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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

Scatter plots showing the weak correlation between experimental-log(Kd) and predicted Rosetta energy score for models in the 43 protein benchmark.Scores from models generated using the Transform/MCM protocol are in red while scores from models generated using the Transform/MCM protocol are in black.
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pone.0132508.g008: Scatter plots showing the weak correlation between experimental-log(Kd) and predicted Rosetta energy score for models in the 43 protein benchmark.Scores from models generated using the Transform/MCM protocol are in red while scores from models generated using the Transform/MCM protocol are in black.

Mentions: While the Transform algorithm results in slightly lower scores, it has no impact on the correlation between Rosetta score and experimentally derived Kd values (Fig 8). The correlation coefficient between-log(Kd) and the Rosetta energy of the models made with the TransRot/MCM protocol is 0.49, while the correlation coefficient for models made with Transform/MCM is protocol improves to 0.54. This observation is in line with previous published studies [14,20] which indicates that the Rosetta energy function, as well as other popular energy functions [11,22] are frequently unable to effectively predict binding affinity.


Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand.

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

Scatter plots showing the weak correlation between experimental-log(Kd) and predicted Rosetta energy score for models in the 43 protein benchmark.Scores from models generated using the Transform/MCM protocol are in red while scores from models generated using the Transform/MCM protocol are in black.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132508.g008: Scatter plots showing the weak correlation between experimental-log(Kd) and predicted Rosetta energy score for models in the 43 protein benchmark.Scores from models generated using the Transform/MCM protocol are in red while scores from models generated using the Transform/MCM protocol are in black.
Mentions: While the Transform algorithm results in slightly lower scores, it has no impact on the correlation between Rosetta score and experimentally derived Kd values (Fig 8). The correlation coefficient between-log(Kd) and the Rosetta energy of the models made with the TransRot/MCM protocol is 0.49, while the correlation coefficient for models made with Transform/MCM is protocol improves to 0.54. This observation is in line with previous published studies [14,20] which indicates that the Rosetta energy function, as well as other popular energy functions [11,22] are frequently unable to effectively predict binding affinity.

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