On flexibility, deformability and mobility of protein structures in the light of a structural alphabet
Creators: name: Narwani, Tarun J.
name: Craveur, Pierrick
name: Shinada, Nicolas K.
name: Santuz, Hubert
name: Rebehmed, Joseph
name: Etchebest, Catherine
name: Brevern, Alexandre G. De
Publisher: name: F1000Research
Year: 2017

description: The function of a protein is directly dependent on its 3-dimensional structure. Visualization tools have oversimplified our views of protein structures. Often, they are considered as macromolecules with repetitive structures as rigid while the connecting loops as flexible or even disordered. <i>in silico</i> approaches are interesting tools to tackle this critical question of protein flexibility. Moreover, it allows applying other criteria than B-factors, to define flexibility [1].<br><br>We have previously developed different structural alphabets (SAs) [2], [3]. They are libraries of small protein fragments that are able to approximate every part of protein structures, making them more precise than classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using SAs for the structural analysis; from definition of ligand binding sites to superimposition of protein structures [4]. SAs are also well suited to perform prediction of protein flexibility from the sequence [5]. We have also used them to analyse the dynamics of protein structures in a case of a transmembrane protein [6] and for integrins implicated in pathologies [7], [8].<br><br>Here, we have selected a representative set of 169 protein structures describing equally the main four SCOP classes [9]. 3 independent Molecular Dynamic (MD) simulations of 50 ns have been performed for each system using GROMACS software [10] with classical parameters of AMBER99sb forcefield. All the simulations quickly reached a stabilize state, i.e. a plateau.<br><br>Each simulation was analysed through classical secondary structures assignment (with DSSP [11]) and Protein Blocks (PBs [2]) assignment (with PBxplore tool [12]). Notably, from PBs’ assignment, entropy value: <i>N</i><sub>eq</sub> can be computed which is a quantitative value reflecting the local conformational changes [2]. Of course, classical MD analyses, e.g. root mean square fluctuation (<i>RMSf</i>), have been performed.<br><br>From the original protein structures, normalized B-factors and relative solvent accessibility have been computed with in-house tools and DSSP [11].<br>Using this large and diverse set of protein dynamics enables us to efficiently analyse the evolution of local protein structures. For instance, at a global level, correlation between normalized B-factors and RMSF is 0.43. Contrastingly, by computing the average value per PB (total 16), this correlation enhances to 0.98, underlining that PBs are of great interest to analyse protein flexibility.<br><br>Interestingly, correlation of <i>N</i><sub>eq</sub> values with normalized B-factors and <i>RMSf</i> is merely 0.41 and 0.46, respectively. This behaviour roots from the fact that <i>N</i><sub>eq</sub> encompasses local conformational variations (at residue level) while <i>RMSf</i> is computed for the overall structure every time. Therefore, we encounter cases where <i>RMSf</i> values are very high (flexibility) while <i>N</i><sub>eq</sub> remains low (rigidity). Such cases may look awkward, as flexibility corresponds to huge movements and so must associate to high <i>N</i><sub>eq</sub> values. However, it may happen due to confusion of resolving between flexibility, deformability and mobility. Biologically, such cases could correspond to rigid regions (mobility) enclosed between two flexible regions (deformability).<br><br>Protein Blocks are thus of great interest to analyse MD simulation and their coupling to various experimental data (e.g. B-factor) allows understanding general and specific behaviours of local protein conformations found in various proteins.<br><br>Many complementary analyses were performed using PBs. Following are some of the primary findings. PB <i>d</i> and <i>m</i> (core of repetitive structures), depict higher tendencies to resist changes while many others are less reserved, e.g. PB <i>g</i> (for “coil”) which changes 40% of the times to another PB during the dynamics. Clustering of different behaviours of the PBs has been performed and reveals that some changes are not expected but often found. In a significant number of cases, PB <i>g</i> changes to PB <i>p</i>, during majority of the simulation time and even sometimes to PB <i>m</i>. Noteworthy is that, PB <i>p</i> is associated to loops connecting α-helix to β-strand while PB <i>m</i> is denotes stable α-helical regions.<br><br>We will present here a complete summary of all our results and perspectives of this work.<br> 

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