A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers

This paper describes a new and efficient stochastic conformational sampling method for generating a range of low-energy molecule conformations. Sampling can be tailored to a specific structural domain (e.g., peptides) by extracting torsional profiles from specific datasets and subsequently applying...

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Bibliographic Details
Published in:Journal of computer-aided molecular design 2008-09, Vol.22 (9), p.681-691
Main Authors: Dorfman, Roman J., Smith, Karl M., Masek, Brian B., Clark, Robert D.
Format: Article
Language:English
Subjects:
Animal Anatomy
Biophysics
Chemistry
Chemistry and Materials Science
Computer Applications in Chemistry
Endothelin-1 - antagonists & inhibitors
Endothelin-1 - metabolism
Histology
Knowledge Bases
Ligands
Models, Molecular
Molecular biology
Molecular Conformation
Morphology
Pharmaceutical Preparations - chemistry
Pharmacology
Physical Chemistry
Proteins
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Summary:This paper describes a new and efficient stochastic conformational sampling method for generating a range of low-energy molecule conformations. Sampling can be tailored to a specific structural domain (e.g., peptides) by extracting torsional profiles from specific datasets and subsequently applying them to target molecules outside the reference set. The programs that handle creation of the knowledge-based torsional profiles and conformer generation per se are separate and so can be used independently or sequentially, depending on the task at hand. The conformational ensembles produced are contrasted with those generated using local minimization approaches. They are also quantitatively compared with a broader range of techniques in terms of speed and the ability to reproduce bound ligand conformations found in complexes with proteins.
ISSN:0920-654X
1573-4951
DOI:10.1007/s10822-007-9156-5