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      The Extent of Human Apolipoprotein A-I Lipidation Strongly Affects the β-Amyloid Efflux Across the Blood-Brain Barrier in vitro


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          Much evidence suggests a protective role of high-density lipoprotein (HDL) and its major apolipoprotein apoA-I, in Alzheimer’s disease (AD). The biogenesis of nascent HDL derived from a first lipidation of apoA-I, which is synthesized by the liver and intestine but not in the brain, in a process mediated by ABCA1. The maturation of nascent HDL in mature spherical HDL is due to a subsequent lipidation step, LCAT-mediated cholesterol esterification, and the change of apoA-I conformation. Therefore, different subclasses of apoA-I-HDL simultaneously exist in the blood circulation. Here, we investigated if and how the lipidation state affects the ability of apoA-I-HDL to target and modulate the cerebral β-amyloid (Aβ) content from the periphery, that is thus far unclear. In particular, different subclasses of HDL, each with different apoA-I lipidation state, were purified from human plasma and their ability to cross the blood-brain barrier (BBB), to interact with Aβ aggregates, and to affect Aβ efflux across the BBB was assessed in vitro using a transwell system. The results showed that discoidal HDL displayed a superior capability to promote Aβ efflux in vitro (9 × 10 -5 cm/min), when compared to apoA-I in other lipidation states. In particular, no effect on Aβ efflux was detected when apoA-I was in mature spherical HDL, suggesting that apoA-I conformation, and lipidation could play a role in Aβ clearance from the brain. Finally, when apoA-I folded its structure in discoidal HDL, rather than in spherical ones, it was able to cross the BBB in vitro and strongly destabilize the conformation of Aβ fibrils by decreasing the order of the fibril structure (-24%) and the β-sheet content (-14%). These data suggest that the extent of apoA-I lipidation, and consequently its conformation, may represent crucial features that could exert their protective role in AD pathogenesis.

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          FireDock: a web server for fast interaction refinement in molecular docking†

          Structural details of protein–protein interactions are invaluable for understanding and deciphering biological mechanisms. Computational docking methods aim to predict the structure of a protein–protein complex given the structures of its single components. Protein flexibility and the absence of robust scoring functions pose a great challenge in the docking field. Due to these difficulties most of the docking methods involve a two-tier approach: coarse global search for feasible orientations that treats proteins as rigid bodies, followed by an accurate refinement stage that aims to introduce flexibility into the process. The FireDock web server, presented here, is the first web server for flexible refinement and scoring of protein–protein docking solutions. It includes optimization of side-chain conformations and rigid-body orientation and allows a high-throughput refinement. The server provides a user-friendly interface and a 3D visualization of the results. A docking protocol consisting of a global search by PatchDock and a refinement by FireDock was extensively tested. The protocol was successful in refining and scoring docking solution candidates for cases taken from docking benchmarks. We provide an option for using this protocol by automatic redirection of PatchDock candidate solutions to the FireDock web server for refinement. The FireDock web server is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.
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            Identification of Scavenger Receptor SR-BI as a High Density Lipoprotein Receptor

            High density lipoprotein (HDL) and low density lipoprotein (LDL) are cholesterol transport particles whose plasma concentrations are directly (LDL) and inversely (HDL) correlated with risk for atherosclerosis. LDL catabolism involves cellular uptake and degradation of the entire particle by a well-characterized receptor. HDL, in contrast, selectively delivers its cholesterol, but not protein, to cells by unknown receptors. Here it is shown that the class B scavenger receptor SR-BI is an HDL receptor. SR-BI binds HDL with high affinity, is expressed primarily in liver and nonplacental steroidogenic tissues, and mediates selective cholesterol uptake by a mechanism distinct from the classic LDL receptor pathway.
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              FireDock: fast interaction refinement in molecular docking.

              Here, we present FireDock, an efficient method for the refinement and rescoring of rigid-body docking solutions. The refinement process consists of two main steps: (1) rearrangement of the interface side-chains and (2) adjustment of the relative orientation of the molecules. Our method accounts for the observation that most interface residues that are important in recognition and binding do not change their conformation significantly upon complexation. Allowing full side-chain flexibility, a common procedure in refinement methods, often causes excessive conformational changes. These changes may distort preformed structural signatures, which have been shown to be important for binding recognition. Here, we restrict side-chain movements, and thus manage to reduce the false-positive rate noticeably. In the later stages of our procedure (orientation adjustments and scoring), we smooth the atomic radii. This allows for the minor backbone and side-chain movements and increases the sensitivity of our algorithm. FireDock succeeds in ranking a near-native structure within the top 15 predictions for 83% of the 30 enzyme-inhibitor test cases, and for 78% of the 18 semiunbound antibody-antigen complexes. Our refinement procedure significantly improves the ranking of the rigid-body PatchDock algorithm for these cases. The FireDock program is fully automated. In particular, to our knowledge, FireDock's prediction results are comparable to current state-of-the-art refinement methods while its running time is significantly lower. The method is available at http://bioinfo3d.cs.tau.ac.il/FireDock/. 2007 Wiley-Liss, Inc.

                Author and article information

                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                16 May 2019
                : 13
                : 419
                [1] 1School of Medicine and Surgery, Nanomedicine Center NANOMIB, University of Milano-Bicocca , Monza, Italy
                [2] 2Dipartimento di Scienze Farmacologiche e Biomolecolari, Centro Grossi Paoletti, Università degli Studi di Milano , Milan, Italy
                [3] 3Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, Scuola Universitaria Professionale della Svizzera Italiana, Università della Svizzera Italiana , Manno, Switzerland
                Author notes

                Edited by: Irving E. Vega, Michigan State University, United States

                Reviewed by: Ling Li, University of Minnesota, United States; Baiba Kurins Gillard, Houston Methodist Research Institute, United States; Cheryl Wellington, University of British Columbia, Canada

                *Correspondence: Alysia Cox, a.cox1@ 123456campus.unimib.it

                These authors have contributed equally to this work

                This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience

                Copyright © 2019 Dal Magro, Simonelli, Cox, Formicola, Corti, Cassina, Nardo, Mantegazza, Salerno, Grasso, Deriu, Danani, Calabresi and Re.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 23 January 2019
                : 11 April 2019
                Page count
                Figures: 8, Tables: 0, Equations: 1, References: 76, Pages: 15, Words: 0
                Funded by: Università degli Studi di Milano-Bicocca 10.13039/501100002954
                Award ID: 2015-ATE-0510
                Original Research

                hdl,apoa-i,β-amyloid,alzheimer’s disease,blood-brain barrier
                hdl, apoa-i, β-amyloid, alzheimer’s disease, blood-brain barrier


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