In Vitro and In Vivo Inhibition of the Mycobacterium tuberculosis Phosphopantetheinyl Transferase PptT by Amidinoureas.

A newly validated target for tuberculosis treatment is phosphopantetheinyl transferase, an essential enzyme that plays a critical role in the biosynthesis of cellular lipids and virulence factors in Mycobacterium tuberculosis. The structure-activity relationships of a recently disclosed inhibitor, amidinourea (AU) 8918 (1), were explored, focusing on the biochemical potency, determination of whole-cell on-target activity for active compounds, and profiling of selective active congeners. These studies show that the AU moiety in AU 8918 is largely optimized and that potency enhancements are obtained in analogues containing a para-substituted aromatic ring. Preliminary data reveal that while some analogues, including 1, have demonstrated cardiotoxicity (e.g., changes in cardiomyocyte beat rate, amplitude, and peak width) and inhibit Cav1.2 and Nav1.5 ion channels (although not hERG channels), inhibition of the ion channels is largely diminished for some of the para-substituted analogues, such as 5k (p-benzamide) and 5n (p-phenylsulfonamide).

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking.

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

Mechanisms for Benzene Dissociation through the Excited State of T4 Lysozyme L99A Mutant.

The atomic-level mechanisms that coordinate ligand release from protein pockets are only known for a handful of proteins. Here, we report results from accelerated molecular dynamics simulations for benzene dissociation from the buried cavity of the T4 lysozyme Leu99Ala mutant (L99A). In these simulations, benzene is released through a previously characterized, sparsely populated room-temperature excited state of the mutant, explaining the coincidence for experimentally measured benzene off rate and apo protein slow-timescale NMR relaxation rates between ground and excited states. The path observed for benzene egress is a multistep ligand migration from the buried cavity to ultimate release through an opening between the F/G-, H-, and I-helices and requires a number of cooperative multiresidue and secondary-structure rearrangements within the C-terminal domain of L99A. These rearrangements are identical to those observed along the ground state to excited state transitions characterized by molecular dynamic simulations run on the Anton supercomputer. Analyses of the molecular properties of the residues lining the egress path suggest that protein surface electrostatic potential may play a role in the release mechanism. Simulations of wild-type T4 lysozyme also reveal that benzene-egress-associated dynamics in the L99A mutant are potentially exaggerations of the substrate-processivity-related dynamics of the wild type.

The Role of Bridging Water and Hydrogen Bonding as Key Determinants of Noncovalent Protein-Carbohydrate Recognition.

Mechanisms of protein-carbohydrate recognition attract a lot of interest due to their roles in various cellular processes and metabolism disorders. We have performed a large-scale analysis of protein structures solved in complex with glucose, galactose and their substituted analogues. We found that, on average, sugar molecules establish five hydrogen bonds (HBs) in the binding site, including one to three HBs with bridging water molecules. The free energy contribution of bridging and direct HBs was estimated using the free energy perturbation (FEP+) methodology for mono- and disaccharides that bind to l-ABP, ttGBP, TrmB, hGalectin-1 and hGalectin-3. We show that removing hydroxy groups that are engaged in direct HBs with the charged groups of Asp, Arg and Glu residues, protein backbone amide or buried water dramatically decreases binding affinity. In contrast, all solvent-exposed hydroxy groups and hydroxy groups engaged in HBs with the solvent-exposed bridging water molecules contribute weakly to binding affinity and so can be replaced to optimize ligand potency. Finally, we rationalize an effect of binding site water replacement on the binding affinity to l-ABP.

D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank ( http://www.pdb.org ), and in affinity ranking and scoring of bound ligands.

Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A.

Proteins commonly sample a number of conformational states to carry out their biological function, often requiring transitions from the ground state to higher-energy states. Characterizing the mechanisms that guide these transitions at the atomic level promises to impact our understanding of functional protein dynamics and energy landscapes. The leucine-99-to-alanine (L99A) mutant of T4 lysozyme is a model system that has an experimentally well characterized excited sparsely populated state as well as a ground state. Despite the exhaustive study of L99A protein dynamics, the conformational changes that permit transitioning to the experimentally detected excited state (∼3%, ΔG ∼2 kcal/mol) remain unclear. Here, we describe the transitions from the ground state to this sparsely populated excited state of L99A as observed through a single molecular dynamics (MD) trajectory on the Anton supercomputer. Aside from detailing the ground-to-excited-state transition, the trajectory samples multiple metastates and an intermediate state en route to the excited state. Dynamic motions between these states enable cavity surface openings large enough to admit benzene on timescales congruent with known rates for benzene binding. Thus, these fluctuations between rare protein states provide an atomic description of the concerted motions that illuminate potential path(s) for ligand binding. These results reveal, to our knowledge, a new level of complexity in the dynamics of buried cavities and their role in creating mobile defects that affect protein dynamics and ligand binding.

D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

Outcome of the First wwPDB/CCDC/D3R Ligand Validation Workshop.

Crystallographic studies of ligands bound to biological macromolecules (proteins and nucleic acids) represent an important source of information concerning drug-target interactions, providing atomic level insights into the physical chemistry of complex formation between macromolecules and ligands. Of the more than 115,000 entries extant in the Protein Data Bank (PDB) archive, ∼75% include at least one non-polymeric ligand. Ligand geometrical and stereochemical quality, the suitability of ligand models for in silico drug discovery and design, and the goodness-of-fit of ligand models to electron-density maps vary widely across the archive. We describe the proceedings and conclusions from the first Worldwide PDB/Cambridge Crystallographic Data Center/Drug Design Data Resource (wwPDB/CCDC/D3R) Ligand Validation Workshop held at the Research Collaboratory for Structural Bioinformatics at Rutgers University on July 30-31, 2015. Experts in protein crystallography from academe and industry came together with non-profit and for-profit software providers for crystallography and with experts in computational chemistry and data archiving to discuss and make recommendations on best practices, as framed by a series of questions central to structural studies of macromolecule-ligand complexes. What data concerning bound ligands should be archived in the PDB? How should the ligands be best represented? How should structural models of macromolecule-ligand complexes be validated? What supplementary information should accompany publications of structural studies of biological macromolecules? Consensus recommendations on best practices developed in response to each of these questions are provided, together with some details regarding implementation. Important issues addressed but not resolved at the workshop are also enumerated.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation.

A Gaussian accelerated molecular dynamics (GaMD) approach for simultaneous enhanced sampling and free energy calculation of biomolecules is presented. By constructing a boost potential that follows Gaussian distribution, accurate reweighting of the GaMD simulations is achieved using cumulant expansion to the second order. Here, GaMD is demonstrated on three biomolecular model systems: alanine dipeptide, chignolin folding, and ligand binding to the T4-lysozyme. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of these biomolecules. Furthermore, the free energy profiles obtained from reweighting of the GaMD simulations allow us to identify distinct low-energy states of the biomolecules and characterize the protein-folding and ligand-binding pathways quantitatively.

Molecular docking to flexible targets.

It is widely accepted that protein receptors exist as an ensemble of conformations in solution. How best to incorporate receptor flexibility into virtual screening protocols used for drug discovery remains a significant challenge. Here, stepwise methodologies are described to generate and select relevant protein conformations for virtual screening in the context of the relaxed complex scheme (RCS), to design small molecule libraries for docking, and to perform statistical analyses on the virtual screening results. Methods include equidistant spacing, RMSD-based clustering, and QR factorization protocols for ensemble generation and ROC analysis for ensemble selection.

Computational approaches to mapping allosteric pathways.

Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.

Computation-guided discovery of influenza endonuclease inhibitors.

Influenza is a global human health threat, and there is an immediate need for new antiviral therapies to circumvent the limitations of vaccination and current small molecule therapies. During viral transcription, influenza incorporates the 5'-end of the host cell's mRNA in a process that requires the influenza endonuclease. Based on recently published endonuclease crystalized structures, a three-dimensional pharmacophore was developed and used to virtually screen 450,000 compounds for influenza endonuclease inhibitors. Of 264 compounds tested in a FRET-based endonuclease-inhibition assay, 16 inhibitors (IC50 <50 µM) that span 5 molecular classes novel to this endonuclease were found (6.1% hit rate). To determine cytotoxicity and antiviral activity, subsequent cellular assays were performed. Two compounds suppress viral replication with negligible cell toxicity.

A 3-dimensional trimeric ß-barrel model for Chlamydia MOMP contains conserved and novel elements of Gram-negative bacterial porins.

Chlamydia trachomatis is the most prevalent cause of bacterial sexually transmitted diseases and the leading cause of preventable blindness worldwide. Global control of Chlamydia will best be achieved with a vaccine, a primary target for which is the major outer membrane protein, MOMP, which comprises ~60% of the outer membrane protein mass of this bacterium. In the absence of experimental structural information on MOMP, three previously published topology models presumed a16-stranded barrel architecture. Here, we use the latest ß-barrel prediction algorithms, previous 2D topology modeling results, and comparative modeling methodology to build a 3D model based on the 16-stranded, trimeric assumption. We find that while a 3D MOMP model captures many structural hallmarks of a trimeric 16-stranded ß-barrel porin, and is consistent with most of the experimental evidence for MOMP, MOMP residues 320-334 cannot be modeled as ß-strands that span the entire membrane, as is consistently observed in published 16-stranded ß-barrel crystal structures. Given the ambiguous results for ß-strand delineation found in this study, recent publications of membrane ß-barrel structures breaking with the canonical rule for an even number of ß-strands, findings of ß-barrels with strand-exchanged oligomeric conformations, and alternate folds dependent upon the lifecycle of the bacterium, we suggest that although the MOMP porin structure incorporates canonical 16-stranded conformations, it may have novel oligomeric or dynamic structural changes accounting for the discrepancies observed.

Structural characterisation of Tpx from Yersinia pseudotuberculosis reveals insights into the binding of salicylidene acylhydrazide compounds.

Thiol peroxidase, Tpx, has been shown to be a target protein of the salicylidene acylhydrazide class of antivirulence compounds. In this study we present the crystal structures of Tpx from Y. pseudotuberculosis (ypTpx) in the oxidised and reduced states, together with the structure of the C61S mutant. The structures solved are consistent with previously solved atypical 2-Cys thiol peroxidases, including that for "forced" reduced states using the C61S mutant. In addition, by investigating the solution structure of ypTpx using small angle X-ray scattering (SAXS), we have confirmed that reduced state ypTpx in solution is a homodimer. The solution structure also reveals flexibility around the dimer interface. Notably, the conformational changes observed between the redox states at the catalytic triad and at the dimer interface have implications for substrate and inhibitor binding. The structural data were used to model the binding of two salicylidene acylhydrazide compounds to the oxidised structure of ypTpx. Overall, the study provides insights into the binding of the salicylidene acylhydrazides to ypTpx, aiding our long-term strategy to understand the mode of action of this class of compounds.

Structure-based design and synthesis of pyrrole derivatives as MEK inhibitors.

A novel series of pyrrole inhibitors of MEK kinase has been developed using structure-based drug design. Optimization of the series led to the identification of potent inhibitors with good pharmaceutical properties.

Structure-based design, synthesis, and antimicrobial activity of purine derived SAH/MTA nucleosidase inhibitors.

The structure-based design, synthesis, and biological activity of novel inhibitors of S-adenosyl homocysteine/methylthioadenosine (SAH/MTA) nucleosidase are described. Using 6-substituted purine and deaza purines as the core scaffolds, a systematic and structure guided series of modifications provided low nM inhibitors with broad-spectrum antimicrobial activity.

Application of NMR SHAPES screening to an RNA target.

Several NMR screening techniques have been developed in recent years to aid in the identification of lead drug compounds. These NMR methods have traditionally been used for protein targets, and here we examine their applicability for an RNA target. We used the SHAPES compound library to test three different NMR screening methodologies: the saturation transfer difference (STD), the 2D trNOESY, and the WaterLOGSY experiments. We found that the WaterLOGSY experiment was the most sensitive method for our RNA target, the P4P6 domain of the Tetrahymena thermophila Group I intron. Using the WaterLOGSY experiment, we found that 23 of the 112 SHAPES compounds interact with P4P6. To identify which of these 23 hits bind through nonspecific interactions, we counterscreened with a linear duplex RNA control and identified one of the SHAPES compounds as interacting with P4P6 specifically. We thus demonstrated that the WaterLOGSY experiment in combination with the SHAPES compound library can be used to efficiently find RNA binding lead compounds.

Structure-based design, synthesis, and antimicrobial activity of indazole-derived SAH/MTA nucleosidase inhibitors.

The structure-based design, synthesis, and biological activity of a novel indazole-containing inhibitor series for S-adenosyl homocysteine/methylthioadenosine (SAH/MTA) nucleosidase are described. Use of 5-aminoindazole as the core scaffold provided a structure-guided series of low nanomolar inhibitors with broad-spectrum antimicrobial activity. The implementation of structure-based methodologies provided a 6000-fold increase in potency over a short timeline (several months) and an economy of synthesized compounds.