Alexandre G. de Brevern's publications

Year 2018

  1. Narwani T.J., Craveur P., Shinada N.K., Santuz H., Rebehmed J., Etchebest C., de Brevern A.G.#
    Dynamics and Deformability of α-, 310- and π-Helices.
    Archives of Biological Sciences (2018) 70(1):21-31.

    Protein structures are often represented as seen in crystals as (i) rigid macromolecules (ii) with helices, sheets and coils. However, both definitions are partial because (i) proteins are highly dynamic macromolecules and (ii) the description of protein structures could be more precise. With regard to these two points, we analyzed and quantified the stability of helices by considering α-helices as well as 310-and π-helices. Molecular dynamic (MD) simulations were performed on a large set of 169 representative protein domains. The local protein conformations were followed during each simulation and analyzed. The classical flexibility index (B-factor) was confronted with the MD root mean square flexibility (RMSF) index. Helical regions were classified according to their level of helicity from high to none. For the first time, a precise quantification showed the percentage of rigid and flexible helices that underlie unexpected behaviors. Only 76.4% of the residues associated with α-helices retain the conformation, while this tendency drops to 40.5% for 310-helices and is never observed for π-helices. α-helix residues that do not remain as an α-helix have a higher tendency to assume β-turn conformations than 310-or π-helices. The 310-helices that switch to the α-helix conformation have a higher B-factor and RMSF values than the average 310-helix but are associated with a lower accessibility. Rare π-helices assume a β-turn, bend and coil conformations, but not α-or 310-helices. The view on π-helices drastically changes with the new DSSP (Dictionary of Secondary Structure of Proteins) assignment approach, leading to behavior similar to 310-helices, thus underlining the importance of secondary structure assignment methods..
    paper paper , sup data paper .

    • The Ministry of Research (France)
    • The University Paris Diderot, Sorbonne Paris Cite (France)
    • The National Institute of Blood Transfusion (INTS, France)
    • The National Institute of Health and Medical Research (INSERM, France)
    • The Laboratories of Excellence, GR-Ex (France)
    • Indo-French Centre for the Promotion of Advanced Research/CEFIPRA for collaborative grant (number 5302-2)
    • grant from the French National Research Agency (ANR): NaturaDyRe (ANR-2010-CD2I-014-04)
    • ANRT
    • The authors were granted access to high performance computing (HPC) resources at the French National Computing Centre CINES under Grant No. c2013037147 funded by the GENCI (Grand Equipement National de Calcul Intensif).
    • Calculations were also performed on an SGI cluster granted by Conseil Régional Ile de France and INTS (SESAME Grant).

Alexandre G. de Brevern
Last Modification : August 2018
Paris7 Inserm INTS INTS GR-Ex Sorbonne Paris Cite