CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge.

Community Structure-Activity Resource (CSAR) conducted a benchmark exercise to evaluate the current computational methods for protein design, ligand docking, and scoring/ranking. The exercise consisted of three phases. The first phase required the participants to identify and rank order which designed sequences were able to bind the small molecule digoxigenin. The second phase challenged the community to select a near-native pose of digoxigenin from a set of decoy poses for two of the designed proteins. The third phase investigated the ability of current methods to rank/score the binding affinity of 10 related steroids to one of the designed proteins (pKd = 4.1 to 6.7). We found that 11 of 13 groups were able to correctly select the sequence that bound digoxigenin, with most groups providing the correct three-dimensional structure for the backbone of the protein as well as all atoms of the active-site residues. Eleven of the 14 groups were able to select the appropriate pose from a set of plausible decoy poses. The ability to predict absolute binding affinities is still a difficult task, as 8 of 14 groups were able to correlate scores to affinity (Pearson-r > 0.7) of the designed protein for congeneric steroids and only 5 of 14 groups were able to correlate the ranks of the 10 related ligands (Spearman-ρ > 0.7).

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.

CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pK(a). This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.

Direct transcriptional induction of Gadd45gamma by Ascl1 during neuronal differentiation.

The basic helix-loop-helix transcription factor Ascl1 plays a critical role in the intrinsic genetic program responsible for neuronal differentiation. Here, we describe a novel model system of P19 embryonic carcinoma cells with doxycycline-inducible expression of Ascl1. Microarray hybridization and real-time PCR showed that these cells demonstrated increased expression of many neuronal proteins in a time- and concentration-dependent manner. Interestingly, the gene encoding the cell cycle regulator Gadd45gamma was increased earliest and to the greatest extent following Ascl1 induction. Here, we provide the first evidence identifying Gadd45gamma as a direct transcriptional target of Ascl1. Transactivation and chromatin immunoprecipitation assays identified two E-box consensus sites within the Gadd45gamma promoter necessary for Ascl1 regulation, and demonstrated that Ascl1 is bound to this region within the Gadd45gamma promoter. Furthermore, we found that overexpression of Gadd45gamma itself is sufficient to initiate some aspects of neuronal differentiation independent of Ascl1.

Transcriptional regulatory events initiated by Ascl1 and Neurog2 during neuronal differentiation of P19 embryonic carcinoma cells.

As members of the proneural basic-helix-loop-helix (bHLH) family of transcription factors, Ascl1 and Neurog2 direct the differentiation of specific populations of neurons at various times and locations within the developing nervous system. In order to characterize the mechanisms employed by these two bHLH factors, we generated stable, doxycycline-inducible lines of P19 embryonic carcinoma cells that express comparable levels of Ascl1 and Neurog2. Upon induction, both Ascl1 and Neurog2 directed morphological and immunocytochemical changes consistent with initiation of neuronal differentiation. Comparison of Ascl1- and Neurog2-regulated genes by microarray analyses showed both shared and distinct transcriptional changes for each bHLH protein. In both Ascl1- and Neurog2-differentiating cells, repression of Oct4 mRNA levels was accompanied by increased Oct4 promoter methylation. However, DNA demethylation was not detected for genes induced by either bHLH protein. Neurog2-induced genes included glutamatergic marker genes while Ascl1-induced genes included GABAergic marker genes. The Neurog2-specific induction of a gene encoding a protein phosphatase inhibitor, Ppp1r14a, was dependent on distinct, canonical E-box sequences within the Ppp1r14a promoter and the nucleotide sequences within these E-boxes were partially responsible for Neurog2-specific regulation. Our results illustrate multiple novel mechanisms by which Ascl1 and Neurog2 regulate gene repression during neuronal differentiation in P19 cells.

Microarray transfection analysis of transcriptional regulation by cAMP-dependent protein kinase.

A wide variety of bioinformatic tools have been described to characterize potential transcriptional regulatory mechanisms based on genomic sequence analysis and microarray hybridization studies. However, these regulatory mechanisms are still experimentally verified using transient transfection methods. Current transfection methods are limited both by their large scale and by the low level of efficiency for certain cell types. Our goals were to develop a microarray-based transfection method that could be optimized for different cell types and that would be useful in reporter assays of transcriptional regulation. Here we describe a novel transfection method, termed STEP (surface transfection and expression protocol), which employs microarray-based DNA transfection of adherent cells in the functional analysis of transcriptional regulation. In STEP, recombinant proteins with biological activities designed to enhance transfection are complexed with expression vector DNAs prior to spotting on microscope slides. The recombinant proteins used in STEP complexes can be varied to increase the efficiency for different cell types. We demonstrate that STEP efficiently transfects both supercoiled plasmids and PCR-generated linear expression cassettes. A co-transfection assay using effector expression vectors encoding the cAMP-dependent protein kinase (PKA), as well as reporter vectors containing PKA-regulated promoters, showed that STEP transfection allows detection and quantitation of transcriptional regulation by this protein kinase. Because bioinformatic studies often result in the identification of many putative regulatory elements and signaling pathways, this approach should be of utility in high-throughput functional genomic studies of transcriptional regulation.