Petri nets formalism facilitates analysis of complex biomolecular structural data

Abstract

Molecular dynamics (MD) simulation is a popular method of protein and nucleic acids research. Current MD output trajectories are huge files and therefore they are hard to analyze. Petri nets (PNs) is a mathematical modeling language that allows for concise, graphical representation of complex data. We have developed a few algorithms for PNs generation from such large MD trajectories. One of them, called the One Place One Conformation (OPOC) algorithm, is presented in a greater detail. In the OPOC algorithm one biomolecular conformation corresponds to one PN place and a transition occurring in PN graph is related to a change between biomolecules conformations. As case studies three simulations are analyzed: an enforced steered MD (SMD) dissociation of a transthyretin protein tetramer into dimers, the SMD dissociation of an antibody-antigen complex and a classical MD simulation of transthyretin. We show that PNs reproduce events hidden in MD trajectories and enable observations of the conformational space features hard-to-see by the other clustering methods. Thus, a fundamental process of biomolecular data classification may be optimized using the PN approach.

Recent Advances in Operations Research in Computational Biology, Bioinformatics and Medicine

“…Gogolinska et al [5] presents a number of algorithms for generating Petri Nets (PN) from a large set of MD trajectories. Among the algorithms, One Place One Conformation (OPOC), is presented in a greater detail that reflects the changes between biomolecule conformations.…”

Introduction of steered molecular dynamics into UNRES coarse‐grained simulations package

“…The results can be compared with experimental atomic force microscopy method (AFM) and along with this technique provide insight into the mechanical properties of molecules, for instance ‐ proteins or DNA . Moreover, new extensions enlarging the applicability of SMD‐related methods are under development, for instance methods to probe ligand diffusion pathways from protein cavities, as well as new analysis methods are tested to explore more information from a SMD trajectory …”

“…[19] Moreover, new extensions enlarging the applicability of SMD-related methods are under development, for instance methods to probe ligand diffusion pathways from protein cavities, [20,21] as well as new analysis methods are tested to explore more information from a SMD trajectory. [22,23] One of the biggest problems in comparison of results from the SMD and the AFM methods comes from the difference in pulling speed, caused by a huge computational cost of conventional all-atom steered molecular dynamics (AA SMD). This problem increases with growing size of an investigated system.…”