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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

Plots comparing the performance of various docking protocols when docking ligands into relaxed structures.A) For RMSD versus RMSD plots 20 samples of 150 models were collected, and the average of the RMSD of the lowest scoring model is plotted for each protein/ligand system. The standard deviation of these 20 samples is shown with error bars. Dotted lines indicate the 2.0 Å RMSD cutoff used to classify correct vs incorrect binding positions. B) For score versus score plots the change in average all-atom Rosetta score of the lowest scoring model generated by several pairs of docking algorithms.
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pone.0132508.g006: Plots comparing the performance of various docking protocols when docking ligands into relaxed structures.A) For RMSD versus RMSD plots 20 samples of 150 models were collected, and the average of the RMSD of the lowest scoring model is plotted for each protein/ligand system. The standard deviation of these 20 samples is shown with error bars. Dotted lines indicate the 2.0 Å RMSD cutoff used to classify correct vs incorrect binding positions. B) For score versus score plots the change in average all-atom Rosetta score of the lowest scoring model generated by several pairs of docking algorithms.

Mentions: Fig 6A compares the performance of several of the tested RosettaLigand protocols, and provides further insight into the impact of the various components of the protocol on overall performance. The RMSD versus RMSD plots illustrate specific performance differences comparison between pairs of Rosetta protocols when 1000 models are generated. When the original TransRot initial placement algorithm is used, minimal improvement is observed when the MCM refinement algorithm is used as compared to MIN initial placement (Left). Comparison of the TransRot and Transform initial placement (Center) shows substantial improvement, with 24/43 proteins having improved RMSD, and 15/43 cross the 2.0 Å threshold. Comparison of the MCM and MIN refinement algorithms in combination with the Transform initial placement algorithm shows the two refinement algorithms have nearly identical performance (Right). Improvements in RosettaLigand performance are driven by the new initial placement algorithm.


Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand.

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

Plots comparing the performance of various docking protocols when docking ligands into relaxed structures.A) For RMSD versus RMSD plots 20 samples of 150 models were collected, and the average of the RMSD of the lowest scoring model is plotted for each protein/ligand system. The standard deviation of these 20 samples is shown with error bars. Dotted lines indicate the 2.0 Å RMSD cutoff used to classify correct vs incorrect binding positions. B) For score versus score plots the change in average all-atom Rosetta score of the lowest scoring model generated by several pairs of docking algorithms.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4514752&req=5

pone.0132508.g006: Plots comparing the performance of various docking protocols when docking ligands into relaxed structures.A) For RMSD versus RMSD plots 20 samples of 150 models were collected, and the average of the RMSD of the lowest scoring model is plotted for each protein/ligand system. The standard deviation of these 20 samples is shown with error bars. Dotted lines indicate the 2.0 Å RMSD cutoff used to classify correct vs incorrect binding positions. B) For score versus score plots the change in average all-atom Rosetta score of the lowest scoring model generated by several pairs of docking algorithms.
Mentions: Fig 6A compares the performance of several of the tested RosettaLigand protocols, and provides further insight into the impact of the various components of the protocol on overall performance. The RMSD versus RMSD plots illustrate specific performance differences comparison between pairs of Rosetta protocols when 1000 models are generated. When the original TransRot initial placement algorithm is used, minimal improvement is observed when the MCM refinement algorithm is used as compared to MIN initial placement (Left). Comparison of the TransRot and Transform initial placement (Center) shows substantial improvement, with 24/43 proteins having improved RMSD, and 15/43 cross the 2.0 Å threshold. Comparison of the MCM and MIN refinement algorithms in combination with the Transform initial placement algorithm shows the two refinement algorithms have nearly identical performance (Right). Improvements in RosettaLigand performance are driven by the new initial placement algorithm.

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