyclodextrin (SBE-β-CD) to enhance the aqueous solubility of RSV. The in-silico docking studies and Physico-chemical characterization assays were performed to understand the interaction of RSV inside the SBE-β-CD cavity. The in vivo safety assessment of RSV-SBE-β-CD inclusion complex (R-CDIC) was performed in healthy Wistar rats. The efficacy of the inclusion complex against postmenopausal osteoporosis was further investigated in ovariectomized (OVX) rat model. The alteration in the bone micro-architectural structure was evaluated by microcomputed tomographic scanning, serum biochemical estimations, biomechanical strength and histopathological investigation. Administration of RSV-SBE-β-CD inclusion complex was found to be safe and significantly improved micro-architectural deterioration induced by estrogen withdrawal. Results of bone morphometry and biomechanics study further emboldened the efficacy claim of the RSV-SBE-β-CD complex. Thus, the present study demonstrated the efficacy of the RSV-SBE-β-CD inclusion complex for treating osteolytic degradation in osteoporosis.

Azetidinimines as a novel series of non-covalent broad-spectrum inhibitors of β-lactamases with submicromolar activities against carbapenemases KPC-2 (class A), NDM-1 (class B) and OXA-48 (class D)

The occurrence of resistances in Gram negative bacteria is steadily increasing to reach extremely worrying levels and one of the main causes of resistance is the massive spread of very efficient β-lactamases which render most β-lactam antibiotics useless. Herein, we report the development of a series of imino-analogues of β-lactams (namely azetidinimines) as efficient non-covalent inhibitors of β-lactamases. Despite the structural and mechanistic differences between serine-β-lactamases KPC-2 and OXA-48 and metallo-β-lactamase NDM-1, all three enzymes can be inhibited at a submicromolar level by compound 7dfm, which can also repotentiate imipenem against a resistant strain of Escherichia coli expressing NDM-1. We show that 7dfm can efficiently inhibit not only the three main clinically-relevant carbapenemases of Ambler classes A (KPC-2), B (NDM-1) and D (OXA-48) with Ki's below 0.3 μM, but also the cephalosporinase CMY-2 (class C, 86% inhibition at 10 μM). Our results pave the way for the development of a new structurally original family of non-covalent broad-spectrum inhibitors of β-lactamases.

tRNA methylation: An unexpected link to bacterial resistance and persistence to antibiotics and beyond

A major threat to public health is the resistance and persistence of Gram-negative bacteria to multiple drugs during antibiotic treatment. The resistance is due to the ability of these bacteria to block antibiotics from permeating into and accumulating inside the cell, while the persistence is due to the ability of these bacteria to enter into a nonreplicating state that shuts down major metabolic pathways but remains active in drug efflux. Resistance and persistence are permitted by the unique cell envelope structure of Gram-negative bacteria, which consists of both an outer and an inner membrane (OM and IM, respectively) that lay above and below the cell wall. Unexpectedly, recent work reveals that m¹ G37 methylation of tRNA, at the N¹ of guanosine at position 37 on the 3'-side of the tRNA anticodon, controls biosynthesis of both membranes and determines the integrity of cell envelope structure, thus providing a novel link to the development of bacterial resistance and persistence to antibiotics. The impact of m¹ G37-tRNA methylation on Gram-negative bacteria can reach further, by determining the ability of these bacteria to exit from the persistence state when the antibiotic treatment is removed. These conceptual advances raise the possibility that successful targeting of m¹ G37-tRNA methylation can provide new approaches for treating acute and chronic infections caused by Gram-negative bacteria. This article is categorized under: Translation > Translation Regulation RNA Processing > RNA Editing and Modification RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset

Using the D3R Grand Challenge 4 dataset containing Beta-secretase 1 (BACE) and Cathepsin S (CatS) inhibitors, we have evaluated the performance of our in-house docking workflow that involves in the first step the selection of the most suitable docking software for the system of interest based on structural and functional information available in public databases, followed by the docking of the dataset to predict the binding modes and ranking of ligands. The macrocyclic nature of the BACE ligands brought additional challenges, which were dealt with by a careful preparation of the three-dimensional input structures for ligands. This provided top-performing predictions for BACE, in contrast with CatS, where the predictions in the absence of guiding constraints provided poor results. These results highlight the importance of previous structural knowledge that is needed for correct predictions on some challenging targets. After the end of the challenge, we also carried out free energy calculations (i.e. in a non-blinded manner) for CatS using the pmx software and several force fields (AMBER, Charmm). Using knowledge-based starting pose construction allowed reaching remarkable accuracy for the CatS free energy estimates. Interestingly, we show that the use of a consensus result, by averaging the results from different force fields, increases the prediction accuracy.

mTOR Inhibition via Displacement of Phosphatidic Acid Induces Enhanced Cytotoxicity Specifically in Cancer Cells

The mTOR is a central regulator of cell growth and is highly activated in cancer cells to allow rapid tumor growth. The use of mTOR inhibitors as anticancer therapy has been approved for some types of tumors, albeit with modest results. We recently reported the synthesis of ICSN3250, a halitulin analogue with enhanced cytotoxicity. We report here that ICSN3250 is a specific mTOR inhibitor that operates through a mechanism distinct from those described for previous mTOR inhibitors. ICSN3250 competed with and displaced phosphatidic acid from the FRB domain in mTOR, thus preventing mTOR activation and leading to cytotoxicity. Docking and molecular dynamics simulations evidenced not only the high conformational plasticity of the FRB domain, but also the specific interactions of both ICSN3250 and phosphatidic acid with the FRB domain in mTOR. Furthermore, ICSN3250 toxicity was shown to act specifically in cancer cells, as noncancer cells showed up to 100-fold less sensitivity to ICSN3250, in contrast to other mTOR inhibitors that did not show selectivity. Thus, our results define ICSN3250 as a new class of mTOR inhibitors that specifically targets cancer cells.Significance: ICSN3250 defines a new class of mTORC1 inhibitors that displaces phosphatidic acid at the FRB domain of mTOR, inducing cell death specifically in cancer cells but not in noncancer cells. Cancer Res; 78(18); 5384-97. ©2018 AACR.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014

The Community Structure-Activity Resource (CSAR) benchmark exercise provides a unique opportunity for researchers to objectively evaluate the performance of protein-ligand docking methods. Patch-Surfer and PL-PatchSurfer, molecular surface-based methods for predicting binding ligands of proteins developed in our group, were tested on both CSAR 2013 and 2014 benchmark exercises in combination with an empirical scoring function-based method, AutoDock, while we only participated in CSAR 2013 using Patch-Surfer. The prediction results for Phase 1 task in CSAR 2013 showed that Patch-Surfer was able to rank all the four designed binding proteins within top ranks, outperforming AutoDock Vina. In Phase 2 of 2013, PL-PatchSurfer correctly selected the correct ligand pose for two target proteins. PL-PatchSurfer performed reasonably well in ranking ligands according to their binding affinity and in selecting near-native ligand poses in 2013 Phase 3 and 2014 Phase 1, respectively, although AutoDock Vina showed better performance. Lastly, in the 2014 Phase 2 exercise, the PL-PatchSurfer scores computed for ligands to target protein pairs correlated well with their pIC50 values, which was better or comparable to results by other participants. Overall, our methods showed fairly good performance in CSAR 2013 and 2014. Unique characteristics of the methods are discussed in comparison with AutoDock.