Detailed Information on Publication Record
2019
Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations
VONÁSEK, Vojtěch, Adam JURČÍK, Katarína FURMANOVÁ and Barbora KOZLÍKOVÁBasic information
Original name
Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations
Authors
VONÁSEK, Vojtěch (203 Czech Republic, guarantor), Adam JURČÍK (203 Czech Republic, belonging to the institution), Katarína FURMANOVÁ (703 Slovakia, belonging to the institution) and Barbora KOZLÍKOVÁ (203 Czech Republic, belonging to the institution)
Edition
Journal of Intelligent & Robotic Systems, 2019, 0921-0296
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10200 1.2 Computer and information sciences
Country of publisher
Netherlands
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 2.259
RIV identification code
RIV/00216224:14330/19:00107165
Organization unit
Faculty of Informatics
UT WoS
000459439400024
Keywords in English
Sampling-based motion planning;Rapidly exploring random tree;Protein tunnel detection
Tags
International impact, Reviewed
Změněno: 29/4/2019 17:20, RNDr. Pavel Šmerk, Ph.D.
Abstract
V originále
Proteins are involved in many biochemical processes. The behavior of proteins is highly influenced by the presence of internal void space, in literature denoted as tunnels or cavities. Tunnels are paths leading from an inner protein active site to its surface. The knowledge about tunnels and their evolution over time, captured in molecular dynamics simulations, provides an insight into important protein properties (e.g., their stability or activity). For each individual snapshot of molecular dynamics, tunnels can be detected using Voronoi diagrams and then aggregated over time to trace their behavior. However, this approach is suitable only when a given tunnel is detected in all snapshots of molecular dynamics. This is often not the case of traditionally used approaches to tunnel computation. When a tunnel becomes too narrow in a particular snapshot, the existing approaches cannot detect this case and the tunnel completely disappears from the results. On the other hand, this situation can be quite common as tunnels move, disappear and appear again, split, or merge. Therefore, in this paper we propose a method which enables to trace also tunnels in those missing snapshots. We call them dynamic tunnels and we use the sampling-based motion planning to compute them. The Rapidly Exploring Random Tree (RRT) algorithm is used to explore the void space in each frame of the protein dynamics. The void space is represented by a tree structure that is transferred to the next frame of the dynamics and updated to remove collisions and to cover newly emerged free regions of the void space. If the void space reaches the surface of the protein, a dynamic tunnel is reconstructed by tracking back in the tree towards a desired place (i.e., the active site). To efficiently sample the narrow void space inside proteins, a Voronoi diagram of the static protein frames is used. The results of the proposed method are demonstrated on an exemplary dataset obtained from the domain experts and the results are compared with the classic aggregation-based tunnel detection performed using the state-of-the-art CAVER 3.0 tool.
Links
| GA17-07690S, research and development project |
|