Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

Koes, David Ryan; Baumgartner, Matthew P.; Camacho, Carlos J.

doi:10.1021/ci300604z

Cited by 775 publications

(850 citation statements)

References 45 publications

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“…Our lab participated and obtained some of the best results in these challenges by testing a variety of strategies designed to identify the best possible receptor structure for screening, while scoring using two established scoring functions that can be found in the literature, AutoDock Vina 28 and the Custom scoring function that we previously developed for the 2012 CSAR competition. 8 Our findings underscore that molecular docking can consistently predict and score bound-like poses to a bound-like receptor (redocking). Docking to homology models is still challenging, yet predicting druggable pockets and docking to multiple models allowed us to identify the targets of digoxigenin among a set of 14 protein sequences.…”

Section: Introductionmentioning

confidence: 56%

“…1–7 However, purely computational approaches are not able to predict binding free energies. 8,9 Thus, rational or expert-guided approaches are required to improve hit rates. 6,9 To prospectively assess and benchmark methodologies, the Community Structure–Activity Resource (CSAR) developed a set of challenges to identify robust methods and to improve computational methods for drug discovery.…”

Section: Introductionmentioning

confidence: 99%

“…We also note that the in-house custom scoring function that was developed for the 2012 CSAR competition was used in some phases of the competition and will be denoted as the Custom scoring function. 8 …”

Section: Introductionmentioning

confidence: 99%

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Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

Baumgartner

Camacho

2015

J. Chem. Inf. Model.

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The 2013/2014 Community Structure–Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor–small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R2 = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.

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Section: Introductionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

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Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

Baumgartner

Camacho

2015

J. Chem. Inf. Model.

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“…3. We used the minimization software smina [82], a fork of AutoDock Vina [83] that is customized to better support energy minimization and scoring function development. When minimized against the estradiol bound structure (PDB 1QKU), there is a substantial improvement in the ranking of the true actives, as shown in Fig.…”

Section: Using the Results Of Pharmacophore Searchmentioning

confidence: 99%

Pharmacophore Modeling: Methods and Applications

Koes

2015

Methods in Pharmacology and Toxicology

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“…Using the structure of h-5-LOX with the arachidonic acid substrate removed, we modeled the binding mode of 23a and 23d by molecular docking using smina [55] and the resulting poses were reranked using the vina scoring function. Despite the large binding site (relative to the size of the compound), we observe a common binding mode for compounds 23a and 23d (as well as 23b and 23c , not shown).…”

Section: Resultsmentioning

confidence: 99%

Discovery of a novel activator of 5-lipoxygenase from an anacardic acid derived compound collection

Wisastra

Kok

Eleftheriadis

et al. 2013

Bioorganic & Medicinal Chemistry

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Lipoxygenases (LOXs) and cyclooxygenases (COXs) metabolize poly-unsaturated fatty acids into inflammatory signaling molecules. Modulation of the activity of these enzymes may provide new approaches for therapy of inflammatory diseases. In this study, we screened novel anacardic acid derivatives as modulators of human 5-LOX and COX-2 activity. Interestingly, a novel salicylate derivative 23a was identified as a surprisingly potent activator of human 5-LOX. This compound showed both non-competitive activation towards the human 5-LOX activator adenosine triphosphate (ATP) and non-essential mixed type activation against the substrate linoleic acid, while having no effect on the conversion of the substrate arachidonic acid. The kinetic analysis demonstrated a non-essential activation of the linoleic acid conversion with a KA of 8.65 μM, αKA of 0.38 μM and a β value of 1.76. It is also of interest that a comparable derivative 23d showed a mixed type inhibition for linoleic acid conversion. These observations indicate the presence of an allosteric binding site in human 5-LOX distinct from the ATP binding site. The activatory and inhibitory behavior of 23a and 23d on the conversion of linoleic compared to arachidonic acid are rationalized by docking studies, which suggest that the activator 23a stabilizes linoleic acid, whereas the larger inhibitor 23d blocks the enzyme active site.

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Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

Cited by 775 publications

References 45 publications

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

Pharmacophore Modeling: Methods and Applications

Discovery of a novel activator of 5-lipoxygenase from an anacardic acid derived compound collection

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