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Evaluation of 11 scoring functions performance on matrix metalloproteinases.

Shamsara J - Int J Med Chem (2014)

Bottom Line: Finally, we have developed a PCA model from the best functions.Consensus scorings did not show statistically significant superiority over the other scorings methods in correlation study while PCA model which consists of ChemScore, Autodock, and DSX improved overall enrichment.Outcome of this study could be useful for the setting up of a suitable scoring protocol, resulting in enrichment of MMPs inhibitors.

View Article: PubMed Central - PubMed

Affiliation: Pharmaceutical Research Center, Mashhad University of Medical Sciences, Mashhad 91775-1365, Iran.

ABSTRACT
Matrix metalloproteinases (MMPs) have distinctive roles in various physiological and pathological processes such as inflammatory diseases and cancer. This study explored the performance of eleven scoring functions (D-Score, G-Score, ChemScore, F-Score, PMF-Score, PoseScore, RankScore, DSX, and X-Score and scoring functions of AutoDock4.1 and AutoDockVina). Their performance was judged by calculation of their correlations to experimental binding affinities of 3D ligand-enzyme complexes of MMP family. Furthermore, they were evaluated for their ability in reranking virtual screening study results performed on a member of MMP family (MMP-12). Enrichment factor at different levels and receiver operating characteristics (ROC) curves were used to assess their performance. Finally, we have developed a PCA model from the best functions. Of the scoring functions evaluated, F-Score, DSX, and ChemScore were the best overall performers in prediction of MMPs-inhibitors binding affinities while ChemScore, Autodock, and DSX had the best discriminative power in virtual screening against the MMP-12 target. Consensus scorings did not show statistically significant superiority over the other scorings methods in correlation study while PCA model which consists of ChemScore, Autodock, and DSX improved overall enrichment. Outcome of this study could be useful for the setting up of a suitable scoring protocol, resulting in enrichment of MMPs inhibitors.

No MeSH data available.


Related in: MedlinePlus

ROC curve of (a) Glide-Score, (b) DSX, (c) Autodock, (d) ChemScore, and (e) PC1 for Glide (SP) virtual screening results.
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fig3: ROC curve of (a) Glide-Score, (b) DSX, (c) Autodock, (d) ChemScore, and (e) PC1 for Glide (SP) virtual screening results.

Mentions: ROC curve plots specificity against sensitivity at different cutoff values (in this case, different scores). The enrichment ability of scoring functions was assessed on a set of docked compounds including known inhibitors and decoys. Table 3 demonstrated the obtained EFs at different level for various scoring function on docked poses with either Glide standard precision or Glide HTVS protocols. In addition to Glide-native scoring function, ChemScore, Autodock, and DSX showed better performance than other tested functions in both rescoring jobs. Figures 2 and 3 show representative ROC plots for scoring functions with the best performances among scoring programs evaluated in the enrichment study. The calculated areas under the receiver-operating characteristic curves values for each scoring program are given in Table 3. PC1 obtained from performed PCA on Autodock, DSX, and ChemScore scores led to the best EF1% and AUC for SP docking runs. Principle component 2 (PC2) was plotted against PC1 in Figure 4. As it was shown in Figure 4, PC1 has an ability to discriminate true binders from decoys, as at the left side the density of true ligands is much higher. PC2 is not very informative in this regard.


Evaluation of 11 scoring functions performance on matrix metalloproteinases.

Shamsara J - Int J Med Chem (2014)

ROC curve of (a) Glide-Score, (b) DSX, (c) Autodock, (d) ChemScore, and (e) PC1 for Glide (SP) virtual screening results.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4291136&req=5

fig3: ROC curve of (a) Glide-Score, (b) DSX, (c) Autodock, (d) ChemScore, and (e) PC1 for Glide (SP) virtual screening results.
Mentions: ROC curve plots specificity against sensitivity at different cutoff values (in this case, different scores). The enrichment ability of scoring functions was assessed on a set of docked compounds including known inhibitors and decoys. Table 3 demonstrated the obtained EFs at different level for various scoring function on docked poses with either Glide standard precision or Glide HTVS protocols. In addition to Glide-native scoring function, ChemScore, Autodock, and DSX showed better performance than other tested functions in both rescoring jobs. Figures 2 and 3 show representative ROC plots for scoring functions with the best performances among scoring programs evaluated in the enrichment study. The calculated areas under the receiver-operating characteristic curves values for each scoring program are given in Table 3. PC1 obtained from performed PCA on Autodock, DSX, and ChemScore scores led to the best EF1% and AUC for SP docking runs. Principle component 2 (PC2) was plotted against PC1 in Figure 4. As it was shown in Figure 4, PC1 has an ability to discriminate true binders from decoys, as at the left side the density of true ligands is much higher. PC2 is not very informative in this regard.

Bottom Line: Finally, we have developed a PCA model from the best functions.Consensus scorings did not show statistically significant superiority over the other scorings methods in correlation study while PCA model which consists of ChemScore, Autodock, and DSX improved overall enrichment.Outcome of this study could be useful for the setting up of a suitable scoring protocol, resulting in enrichment of MMPs inhibitors.

View Article: PubMed Central - PubMed

Affiliation: Pharmaceutical Research Center, Mashhad University of Medical Sciences, Mashhad 91775-1365, Iran.

ABSTRACT
Matrix metalloproteinases (MMPs) have distinctive roles in various physiological and pathological processes such as inflammatory diseases and cancer. This study explored the performance of eleven scoring functions (D-Score, G-Score, ChemScore, F-Score, PMF-Score, PoseScore, RankScore, DSX, and X-Score and scoring functions of AutoDock4.1 and AutoDockVina). Their performance was judged by calculation of their correlations to experimental binding affinities of 3D ligand-enzyme complexes of MMP family. Furthermore, they were evaluated for their ability in reranking virtual screening study results performed on a member of MMP family (MMP-12). Enrichment factor at different levels and receiver operating characteristics (ROC) curves were used to assess their performance. Finally, we have developed a PCA model from the best functions. Of the scoring functions evaluated, F-Score, DSX, and ChemScore were the best overall performers in prediction of MMPs-inhibitors binding affinities while ChemScore, Autodock, and DSX had the best discriminative power in virtual screening against the MMP-12 target. Consensus scorings did not show statistically significant superiority over the other scorings methods in correlation study while PCA model which consists of ChemScore, Autodock, and DSX improved overall enrichment. Outcome of this study could be useful for the setting up of a suitable scoring protocol, resulting in enrichment of MMPs inhibitors.

No MeSH data available.


Related in: MedlinePlus