DocumentCode
21315
Title
Characterizing Molecular Interactions in Chemical Systems
Author
Gunther, David ; Boto, Roberto A. ; Contreras-Garcia, Juila ; Piquemal, Jean-Philip ; Tierny, Julien
Author_Institution
Inst. Mines-Telecom, Telecom ParisTech, Paris, France
Volume
20
Issue
12
fYear
2014
fDate
Dec. 31 2014
Firstpage
2476
Lastpage
2485
Abstract
Interactions between atoms have a major influence on the chemical properties of molecular systems. While covalent interactions impose the structural integrity of molecules, noncovalent interactions govern more subtle phenomena such as protein folding, bonding or self assembly. The understanding of these types of interactions is necessary for the interpretation of many biological processes and chemical design tasks. While traditionally the electron density is analyzed to interpret the quantum chemistry of a molecular system, noncovalent interactions are characterized by low electron densities and only slight variations of them - challenging their extraction and characterization. Recently, the signed electron density and the reduced gradient, two scalar fields derived from the electron density, have drawn much attention in quantum chemistry since they enable a qualitative visualization of these interactions even in complex molecular systems and experimental measurements. In this work, we present the first combinatorial algorithm for the automated extraction and characterization of covalent and noncovalent interactions in molecular systems. The proposed algorithm is based on a joint topological analysis of the signed electron density and the reduced gradient. Combining the connectivity information of the critical points of these two scalar fields enables to visualize, enumerate, classify and investigate molecular interactions in a robust manner. Experiments on a variety of molecular systems, from simple dimers to proteins or DNA, demonstrate the ability of our technique to robustly extract these interactions and to reveal their structural relations to the atoms and bonds forming the molecules. For simple systems, our analysis corroborates the observations made by the chemists while it provides new visual and quantitative insights on chemical interactions for larger molecular systems.
Keywords
DNA; biology computing; chemistry computing; proteins; DNA; atoms; automated extraction; bonding; chemical systems; combinatorial algorithm; complex molecular systems; connectivity information; electron density; molecular interactions; molecules; noncovalent interactions; protein folding; qualitative visualization; quantum chemistry; self assembly; structural integrity; Bonding; Chemicals; Data visualization; Feature extraction; Isosurfaces; Join Tree; Molecular Chemistry; Morse-Smale Complex; Topological Data Analysis;
fLanguage
English
Journal_Title
Visualization and Computer Graphics, IEEE Transactions on
Publisher
ieee
ISSN
1077-2626
Type
jour
DOI
10.1109/TVCG.2014.2346403
Filename
6875922
Link To Document