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Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand.

DeLuca S, Khar K, Meiler J - PLoS ONE (2015)

Bottom Line: The docking success rate is improved by 10-15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150.As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries.We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the 'high-resolution' full atom refinement step.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Vanderbilt University, Nashville, TN, United States of America.

ABSTRACT
RosettaLigand has been successfully used to predict binding poses in protein-small molecule complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small molecule (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial 'low-resolution' docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10-15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150. The average time to generate a model is reduced from 50 seconds to 10 seconds. As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the 'high-resolution' full atom refinement step.

No MeSH data available.


Related in: MedlinePlus

The fraction of protein systems in which the lowest scoring model has a RMSD of less than 2.0 Å to the native structure as function of the total number of structures generated using the four RosettaLigand docking algorithms and three starting protein models.A) Experimental structures, B) models in which only the sidechains are repacked, and C) models in which all atoms have been minimized using the Rosetta energy function. A large pool of models was generated, 20 random subsamples were taken for each point, and the means and standard deviation are plotted. Docking protocols which make use of the Transform algorithm are reliably converged after approximately 150 models (dotted line).
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pone.0132508.g005: The fraction of protein systems in which the lowest scoring model has a RMSD of less than 2.0 Å to the native structure as function of the total number of structures generated using the four RosettaLigand docking algorithms and three starting protein models.A) Experimental structures, B) models in which only the sidechains are repacked, and C) models in which all atoms have been minimized using the Rosetta energy function. A large pool of models was generated, 20 random subsamples were taken for each point, and the means and standard deviation are plotted. Docking protocols which make use of the Transform algorithm are reliably converged after approximately 150 models (dotted line).

Mentions: Fig 4 and Fig 5 plot the fraction of protein/ligand complexes for which the lowest scoring binding position is less than 2.0 Å RMSD as a function of total Central Processing Unit (CPU) time and number of models generated, respectively. Thus the choice of the initial placement algorithm is far more important than choice of low resolution scoring method or refinement method. Docking protocols which make use of the Transform initial placement algorithm can reliably dock an additional 10–15% of models within roughly 15 minutes of CPU time, or 150 models, compared to protocols which use the TransRot initial placement algorithm. The choice of refinement algorithm appears to play little role in the overall performance of the docking protocol, except in the case of the previously published protocol (TransRot/MCM), in which case docking performance begins to approach the Transform based protocols after roughly 800–1000 models have been generated (Fig 5). This observed behavior is consistent with previously published studies of RosettaLigand performance using this protocol [13,15,20].


Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand.

DeLuca S, Khar K, Meiler J - PLoS ONE (2015)

The fraction of protein systems in which the lowest scoring model has a RMSD of less than 2.0 Å to the native structure as function of the total number of structures generated using the four RosettaLigand docking algorithms and three starting protein models.A) Experimental structures, B) models in which only the sidechains are repacked, and C) models in which all atoms have been minimized using the Rosetta energy function. A large pool of models was generated, 20 random subsamples were taken for each point, and the means and standard deviation are plotted. Docking protocols which make use of the Transform algorithm are reliably converged after approximately 150 models (dotted line).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4514752&req=5

pone.0132508.g005: The fraction of protein systems in which the lowest scoring model has a RMSD of less than 2.0 Å to the native structure as function of the total number of structures generated using the four RosettaLigand docking algorithms and three starting protein models.A) Experimental structures, B) models in which only the sidechains are repacked, and C) models in which all atoms have been minimized using the Rosetta energy function. A large pool of models was generated, 20 random subsamples were taken for each point, and the means and standard deviation are plotted. Docking protocols which make use of the Transform algorithm are reliably converged after approximately 150 models (dotted line).
Mentions: Fig 4 and Fig 5 plot the fraction of protein/ligand complexes for which the lowest scoring binding position is less than 2.0 Å RMSD as a function of total Central Processing Unit (CPU) time and number of models generated, respectively. Thus the choice of the initial placement algorithm is far more important than choice of low resolution scoring method or refinement method. Docking protocols which make use of the Transform initial placement algorithm can reliably dock an additional 10–15% of models within roughly 15 minutes of CPU time, or 150 models, compared to protocols which use the TransRot initial placement algorithm. The choice of refinement algorithm appears to play little role in the overall performance of the docking protocol, except in the case of the previously published protocol (TransRot/MCM), in which case docking performance begins to approach the Transform based protocols after roughly 800–1000 models have been generated (Fig 5). This observed behavior is consistent with previously published studies of RosettaLigand performance using this protocol [13,15,20].

Bottom Line: The docking success rate is improved by 10-15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150.As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries.We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the 'high-resolution' full atom refinement step.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, Vanderbilt University, Nashville, TN, United States of America.

ABSTRACT
RosettaLigand has been successfully used to predict binding poses in protein-small molecule complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small molecule (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial 'low-resolution' docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10-15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150. The average time to generate a model is reduced from 50 seconds to 10 seconds. As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the 'high-resolution' full atom refinement step.

No MeSH data available.


Related in: MedlinePlus