Ligand placement based on prior structures: the guided ligand‐replacement method. (1st January 2014)

• Record Type: Journal Article
• Authors: Klei, Herbert E.; Moriarty, Nigel W.; Echols, Nathaniel; Terwilliger, Thomas C.; Baldwin, Eric T.; Pokross, Matt; Posy, Shana; Adams, Paul D.
• Is Part Of: Acta crystallographica. Volume 70:Part 1(2014:Jan.)
• Page Start: 134
• Page End: 143
• Publication Date: 2014-01-01
• Subjects: Biomolecules -- Structure -- Periodicals; Physical biochemistry -- Periodicals; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; 572
• DOI: 10.1107/S1399004713030071
• Languages: English
• ISSNs: 0907-4449

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>The process of iterative structure‐based drug design involves the X‐ray crystal structure determination of upwards of 100 ligands with the same general scaffold (<italic>i.e.</italic> chemotype) complexed with very similar, if not identical, protein targets. In conjunction with insights from computational models and assays, this collection of crystal structures is analyzed to improve potency, to achieve better selectivity and to reduce liabilities such as absorption, distribution, metabolism, excretion and toxicology. Current methods for modeling ligands into electron‐density maps typically do not utilize information on how similar ligands bound in related structures. Even if the electron density is of sufficient quality and resolution to allow <italic>de novo</italic> placement, the process can take considerable time as the size, complexity and torsional degrees of freedom of the ligands increase. A new module, <italic>Guided Ligand Replacement</italic> (<italic>GLR</italic>), was developed in <italic>Phenix</italic> to increase the ease and success rate of ligand placement when prior protein–ligand complexes are available. At the heart of <italic>GLR</italic> is an algorithm based on graph theory that associates atoms in the target ligand with analogous atoms in the reference ligand. Based on this correspondence, a set of coordinates is generated for the target ligand. <italic>GLR</italic> is especially useful in two situations: (i) modeling a series of large, flexible, complicated or macrocyclic ligands in successive structures and (ii) modeling ligands as part of a refinement pipeline that can automatically select a reference structure. Even in those cases for which no reference structure is available, if there are multiple copies of the bound ligand per asymmetric unit <italic>GLR</italic> offers an efficient way to complete the model after the first ligand has been placed. In all of these applications, <italic>GLR</italic> leverages prior knowledge from earlier structures to facilitate ligand placement in the current structure.</p> </abstract>