Shape: automatic conformation prediction of carbohydrates using a genetic algorithm

Abstract

BackgroundDetailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. has been developed to improve the availability of modelling in this field.ResultsThe software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction.ConclusionThe fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses.

Comprehensive Conformational Studies of Five Tripeptides and a Deduced Method for Efficient Determinations of Peptide Structures

“…Genetic algorithm has been long used for conformational searches of various molecular systems including atomic clusters, 77,78 alkanes, 79 carbohydrates, 80 amino acids, 81 peptides 18,19 and even proteins. 20,21 A genetic algorithm applied for finding stable conformers can be outlined as the following: The first step is to choose the best parent conformers according to a prescribed rule.…”

Deciphering the Glycan Preference of Bacterial Lectins by Glycan Array and Molecular Docking with Validation by Microcalorimetry and Crystallography

“…Nevertheless, conformational analysis of histo-blood group epitopes demonstrated that these oligosaccharides can adopt a limited number of well defined conformations [23], [24]. These are available in the BIOLIGO database (http://bioligo.cermav.cnrs.fr/) that contains three-dimensional structures of bioactive oligosaccharides generated using the SWEET-II [25] and POLYS [26] builders altogether with Shape a conformational search engine based on a genetic algorithm coupled to the MM3 force-field [27]. Molecular docking studies of fucose and fucose-containing oligosaccharides have been published using a variety of computational approaches, in protein targets such as human lectins [28], [29], antibodies [30], [31] or virus capsid proteins [32].…”

Umbrella Visualization: A method of analysis dedicated to glycan flexibility with UnityMol

“…Even if the first saccharide residues are present in the .pdb file, the remaining part of the glycan has to be reconstructed, avoiding any aberrant structures or steric clashes with the protein. SHAPE is a tool enabling a user to build any glycan structure [27]. SHAPE also predicts several energetically-favorable conformers using a genetic algorithm.…”