Ligand Similarity Complements Sequence, Physical Interaction, and Co-Expression for Gene Function Prediction

The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63-0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited

Identifying mechanism-of-action targets for drugs and probes

Notwithstanding their key roles in therapy and as biological probes, 7% of approved drugs are purported to have no known primary target, and up to 18% lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to “de-orphanize” drugs that lack primary targets. Surprisingly, targets could be easily predicted for many: Whereas these targets were not known to us nor to the common databases, most could be confirmed by literature search, leaving only 13 Food and Drug Administration—approved drugs with unknown targets; the number of drugs without molecular targets likely is far fewer than reported. The number of worldwide drugs without reasonable molecular targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, there remained at least seven drugs for which reasonable mechanism-of-action targets were unknown but could be predicted, including the antitussives clemastine, cloperastine, and nepinalone; the antiemetic benzquinamide; the muscle relaxant cyclobenzaprine; the analgesic nefopam; and the immunomodulator lobenzarit. For each, predicted targets were confirmed experimentally, with affinities within their physiological concentration ranges. Turning this question on its head, we next asked which drugs were specific enough to act as chemical probes. Over 100 drugs met the standard criteria for probes, and 40 did so by more stringent criteria. A chemical information approach to drug-target association can guide therapeutic development and reveal applications to probe biology, a focus of much current interest

Defective Synapse Maturation and Enhanced Synaptic Plasticity in Shank2 Δex7-/- Mice

Autism spectrum disorders (ASDs) are neurodevelopmental disorders with a strong genetic etiology. Since mutations in human SHANK genes have been found in patients with autism, genetic mouse models are used for a mechanistic understanding of ASDs and the development of therapeutic strategies. SHANKs are scaffold proteins in the postsynaptic density of mammalian excitatory synapses with proposed functions in synaptogenesis, regulation of dendritic spine morphology, and instruction of structural synaptic plasticity. In contrast to all studies so far on the function of SHANK proteins, we have previously observed enhanced synaptic plasticity in Shank2 Δex7-/- mice. In a series of experiments, we now reproduce these results, further explore the synaptic phenotype, and directly compare our model to the independently generated Shank2 Δex6-7-/- mice. Minimal stimulation experiments reveal that Shank2 Δex7-/- mice possess an excessive fraction of silent (i.e., α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, short, AMPA receptor lacking) synapses. The synaptic maturation deficit emerges during the third postnatal week and constitutes a plausible mechanistic explanation for the mutants' increased capacity for long-term potentiation, both in vivo and in vitro. A direct comparison with Shank2 Δex6-7-/- mice adds weight to the hypothesis that both mouse models show a different set of synaptic phenotypes, possibly due to differences in their genetic background. These findings add to the diversity of synaptic phenotypes in neurodevelopmental disorders and further support the supposed existence of "modifier genes" in the expression and inheritance of ASDs

Structure-inspired design of β-arrestin-biased ligands for aminergic GPCRs

Development of biased ligands targeting G protein-coupled receptors (GPCRs) is a promising approach for current drug discovery. Although structure-based drug design of biased agonists remains challenging even with an abundance of GPCR crystal structures, we present an approach for translating GPCR structural data into β-arrestin-biased ligands for aminergic GPCRs. We identified specific amino acid-ligand contacts at transmembrane helix 5 (TM5) and extracellular loop 2 (EL2) responsible for Gi/o and β-arrestin signaling, respectively, and targeted those residues to develop biased ligands. For these ligands, we found that bias is conserved at other aminergic GPCRs that retain similar residues at TM5 and EL2. Our approach provides a template for generating arrestin-biased ligands by modifying predicted ligand interactions that block TM5 interactions and promote EL2 interactions. This strategy may facilitate the structure-guided design of arrestin-biased ligands at other GPCRs, including polypharmacological biased ligands

Structure-Based Discovery of A2A Adenosine Receptor Ligands

The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A(2A) adenosine receptor, based on complementarity to its recently determined structure. The A(2A) adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular docking to screen a 1.4 million compound database against the X-ray structure computationally and tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed substantial activity with affinities between 200 nM and 9 microM. For the most potent of these new inhibitors, over 50-fold specificity was observed for the A(2A) versus the related A(1) and A(3) subtypes. These high hit rates and affinities at least partly reflect the bias of commercial libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target

Complementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain Inhibitors†

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99 % of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1 % of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone

Characterization and Solubilization of Pyrrole–Imidazole Polyamide Aggregates

To optimize the biological activity of pyrrole–imidazole polyamide DNA-binding molecules, we characterized the aggregation propensity of these compounds through dynamic light scattering and fractional solubility analysis. Nearly all studied polyamides were found to form measurable particles 50–500 nm in size under biologically relevant conditions, while HPLC-based analyses revealed solubility trends in both core sequences and peripheral substituents that did not correlate with overall ionic charge. The solubility of both hairpin and cyclic polyamides was increased upon addition of carbohydrate solubilizing agents, in particular, 2-hydroxypropyl-β-cyclodextrin (HpβCD). In mice, the use of HpβCD allowed for improved injection conditions and subsequent investigations of the availability of polyamides in mouse plasma to human cells. The results of these studies will influence the further design of Py-Im polyamides and facilitate their study in animal models

Automated Docking Screens: A Feasibility Study

Molecular docking is themost practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCKBlaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCKBlaster recapitulates the crystal ligand pose within 2 A ̊ rmsd 50-60 % of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5 % of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5 % of 100 property-matched decoys while also posing within 2 A ̊ rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available a

Promiscuous Aggregate-Based Inhibitors Promote Enzyme Unfolding

One of the leading sources of false positives in early drug discovery is the formation of organic small molecule aggregates, which inhibit enzymes nonspecifically at micromolar concentrations in aqueous solution. The molecular basis for this widespread problem remains hazy. To investigate the mechanism of inhibition at a molecular level, we determined changes in solvent accessibility that occur when an enzyme binds to an aggregate using hydrogen-deuterium exchange mass spectrometry. For AmpC beta-lactamase, binding to aggregates of the small molecule rottlerin increased the deuterium exchange of all 10 reproducibly detectable peptides, which covered 41% of the sequence of beta-lactamase. This suggested a global increase in proton accessibility upon aggregate binding, consistent with denaturation. We then investigated whether enzyme-aggregate complexes were more susceptible to proteolysis than uninhibited enzyme. For five aggregators, trypsin degradation of beta-lactamase increased substantially when beta-lactamase was inhibited by aggregates, whereas uninhibited enzyme was generally stable to digestion. Combined, these results suggest that the mechanism of action of aggregate-based inhibitors proceeds via partial protein unfolding when bound to an aggregate particle

Multilevel Parallelization of AutoDock 4.2

<p>Abstract</p> <p>Background</p> <p>Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4).</p> <p>Results</p> <p>Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers.</p> <p>Conclusions</p> <p>Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.</p