Sharing of research data in public repositories has become best practice in academia. With the accumulation of massive data, network bandwidth and storage requirements are rapidly increasing. The ProteomeXchange (PX) consortium implements a mode of centralized metadata and distributed raw data management, which promotes effective data sharing. To facilitate open access of proteome data worldwide, we have developed the integrated proteome resource iProX (http://www.iprox.org) as a public platform for collecting and sharing raw data, analysis results and metadata obtained from proteomics experiments. The iProX repository employs a web-based proteome data submission process and open sharing of mass spectrometry-based proteomics datasets. Also, it deploys extensive controlled vocabularies and ontologies to annotate proteomics datasets. Users can use a GUI to provide and access data through a fast Aspera-based transfer tool. iProX is a full member of the PX consortium; all released datasets are freely accessible to the public. iProX is based on a high availability architecture and has been deployed as part of the proteomics infrastructure of China, ensuring long-term and stable resource support. iProX will facilitate worldwide data analysis and sharing of proteomics experiments.

Tumor microenvironment contributes to tumor angiogenesis. However, the role of the activated cancer associated-fibroblasts (CAFs) in angiogenesis is still unclear. Here we report that miR-205/YAP1 signaling in the activated stromal fibroblasts plays a critical role in VEGF-independent angiogenesis in breast tumor. Methods: miR-205 expression was assessed by quantitative real-time polymerase chain reaction (qRT-PCR); YAP1 expression by qRT-PCR, western blotting and immunohistochemistry; IL11 and IL15 expression by qRT-PCR, western blotting and ELISA. Tube formation and three-dimensioned sprouting assays in vitro, and orthotopic Xenografts in vivo were conducted as angiogenesis experiments. The mechanism of miR-205/YAP1-mediated tumor angiogenesis was analyzed via overexpression and shRNA, siRNA, or antibody neutralization experiments in combination with anti-VEGF antibody or Axitinib. Results: miR-205/YAP1 signaling axis activates breast normal fibroblasts (NFs) into CAFs, promotes tubule formation and sprouting of Human Umbilical Vein Endothelial Cells (HUVECs). Rescue of miR-205 in CAFs blunts angiogenesis processes. YAP1, a target of miR-205, does not regulate VEGF expression but specifically enhances IL11 and IL15 expressions, maintaining tumor angiogenesis even in the presence of Axitinib or after exhaustion of VEGF by neutralizing VEGF antibody. IL11 and IL15 released from CAFs activate STAT3 signaling in HUVECs. Blockage of IL11 and IL15 expression in CAFs results in the inactivation of STAT3-signaling in HUVECs and repression of the CAF-induced angiogenesis. The blunt angiogenesis halts the invasion and metastasis of breast cancer cells in vivo. Conclusions: These results provide a novel insight into breast CAF-induced tumor angiogenesis in a VEGF-independent manner.

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Protein–protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

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