Creation, refinement, and evaluation of conformational ensembles of proteins using the Torsional Network Model

Máster Universitario en Bioinformática y Biología ComputacionalOne of the main limitations of structural bioinformatics lies in the difficulty of properly accounting for the dynamical aspects of proteins, which are often critical to their functional mechanisms. Among the tools developed to deal with this issue, the Torsional Network Model (TNM) relies on internal degrees of freedom (torsion angles of the protein backbone), and can give a description of the thermal fluctuations of a protein structure, as well as generate structural ensembles. However, the TNM is a coarse-grained model that cannot ensure that the newly created conformations are exempt from any structural defects. Therefore, the main hypothesis of this project is that TNM assembly process can be improved. The ability to generate high-quality structural ensembles describing the dynamical properties of a protein would indeed be highly valuable in various applications. In this thesis, we create, evaluate and refine TNM ensembles from a set of reference protein structures defined experimentally (Levin et al., 2007). An approximation used in Bastolla and Dehouck, 2019, is developed: the evaluation is performed by Molprobity analysis, and the refinement is done by SIDEpro. Furthermore, a new approach is taken when refining the ensembles by Energy Minimization (EM). The results show a potential improvement of the TNM ensembles when adjusting the target RMSD to the protein studied; point to a enhancement when using side-chain reconstructions , and to its combination with Energy Minimization as a way to optimize the structure quality. On the other hand, the pros and cons of the followed methodology are discussed, because the use of the available static-protein oriented measures and methods makes specially important to beware of their limitations when applied to the protein-dynamic oriented TNM. Exploring further target RMSD values, adjusting them to specific protein dynamic simulations or replicating the same pipe-line in different data-sets are some of the proposals for future work. Furthermore, taking into account variables like the temperature, the flexibility of the protein, and the estimated optimal RMSD would be interesting for the next studies