Limits...
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

Kernel Density Estimate curves showing the time necessary to generate a single model using the four RosettaLigand protocols.TransRot/MCM is the protocol previously published by Davis et al. [15].
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4514752&req=5

pone.0132508.g003: Kernel Density Estimate curves showing the time necessary to generate a single model using the four RosettaLigand protocols.TransRot/MCM is the protocol previously published by Davis et al. [15].

Mentions: Fig 3 shows the time necessary to generate a single model with each of the four tested algorithms. The average time needed to generate a model using the previously published TransRot/MCM protocol is 49.4 seconds per model. Changing the Refinement protocol from MCM to MIN reduces the time per model to 33.3 seconds, and changing both the refinement protocol to MIN and the initial placement model from TransRot to Transform further reduces the time per model to 9.3 seconds. The per-model timing is not uniformly distributed, and varies based on the docking protocol used. The standard deviations of the time to generate models using the Transform algorithms are lower than those of the TransRot algorithms. Specifically, the time distribution for the generation of Transform/MCM models has a standard deviation of 10.5 seconds and the standard deviation of the distribution for Transform/MIN is 4.0 seconds. On the other hand, the timing distribution of the TransRot/MCM models has a standard deviation of 26.6 seconds, and the timing distribution of the TransRot/MIN models has a standard deviation of 21.0 seconds.


Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand.

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

Kernel Density Estimate curves showing the time necessary to generate a single model using the four RosettaLigand protocols.TransRot/MCM is the protocol previously published by Davis et al. [15].
© Copyright Policy
Related In: Results  -  Collection

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

pone.0132508.g003: Kernel Density Estimate curves showing the time necessary to generate a single model using the four RosettaLigand protocols.TransRot/MCM is the protocol previously published by Davis et al. [15].
Mentions: Fig 3 shows the time necessary to generate a single model with each of the four tested algorithms. The average time needed to generate a model using the previously published TransRot/MCM protocol is 49.4 seconds per model. Changing the Refinement protocol from MCM to MIN reduces the time per model to 33.3 seconds, and changing both the refinement protocol to MIN and the initial placement model from TransRot to Transform further reduces the time per model to 9.3 seconds. The per-model timing is not uniformly distributed, and varies based on the docking protocol used. The standard deviations of the time to generate models using the Transform algorithms are lower than those of the TransRot algorithms. Specifically, the time distribution for the generation of Transform/MCM models has a standard deviation of 10.5 seconds and the standard deviation of the distribution for Transform/MIN is 4.0 seconds. On the other hand, the timing distribution of the TransRot/MCM models has a standard deviation of 26.6 seconds, and the timing distribution of the TransRot/MIN models has a standard deviation of 21.0 seconds.

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