Sampling-based Motion Planning for Tracking Evolution of
Dynamic Tunnels in Molecular Dynamics Simulations

J 2019

Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations

VONÁSEK, Vojtěch; Adam JURČÍK; Katarína FURMANOVÁ a Barbora KOZLÍKOVÁ

Základní údaje

Originální název

Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations

Autoři

VONÁSEK, Vojtěch; Adam JURČÍK; Katarína FURMANOVÁ a Barbora KOZLÍKOVÁ

Vydání

Journal of Intelligent & Robotic Systems, 2019, 0921-0296

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Nizozemské království

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.259

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/19:00107165

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

Sampling-based motion planning;Rapidly exploring random tree;Protein tunnel detection

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 29. 4. 2019 17:20, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

Návaznosti

GA17-07690S, projekt VaV

Název: Metody identifikace a vizualizace tunelů pro flexibilní ligandy v dynamických proteinech (Akronym: FLigComp)

Investor: Grantová agentura ČR, Methods of Identification and Visualization of Tunnels for Flexible Ligands in Dynamic Proteins

Citovat

VONÁSEK, Vojtěch; Adam JURČÍK; Katarína FURMANOVÁ a Barbora KOZLÍKOVÁ. Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations. Journal of Intelligent & Robotic Systems. 2019, roč. 93, 3-4, s. 763-785. ISSN 0921-0296. Dostupné z: https://doi.org/10.1007/s10846-018-0877-6.

@article{1418319,
   author = {Vonásek, Vojtěch and Jurčík, Adam and Furmanová, Katarína and Kozlíková, Barbora},
   article_number = {3-4},
   doi = {https://doi.org/10.1007/s10846-018-0877-6},
   keywords = {Sampling-based motion planning;Rapidly exploring random tree;Protein tunnel detection},
   language = {eng},
   issn = {0921-0296},
   journal = {Journal of Intelligent & Robotic Systems},
   title = {Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations},
   url = {http://dx.doi.org/10.1007/s10846-018-0877-6},
   volume = {93},
   year = {2019}
}

TY  - JOUR
ID  - 1418319
AU  - Vonásek, Vojtěch - Jurčík, Adam - Furmanová, Katarína - Kozlíková, Barbora
PY  - 2019
TI  - Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations
JF  - Journal of Intelligent & Robotic Systems
VL  - 93
IS  - 3-4
SP  - 763-785
EP  - 763-785
SN  - 09210296
KW  - Sampling-based motion planning;Rapidly exploring random tree;Protein tunnel detection
UR  - http://dx.doi.org/10.1007/s10846-018-0877-6
L2  - http://dx.doi.org/10.1007/s10846-018-0877-6
N2  - 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.
ER  -

VONÁSEK, Vojtěch; Adam JURČÍK; Katarína FURMANOVÁ a Barbora KOZLÍKOVÁ. Sampling-based Motion Planning for Tracking Evolution of Dynamic Tunnels in Molecular Dynamics Simulations. \textit{Journal of Intelligent \&{} Robotic Systems}. 2019, roč.~93, 3-4, s.~763-785. ISSN~0921-0296. Dostupné z: https://doi.org/10.1007/s10846-018-0877-6.

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