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      Glycosaminoglycan-based hydrogels capture inflammatory chemokines and rescue defective wound healing in mice.

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          Abstract

          Excessive production of inflammatory chemokines can cause chronic inflammation and thus impair cutaneous wound healing. Capturing chemokine signals using wound dressing materials may offer powerful new treatment modalities for chronic wounds. Here, a modular hydrogel based on end-functionalized star-shaped polyethylene glycol (starPEG) and derivatives of the glycosaminoglycan (GAG) heparin was customized for maximal chemokine sequestration. The material is shown to effectively scavenge the inflammatory chemokines MCP-1 (monocyte chemoattractant protein-1), IL-8 (interleukin-8), and MIP-1α (macrophage inflammatory protein-1α) and MIP-1β (macrophage inflammatory protein-1β) in wound fluids from patients suffering from chronic venous leg ulcers and to reduce the migratory activity of human monocytes and polymorphonuclear neutrophils. In an in vivo model of delayed wound healing (db/db mice), starPEG-GAG hydrogels outperformed the standard-of-care product Promogran with respect to reduction of inflammation, as well as increased granulation tissue formation, vascularization, and wound closure.

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          Most cited references67

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          How good is automated protein docking?

          The protein docking server ClusPro has been participating in critical assessment of prediction of interactions (CAPRI) since its introduction in 2004. This article evaluates the performance of ClusPro 2.0 for targets 46-58 in Rounds 22-27 of CAPRI. The analysis leads to a number of important observations. First, ClusPro reliably yields acceptable or medium accuracy models for targets of moderate difficulty that have also been successfully predicted by other groups, and fails only for targets that have few acceptable models submitted. Second, the quality of automated docking by ClusPro is very close to that of the best human predictor groups, including our own submissions. This is very important, because servers have to submit results within 48 h and the predictions should be reproducible, whereas human predictors have several weeks and can use any type of information. Third, while we refined the ClusPro results for manual submission by running computationally costly Monte Carlo minimization simulations, we observed significant improvement in accuracy only for two of the six complexes correctly predicted by ClusPro. Fourth, new developments, not seen in previous rounds of CAPRI, are that the top ranked model provided by ClusPro was acceptable or better quality for all these six targets, and that the top ranked model was also the highest quality for five of the six, confirming that ranking models based on cluster size can reliably identify the best near-native conformations. Copyright © 2013 Wiley Periodicals, Inc.
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            ClusPro: an automated docking and discrimination method for the prediction of protein complexes.

            Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu
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              Heparin-Protein Interactions

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                Author and article information

                Journal
                Sci Transl Med
                Science translational medicine
                American Association for the Advancement of Science (AAAS)
                1946-6242
                1946-6234
                Apr 19 2017
                : 9
                : 386
                Affiliations
                [1 ] Department of Dermatology, Venerology, and Allergology, Leipzig University, 04103 Leipzig, Germany.
                [2 ] Collaborative Research Center (SFB-TR67) "Functional Biomaterials for Controlling Healing Processes in Bone and Skin-From Material Science to Clinical Application," Leipzig and Dresden, Germany.
                [3 ] Leibniz Institute of Polymer Research Dresden, Max Bergmann Center of Biomaterials Dresden, Hohe Straße 6, 01069 Dresden, Germany.
                [4 ] Technische Universität Dresden, Center for Regenerative Therapies Dresden, Fetscherstraße 105, 01307 Dresden, Germany.
                [5 ] Department of Dermatology, Venerology, and Allergology, Leipzig University, 04103 Leipzig, Germany. sandra.franz@medizin.uni-leipzig.de freudenberg@ipfdd.de.
                [6 ] Collaborative Research Center (SFB-TR67) "Functional Biomaterials for Controlling Healing Processes in Bone and Skin-From Material Science to Clinical Application," Leipzig and Dresden, Germany. sandra.franz@medizin.uni-leipzig.de freudenberg@ipfdd.de.
                Article
                9/386/eaai9044
                10.1126/scitranslmed.aai9044
                28424334
                bf4e0810-d85a-4e22-91ba-1b04087e37f5
                History

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