Dataset Information

Evaluation of 11 scoring functions performance on matrix metalloproteinases.

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.

SUBMITTER: Shamsara J

PROVIDER: S-EPMC4291136 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Evaluation of 11 scoring functions performance on matrix metalloproteinases.

Shamsara Jamal J

International journal of medicinal chemistry 20141225

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 fam ...[more]

PMID: 25610645

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Project description:In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning algorithms to learn the network structures. The suboptimal structures found by the approximation methods have unknown quality and may affect the reliability of their conclusions. Our study uses an optimal algorithm to learn Bayesian network structures from datasets generated from a set of gold standard Bayesian networks. Because all optimal algorithms always learn equivalent networks, this ensures that only the choice of scoring function affects the learned networks. Another shortcoming of the previous studies stems from their use of random synthetic networks as test cases. There is no guarantee that these networks reflect real-world data. We use real-world data to generate our gold-standard structures, so our experimental design more closely approximates real-world situations. A major finding of our study suggests that, in contrast to results reported by several prior works, the Minimum Description Length (MDL) (or equivalently, Bayesian information criterion (BIC)) consistently outperforms other scoring functions such as Akaike's information criterion (AIC), Bayesian Dirichlet equivalence score (BDeu), and factorized normalized maximum likelihood (fNML) in recovering the underlying Bayesian network structures. We believe this finding is a result of using both datasets generated from real-world applications rather than from random processes used in previous studies and learning algorithms to select high-scoring structures rather than selecting random models. Other findings of our study support existing work, e.g., large sample sizes result in learning structures closer to the true underlying structure; the BDeu score is sensitive to the parameter settings; and the fNML performs pretty well on small datasets. We also tested a greedy hill climbing algorithm and observed similar results as the optimal algorithm.

Dataset Information

Evaluation of 11 scoring functions performance on matrix metalloproteinases.

Publications

Evaluation of 11 scoring functions performance on matrix metalloproteinases.

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