Graph-based molecular alignment (GMA).
We describe a combined 2D/3D approach for the superposition of flexible chemical structures, which is based on recent progress in the efficient identification of common subgraphs and a gradient-based torsion space optimization algorithm. The simplicity of the approach is reflected in its generality and computational efficiency: the suggested approach neither requires precalculated statistics on the conformations of the molecules nor does it make simplifying assumptions on the topology of the molecules being compared. Furthermore, graph-based molecular alignment produces alignments that are consistent with the chemistry of the molecules as well as their general structure, as it depends on both the local connectivities between atoms and the overall topology of the molecules. We validate this approach on benchmark sets taken from the literature and show that it leads to good results compared to computationally and algorithmically more involved methods. The results suggest that, for most practical purposes, graph-based molecular alignment is a viable alternative to molecular field alignment with respect to structural superposition and leads to structures of comparable quality in a fraction of the time.
Marialke
J
J
Institute for Informatics, Research and Educational Unit for Bioinformatics and Practical Informatics, Ludwig-Maximilians-University, Amalienstrasse 17, D-80333 Munich, Germany.
Körner
R
R
Tietze
S
S
Apostolakis
Joannis
J
D019295:Computational Biology; D008024:Ligands; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D012229:Rhinovirus / Q000737:chemistry / Q000378:metabolism; D013997:Time Factors
D008024:Ligands; D011506:Proteins