From experimental, structural probability distributions to the theoretical causality analysis of molecular changes

  • Paweł Daniluk
  • Maciej Dziubiński
  • Bogdan Lesyng
  • Marta Hallay-Suszek
  • Franciszek Rakowski
  • Łukasz Walewski

Abstract

A brief overview of causality analysis (CA) methods applied to MD simulations data for model biomolec ular systems is presented. A CausalMD application for postprocessing of MD data was designed and implemented. MD simulations of two model systems, porphycene (ab initio MD) and HIV-1 protease (coarse-grained MD) were carried out and analyzed. Granger's causality methodology based on a Multivariate Autoregressive (MVAR) formalism, followed by the Directed Transfer Function (DTF) analysis was applied. A novel approach based on the descriptors of local structure was also presented and preliminary results were reported. Casuality analyses are required for a better understanding of biomolecular functioning mechanisms. In particular, such analyses can link physics-based structural dynamics with functions inferred from molecular evolution processes. Current limitations and future developments of the presented methodologies are indicated.

Keywords

causality analysis, signal analysis, local descriptors, alignment, MVAR, Directed Transfer Function, molecular dynamics, porphycene, HIV-1 protease, molecular function, molecular evolution,

References

Published
Jan 25, 2017
How to Cite
DANILUK, Paweł et al. From experimental, structural probability distributions to the theoretical causality analysis of molecular changes. Computer Assisted Methods in Engineering and Science, [S.l.], v. 19, n. 3, p. 257-276, jan. 2017. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/93>. Date accessed: 26 jan. 2022.
Section
Articles