New sulphonamide-peptide hybrid molecules as potential PBP 2a ligands and methicillin resistant Staphylococcus aureus actives

Penicillin binding protein 2a (PbP 2a) expression accounts for the insusceptibility of methicillin-resistant Staphylocuccus aureus (MRSA) to β-lactam antibiotics. Here we employed computational strategies to challenge PbP 2a with series of fifty-five 'ala-ala' and 'ala-pro' sulphonamide-dipeptides. Binding stability of two compounds (labeled: 10i and 10n) with theoretical K_i in nM and µM ranges, for PbP 2a active and allosteric sites respectively, were investigated using molecular dynamics simulations. In addition, the results of the sensitivity of four strains of MRSA for compounds 10i and 10n obtained revealed the compounds at 10 µg/ml caused two isolates (S4 and S10) to revert to being susceptible. Finally, a reliable binding conformations of both compounds in the two binding sites of PbP 2a are described to provide rationale for structure-activity optimization of this series.Communicated by Ramaswamy H. Sarma.

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.

Covalent Docking in Drug Discovery: Scope and Limitations

Drug discovery efforts for new covalent inhibitors have drastically increased in the last few years. The binding mechanism of covalent compounds entails the formation of a chemical bond between their electrophilic warhead group and the protein of interest. The use of moderately reactive warheads targeting nonconserved nucleophilic residues can improve the affinity and selectivity profiles of covalent binders as compared to their non-covalent analogs. Recent advances have also enabled their use as chemical probes to disclose novel and also less tractable targets. Increasing interest in covalent drug discovery prompted the development of new computational tools, including covalent docking methods, that are available to predict the binding mode and affinity of covalent ligands. These tools integrate conventional non-covalent docking and scoring schemes by modeling the newly formed covalent bond and the interactions occurring at the reaction site. In this review, we provide a thorough analysis of state-of-the-art covalent docking programs by highlighting their main features and current limitations. Focusing on the implemented algorithms, we show the differences in handling the formation of the new covalent bond and their relative impact on the prediction. This analysis provides a comprehensive overview of the current technology and suggests future improvements in computer-aided covalent drug design. Finally, discussing successful retrospective and prospective covalent docking-based virtual screening applications, we intend to identify best practices for the drug discovery community.

Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4

For rational drug design, it is essential to predict the binding mode of protein-ligand complexes. Although various machine learning-based models have been reported that use convolutional neural networks (deep learning) to predict binding modes from three-dimensional structures, there are few detailed reports on how best to construct and use datasets. Here, we examined how different datasets affected the prediction of the binding mode of CYP3A4 by a three-dimensional neural network when the number of crystal structures for the target protein was limited. We used four different training datasets: one large, general dataset containing various protein complexes and three smaller, more specific datasets containing complexes with CYP3A4-like pockets, complexes with CYP3A4-binding ligands, and complexes with CYP protein family members. We then trained models with different combinations of datasets with or without subsequent fine-tuning and evaluated the binding mode prediction performance of each model. The best receiver operating characteristic (ROC) area under the curve (AUC) model with respect to area under the receiver operating characteristic curve was obtained by training with a combination of the general protein and CYP family datasets. However, the ROC AUC-recall balanced model was obtained by training with this combination of datasets followed by fine-tuning with the CYP3A4-binding ligands dataset. Our results suggest that datasets that balance protein functionality and data size are important for optimizing binding mode prediction performance. In addition, datasets with large median binding pocket sizes may be important for the binding mode prediction specifically of CYP3A4.

DOCK 6: combining techniques to model RNA-small molecule complexes

With an increasing interest in RNA therapeutics and for targeting RNA to treat disease, there is a need for the tools used in protein-based drug design, particularly DOCKing algorithms, to be extended or adapted for nucleic acids. Here, we have compiled a test set of RNA-ligand complexes to validate the ability of the DOCK suite of programs to successfully recreate experimentally determined binding poses. With the optimized parameters and a minimal scoring function, 70% of the test set with less than seven rotatable ligand bonds and 26% of the test set with less than 13 rotatable bonds can be successfully recreated within 2 A heavy-atom RMSD. When DOCKed conformations are rescored with the implicit solvent models AMBER generalized Born with solvent-accessible surface area (GB/SA) and Poisson-Boltzmann with solvent-accessible surface area (PB/SA) in combination with explicit water molecules and sodium counterions, the success rate increases to 80% with PB/SA for less than seven rotatable bonds and 58% with AMBER GB/SA and 47% with PB/SA for less than 13 rotatable bonds. These results indicate that DOCK can indeed be useful for structure-based drug design aimed at RNA. Our studies also suggest that RNA-directed ligands often differ from typical protein-ligand complexes in their electrostatic properties, but these differences can be accommodated through the choice of potential function. In addition, in the course of the study, we explore a variety of newly added DOCK functions, demonstrating the ease with which new functions can be added to address new scientific questions.