76 research outputs found
The Impact of Introducing a Histidine into an Apolar Cavity Site on Docking and Ligand Recognition
Simplified
model binding sites allow one to isolate entangled terms
in molecular energy functions. Here, we investigate the effects on
ligand recognition of the introduction of a histidine into a hydrophobic
cavity in lysozyme. We docked 656040 molecules and tested 26 highly
and nine poorly ranked. Twenty-one highly ranked molecules bound and
five were false positives, while three poorly ranked molecules were
false negatives. In the 16 X-ray complexes now known, the docking
predictions overlaid well with the crystallographic results. Although
ligand enrichment was high, the false negatives, the false positives,
and the inability to rank order illuminated weaknesses in our scoring,
particularly overweighed apolar and underweighted polar terms. Adjusting
these led to new problems, reflecting the entangled nature of docking
scoring functions. Changes in ligand affinity relative to other lysozyme
cavities speak to the subtleties of molecular recognition even in
these simple sites and to their relevance for testing different models
of recognition
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A New Spin on Antibody–Drug Conjugates: Trastuzumab-Fulvestrant Colloidal Drug Aggregates Target HER2-Positive Cells
While
the formation of colloidal aggregates leads to artifacts in early
drug discovery, their composition makes them attractive as nanoparticle
formulations for targeted drug delivery as the entire nanoparticle
is composed of drug. The typical transient stability of colloidal
aggregates has inhibited exploiting this property. To overcome this
limitation, we investigated a series of proteins to stabilize colloidal
aggregates of the chemotherapeutic, fulvestrant, including the following:
bovine serum albumin, a generic human immunoglobulin G, and trastuzumab,
a therapeutic human epidermal growth factor receptor 2 antibody. Protein
coronas reduced colloid size to <300 nm and improved their stability
to over 48 h in both buffered saline and media containing serum protein.
Unlike colloids stabilized with other proteins, trastuzumab-fulvestrant
colloids were taken up by HER2 overexpressing cells and were cytotoxic.
This new targeted formulation reimagines antibody–drug conjugates,
delivering mM concentrations of drug to a cell
Improving performance of the network as measured through Guilt-by-Association on GO.
<p>(<b>A</b>) The prediction of GO annotation terms grouped by evidence code and sub-ontology by individual and combined networks. The ChEBI subset consists of terms associated with the Chemical Entities of Biological Interest (ChEBI) ontology (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160098#pone.0160098.s006" target="_blank">S3 Table</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0160098#pone.0160098.s003" target="_blank">S3 Fig</a>). Error bars represent the standard error of the mean. Combining the networks improves performance substantially (average ~0.80). (<b>B</b>) The CoExp network performance gains with increasing sample size but with diminishing returns, especially when compared with the gains obtained by combining the orthogonal chemoinformatics network. Extrapolating the aggregation curve (orange line), we predict that we would need millions of more samples to achieve similar performance with CoExp alone as with the combined chemoinformatics and CoExp networks (orange arrow).</p
Colloid Formation by Drugs in Simulated Intestinal Fluid
Many organic molecules form colloidal aggregates in aqueous solution at micromolar concentrations. These aggregates promiscuously inhibit soluble proteins and are a major source of false positives in high-throughput screening. Several drugs also form colloidal aggregates, and there has been speculation that this may affect the absorption and distribution of at least one drug in vivo. Here we investigate the ability of drugs to form aggregates in simulated intestinal fluid. Thirty-three Biopharmaceutics Classification System (BCS) class II and class IV drugs, spanning multiple pharmacological activities, were tested for promiscuous aggregation in biochemical buffers. The 22 that behaved as aggregators were then tested for colloid formation in simulated intestinal fluid, a buffer mimicking conditions in the small intestine. Six formed colloids at concentrations equal to or lower than the concentrations reached in the gut, suggesting that aggregation may have an effect on the absorption and distribution of these drugs, and potentially others, in vivo
Colloidal Aggregation Affects the Efficacy of Anticancer Drugs in Cell Culture
Many small molecules, including bioactive molecules and
approved
drugs, spontaneously form colloidal aggregates in aqueous solution
at micromolar concentrations. Though it is widely accepted that aggregation
leads to artifacts in screens for ligands of soluble proteins, the
effects of colloid formation in cell-based assays have not been studied.
Here, seven anticancer drugs and one diagnostic reagent were found
to form colloids in both biochemical buffer and in cell culture media.
In cell-based assays, the antiproliferative activities of three of
the drugs were substantially reduced when in colloidal form as compared
to monomeric form; a new formulation method ensured the presence of
drug colloids versus drug monomers in solution. We also found that
Evans Blue, a dye classically used to measure vascular permeability
and to demonstrate the “enhanced permeability and retention
(EPR) effect” in solid tumors, forms colloids that adsorb albumin,
as opposed to older literature that suggested the reverse
Top 20 performing GO terms in the different networks.
<p>Top 20 performing GO terms in the different networks.</p
Ligand-based networks better recapitulate Gene Ontology than do PPI or co-expression networks.
<p>The (<b>A</b>) co-expression network and the (<b>B</b>) extended protein-protein interaction network are compared with the (<b>C</b>) ligand derived network for their ability to characterize gene function (defined in the Gene Ontology, GO). We assessed performance through cross-validation (area under the ROC curve, AUROC) of a neighbor-voting algorithm. Each curve represents the distribution of AUROCs across 790 GO terms. The dark grey shows the scores in cross-validation in each network, the black curves are the AUROCs after permuting the network nodes, while the light gray curves are the scores using the node degree as a generic predictor across all functional categories. The PPI network has the highest performance (<b>B</b>, dark grey, AUROC = 0.68) but this reflects node degree bias (light grey line, AUROC = 0.6). Co-expression has less bias (<b>A</b>, light grey line, AUROC = 0.52), but performs less well (dark grey line, AUROC = 0.62). The ligand network performs almost as well (<b>C</b>, dark grey line, AUROC = 0.67) as the extended PPI network with little node degree bias (light grey line, AUROC = 0.52). The random permutation of each network (black), have AUROCs between 0.48 and 0.5.</p
Discordance of bioinformatics and functional genomic similarity with chemoinformatic similarity.
<p>Likelihood that two proteins will be related by ligand similarity (solid line: SEA E-value < 1e-5, dashed line: SEA E-value < 1e-20) given a threshold in the (A) sequence similarity network, (B) co-expression network, and (C) extended protein-protein interaction network. The Y-axis is the likelihood that pairs of targets will have a SEA E-value better than 1e-5 (and, for sequence similarity, also 1e-20) at any given threshold of similarity on the X-axis. (D-F) Truth tables showing the correspondence of the protein-protein pairs that either are or are not related by ligand similarity <i>and</i> by sequence similarity, co-expression, or direct protein-protein interactions. In the upper left and lower right squares, the ligand-based and genomics association agree that the targets are or are not related, while in the lower left and upper right they disagree.</p
Stable Colloidal Drug Aggregates Catch and Release Active Enzymes
Small
molecule aggregates are considered nuisance compounds in
drug discovery, but their unusual properties as colloids could be
exploited to form stable vehicles to preserve protein activity. We
investigated the coaggregation of seven molecules chosen because they
had been previously intensely studied as colloidal aggregators, coformulating
them with bis-azo dyes. The coformulation reduced colloid sizes to
<100 nm and improved uniformity of the particle size distribution.
The new colloid formulations are more stable than previous aggregator
particles. Specifically, coaggregation of Congo Red with sorafenib,
tetraiodophenolphthalein (TIPT), or vemurafenib produced particles
that are stable in solutions of high ionic strength and high protein
concentrations. Like traditional, single compound colloidal aggregates,
the stabilized colloids adsorbed and inhibited enzymes like β-lactamase,
malate dehydrogenase, and trypsin. Unlike traditional aggregates,
the coformulated colloid-protein particles could be centrifuged and
resuspended multiple times, and from resuspended particles, active
trypsin could be released up to 72 h after adsorption. Unexpectedly,
the stable colloidal formulations can sequester, stabilize, and isolate
enzymes by spin-down, resuspension, and release
Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking
A key metric to assess molecular docking remains ligand
enrichment
against challenging decoys. Whereas the directory of useful decoys
(DUD) has been widely used, clear areas for optimization have emerged.
Here we describe an improved benchmarking set that includes more diverse
targets such as GPCRs and ion channels, totaling 102 proteins with
22886 clustered ligands drawn from ChEMBL, each with 50 property-matched
decoys drawn from ZINC. To ensure chemotype diversity, we cluster
each target’s ligands by their Bemis–Murcko atomic frameworks.
We add net charge to the matched physicochemical properties and include
only the most dissimilar decoys, by topology, from the ligands. An
online automated tool (http://decoys.docking.org) generates
these improved matched decoys for user-supplied ligands. We test this
data set by docking all 102 targets, using the results to improve
the balance between ligand desolvation and electrostatics in DOCK
3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org
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