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      Docking and Molecular Dynamics-Based Identification of Interaction between Various Beta-Amyloid Isoforms and RAGE Receptor

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      International Journal of Molecular Sciences
      MDPI AG

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          Abstract

          Beta-amyloid peptide (Aβ) is a ligand associated with RAGE (Advanced glycosylation end product-specific receptor). Aβ is translocated in complexes with RAGE from the blood to brain across the blood–brain barrier (BBB) by transcytosis. Aβ and its isoforms are important factors in the Alzheimer’s disease (AD) pathogenesis. However, interaction with RAGE was previously studied for Aβ but not for its isoforms. The present study has been directed at identifying the key interaction interfaces between RAGE and Aβ isoforms (Aβ40, Aβ42, phosphorylated and isomerized isoforms pS8-Aβ42, isoD7-Aβ42). Two interfaces have been identified by docking: they are represented by an extended area at the junction of RAGE domains V and C1 and a smaller area linking C1 and C2 domains. Molecular dynamics (MD) simulations have shown that all Aβ isoforms form stable and tightly bound complexes. This indicates that all Aβ isoforms potentially can be transported through the cell as part of a complex with RAGE. Modeling of RAGE interaction interfaces with Aβ indicates which chemical compounds can potentially be capable of blocking this interaction, and impair the associated pathogenic cascades. The ability of three RAGE inhibitors (RAP, FPS-ZM1 and RP-1) to disrupt the RAGE:Aβ interaction has been probed by docking and subsequently the complexes’ stability verified by MD. The RP-1 and Aβ interaction areas coincide and therefore this inhibitor is very promising for the RAGE:Aβ interaction inhibition.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

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              Improved side-chain torsion potentials for the Amber ff99SB protein force field

              Recent advances in hardware and software have enabled increasingly long molecular dynamics (MD) simulations of biomolecules, exposing certain limitations in the accuracy of the force fields used for such simulations and spurring efforts to refine these force fields. Recent modifications to the Amber and CHARMM protein force fields, for example, have improved the backbone torsion potentials, remedying deficiencies in earlier versions. Here, we further advance simulation accuracy by improving the amino acid side-chain torsion potentials of the Amber ff99SB force field. First, we used simulations of model alpha-helical systems to identify the four residue types whose rotamer distribution differed the most from expectations based on Protein Data Bank statistics. Second, we optimized the side-chain torsion potentials of these residues to match new, high-level quantum-mechanical calculations. Finally, we used microsecond-timescale MD simulations in explicit solvent to validate the resulting force field against a large set of experimental NMR measurements that directly probe side-chain conformations. The new force field, which we have termed Amber ff99SB-ILDN, exhibits considerably better agreement with the NMR data. Proteins 2010. © 2010 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                IJMCFK
                International Journal of Molecular Sciences
                IJMS
                MDPI AG
                1422-0067
                October 2022
                October 05 2022
                : 23
                : 19
                : 11816
                Article
                10.3390/ijms231911816
                36233130
                1fc15e11-da97-4b6b-a5be-1a0c06d64147
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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