2P2Idb: a structural database dedicated to orthosteric modulation of protein-protein interactions.

Protein-protein interactions are considered as one of the next generation of therapeutic targets. Specific tools thus need to be developed to tackle this challenging chemical space. In an effort to derive some common principles from recent successes, we have built 2P2Idb (freely accessible at http://2p2idb.cnrs-mrs.fr), a hand-curated structural database dedicated to protein-protein interactions with known orthosteric modulators. It includes all interactions for which both the protein-protein and protein-ligand complexes have been structurally characterized. A web server provides links to related sites of interest, binding affinity data, pre-calculated structural information about protein-protein interfaces and 3D interactive views through java applets. Comparison of interfaces in 2P2Idb to those of representative datasets of heterodimeric complexes has led to the identification of geometrical parameters and residue properties to assess the druggability of protein-protein complexes. A tool is proposed to calculate a series of biophysical and geometrical parameters that characterize protein-protein interfaces. A large range of descriptors are computed including, buried accessible surface area, gap volume, non-bonded contacts, hydrogen-bonds, atom and residue composition, number of segments and secondary structure contribution. All together the 2P2I database represents a structural source of information for scientists from academic institutions or pharmaceutical industries.

HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings.

High-throughput RNAi screenings (HTS) allow quantifying the impact of the deletion of each gene in any particular function, from virus-host interactions to cell differentiation. However, there has been less development for functional analysis tools dedicated to RNAi analyses. HTS-Net, a network-based analysis program, was developed to identify gene regulatory modules impacted in high-throughput screenings, by integrating transcription factors-target genes interaction data (regulome) and protein-protein interaction networks (interactome) on top of screening z-scores. HTS-Net produces exhaustive HTML reports for results navigation and exploration. HTS-Net is a new pipeline for RNA interference screening analyses that proves better performance than simple gene rankings by z-scores, by re-prioritizing genes and replacing them in their biological context, as shown by the three studies that we reanalyzed. Formatted input data for the three studied datasets, source code and web site for testing the system are available from the companion web site at http://htsnet.marseille.inserm.fr/. We also compared our program with existing algorithms (CARD and hotnet2).

Solution structure of HndAc: a thioredoxin-like domain involved in the NADP-reducing hydrogenase complex.

The NADP-reducing hydrogenase complex from Desulfovibrio fructosovorans is a heterotetramer encoded by the hndABCD operon. Sequence analysis indicates that the HndC subunit (52 kDa) corresponds to the NADP-reducing unit, and the HndD subunit (63.5 kDa) is homologous to Clostridium pasteurianum hydrogenase. The role of HndA and HndB subunits (18.8 kDa and 13.8 kDa, respectively) in the complex remains unknown. The HndA subunit belongs to the [2Fe-2S] ferredoxin family typified by C. pasteurianum ferredoxin. HndA is organized into two independent structural domains, and we report in the present work the NMR structure of its C-terminal domain, HndAc. HndAc has a thioredoxin-like fold consisting in four beta-strands and two relatively long helices. The [2Fe-2S] cluster is located near the surface of the protein and bound to four cysteine residues particularly well conserved in this class of proteins. Electron exchange between the HndD N-terminal [2Fe-2S] domain (HndDN) and HndAc has been previously evidenced, and in the present studies we have mapped the binding site of the HndDN domain on HndAc. A structural analysis of HndB indicates that it is a FeS subunit with 41% similarity with HndAc and it contains a possible thioredoxin-like fold. Our data let us propose that HndAc and HndB can form a heterodimeric intermediate in the electron transfer between the hydrogenase (HndD) active site and the NADP reduction site in HndC.

Prediction of early breast cancer metastasis from DNA microarray data using high-dimensional cox regression models.

BACKGROUND: DNA microarray studies identified gene expression signatures predictive of metastatic relapse in early breast cancer. Standard feature selection procedures applied to reduce the set of predictive genes did not take into account the correlation between genes. In this paper, we studied the performances of three high-dimensional regression methods - CoxBoost, LASSO (Least Absolute Shrinkage and Selection Operator), and Elastic net - to identify prognostic signatures in patients with early breast cancer. METHODS: We analyzed three public retrospective datasets, including a total of 384 patients with axillary lymph node-negative breast cancer. The Amsterdam van't Veer's training set of 78 patients was used to determine the optimal gene sets and classifiers using sensitivity thresholds resulting in mis-classification of no more than 10% of the poor-prognosis group. To ensure the comparability between different methods, an automatic selection procedure was used to determine the number of genes included in each model. The van de Vijver's and Desmedt's datasets were used as validation sets to evaluate separately the prognostic performances of our classifiers. The results were compared to the original Amsterdam 70-gene classifier. RESULTS: The automatic selection procedure reduced the number of predictive genes up to a minimum of six genes. In the two validation sets, the three models (Elastic net, LASSO, and CoxBoost) led to the definition of genomic classifiers predicting the 5-year metastatic status with similar performances, with respective 59, 56, and 54% accuracy, 83, 75, and 83% sensitivity, and 53, 52, and 48% specificity in the Desmedt's dataset. In comparison, the Amsterdam 70-gene signature showed 45% accuracy, 97% sensitivity, and 34% specificity. The gene overlap and the classification concordance between the three classifiers were high. All the classifiers added significant prognostic information to that provided by the traditional prognostic factors and showed a very high overlap with respect to gene ontologies (GOs) associated with genes overexpressed in the predicted poor-prognosis vs. good-prognosis classes and centred on cell proliferation. Interestingly, all classifiers reported high sensitivity to predict the 4-year status of metastatic disease. CONCLUSIONS: High-dimensional regression methods are attractive in prognostic studies because finding a small subset of genes may facilitate the transfer to the clinic, and also because they strengthen the robustness of the model by limiting the selection of false-positive predictive genes. With only six genes, the CoxBoost classifier predicted the 4-year status of metastatic disease with 93% sensitivity. Selecting a few genes related to ontologies other than cell proliferation might further improve the overall sensitivity performance.

GFscore: a general nonlinear consensus scoring function for high-throughput docking.

Most of the recent published works in the field of docking and scoring protein/ligand complexes have focused on ranking true positives resulting from a Virtual Library Screening (VLS) through the use of a specified or consensus linear scoring function. In this work, we present a methodology to speed up the High Throughput Screening (HTS) process, by allowing focused screens or for hitlist triaging when a prohibitively large number of hits is identified in the primary screen, where we have extended the principle of consensus scoring in a nonlinear neural network manner. This led us to introduce a nonlinear Generalist scoring Function, GFscore, which was trained to discriminate true positives from false positives in a data set of diverse chemical compounds. This original Generalist scoring Function is a combination of the five scoring functions found in the CScore package from Tripos Inc. GFscore eliminates up to 75% of molecules, with a confidence rate of 90%. The final result is a Hit Enrichment in the list of molecules to investigate during a research campaign for biological active compounds where the remaining 25% of molecules would be sent to in vitro screening experiments. GFscore is therefore a powerful tool for the biologist, saving both time and money.