Elastic Potential Grids: Accurate and Efficient Representation of Intermolecular Interactions for Fully Flexible Docking

Kazemi, Sina; Krüger, Dennis M.; Sirockin, Finton; Gohlke, Holger

doi:10.1002/cmdc.200900146

Cited by 31 publications

(42 citation statements)

References 63 publications

(79 reference statements)

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“…For comparison, for proteinÀligand complexes, a ratio k/G = 30.00 ( 0.24 has been determined. 23 This result is encouraging because it demonstrates that the parametrization of our approach is transferable between different RNAÀligand complex classes, irrespective of the kinds of conformational changes observed. At the same time, it reveals that for conformational changes due to ligand binding, which are modeled in terms of elastic deformations of binding site regions, almost identical parameters are obtained for proteins and RNA.…”

Section: Introductionmentioning

confidence: 58%

“…As the ligand had not been purposefully distorted with respect to its bound conformation in the preparation step, it may thus not come as a surprise that good starting structures for the subsequent energy minimization step were generated that way. 16 As already observed in the case of proteinÀligand complexes, 23 it is the type of motion that influences the docking success in the case of elastic potential grids more strongly than the magnitude of motion. While our approach does allow one to consider backbone and base motions simultaneously, limitations of the approach become obvious if base movements are predominantly governed by rotational flip motions or if nucleotide movements lead to an exchange of interaction types, for example, H-bond donor vs acceptor.…”

Section: Lettermentioning

confidence: 81%

“…In contrast to using static 3D grids, that way new RNA conformations can be accommodated during a docking run without the need to recalculate potential field values. A more technical description of the method is given in the Supporting Information of ref 23.…”

Section: Introductionmentioning

confidence: 99%

“…For parametrizing the elastic potential grid, the relation between the force constant k and the modulus of rigidity G was determined by a training procedure as described in ref 23. In agreement with ref 23, Lam e's constant λ, which is linked to the description of expansion or contraction of an elastic body perpendicular to an applied force, is set to zero.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in the present study, we set out to evaluate whether our approach of elastic potential grids, 23 previously developed for proteinÀligand docking, provides an accurate and efficient ABSTRACT: The highly flexible nature of RNA provides a formidable challenge for structure-based drug design approaches that target RNA. We introduce an approach for modeling target conformational changes in RNAÀligand docking based on potential grids that are represented as elastic bodies using Navier's equation.…”

mentioning

confidence: 99%

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Target Flexibility in RNA−Ligand Docking Modeled by Elastic Potential Grids

Krüger

Bergs

Kazemi

et al. 2011

ACS Med. Chem. Lett.

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The highly flexible nature of RNA provides a formidable challenge for structure-based drug design approaches that target RNA. We introduce an approach for modeling target conformational changes in RNA−ligand docking based on potential grids that are represented as elastic bodies using Navier's equation. This representation provides an accurate and efficient description of RNA−ligand interactions even in the case of a moving RNA structure. When applied to a data set of 17 RNA−ligand complexes, filtered out of the largest validation data set used for RNA−ligand docking so far, the approach is twice as successful as docking into an apo structure and still half as successful as redocking to the holo structure. The approach allows considering RNA movements of up to 6 Å rmsd and is based on a uniform and robust parametrization of the properties of the elastic potential grids, so that the approach is applicable to different RNA−ligand complex classes.

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Section: Introductionmentioning

confidence: 58%

Section: Lettermentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Target Flexibility in RNA−Ligand Docking Modeled by Elastic Potential Grids

Krüger

Bergs

Kazemi

et al. 2011

ACS Med. Chem. Lett.

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Protein Flexibility in In Silico Screening

Gohlke

Kopitz

2010

Burger's Medicinal Chemistry and Drug Discovery

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We summarize computational approaches in structure‐based ligand design (SBLD) and in silico screening that address issues of protein flexibility and mobility. In particular, we consider how protein plasticity can be incorporated into docking strategies. As a first requirement, one needs to detect what can move and how. Moving protein parts can be identified from experimental information as well as established computational techniques such as molecular dynamics (MD) simulations, graph theoretical and geometry‐based approaches, or harmonic analysis‐based methods. Second, this knowledge needs to be transformed into a docking algorithm. A multitude of approaches considering protein mobility has been introduced recently, with motions modeled either implicitly or explicitly. In the latter case, one can further distinguish between modeling of side‐chain‐only motions and motions including backbone changes. In all cases, accuracy needs to be balanced against efficiency. Case studies for which the inclusion of protein plasticity was crucial to success are noted along these lines. This allows us to identify scope and limitations of the current approaches, as well as guidelines for further developments.

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