CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

Dunbar, James B.; Smith, Richard; Yang, Chao; Ung, Peter M.U.; Lexa, Katrina W.; Khazanov, Nickolay A.; Stuckey, Jeanne A.; Wang, Shaomeng; Carlson, Heather A.

doi:10.1021/ci200082t

Cited by 138 publications

(228 citation statements)

References 43 publications

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“…The SIE scoring function parametrized in this manner achieves a reasonable transferability across a wide variety of protein-ligand systems, consistently returning absolute binding affinities within the experimental range, as demonstrated by test cases published in the literature [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. External testing of the standard SIE parametrization in the CSAR-2010 scoring challenge consisting in a curated dataset of 343 protein-ligand complexes diverse with respect to ligands and targets [32,33], afforded binding affinity predictions with a mean-unsigned-error (MUE) of about 2 kcal/mol [34].…”

Section: Introductionmentioning

confidence: 76%

“…Using an efficient filtering method, the program exhaustively enumerates, scores and ranks all the ligand poses that do not overlap with the protein. The weighted 5-term scoring function used for docking was trained to recover the most native states using 343 proteinligand complexes from the curated CSAR dataset [32]. The scoring function includes a van der Walls 6-12 LennardJones potential, a Coulomb interaction term, an explicit H-bond term, which considers donor and acceptor orientations, and two surface and polar-surface complementarity terms.…”

Section: Wilma Dockingmentioning

confidence: 99%

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Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Sulea

Hogues

Purisima

2011

J Comput Aided Mol Des

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We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host-guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.

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Section: Introductionmentioning

confidence: 76%

Section: Wilma Dockingmentioning

confidence: 99%

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Sulea

Hogues

Purisima

2011

J Comput Aided Mol Des

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“…The newer 4-term scoring function, 5 which was trained to recover the most native states using 320 protein−ligand complexes from the curated CSAR-2010 data set, 15 …”

Section: ■ Methodsmentioning

confidence: 99%

Evaluation of the Wilma-SIE Virtual Screening Method in Community Structure–Activity Resource 2013 and 2014 Blind Challenges

Hogues

Sulea

Purisima

2015

J. Chem. Inf. Model.

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Prospective assessments of the Wilma-SIE (solvated interaction energy) platform for ligand docking and ranking were performed during the 2013 and 2014 editions of the Community Structure–Activity Resource (CSAR) blind challenge. Diverse targets like a steroid-binding protein, a serine protease (factor Xa), a tyrosine kinase (Syk), and a nucleotide methyltransferase (TrmD) were included. Pose selection was achieved with high precision; in all 24 tests Wilma-SIE top-ranked the native pose among carefully generated sets of decoy conformations. Good separation for the native pose was also observed indicating robustness in pose scoring. Cross-docking was also accomplished with high accuracy for the various systems, with ligand median-RMSD values around 1 Å from the crystal structures. Larger deviations were occasionally obtained due to the rigid-target approach even if multiple target structures were used. Affinity ranking of congeneric ligands after cross-docking was reasonable for three of the four systems, with Spearman ranking coefficients around 0.6. Poor affinity ranking for FXa is possibly due to missing structural domains, which are present during measurements. Assignment of protonation states is critical for affinity scoring with the SIE function, as shown here for the Syk system. Including the FiSH model improved cross-docking but worsened affinity predictions, pointing to the need for further fine-tuning of this newer solvation model. The consistently strong performance of the Wilma-SIE platform in recent CSAR and SAMPL blind challenges validates its applicability for virtual screening on a broad range of molecular targets.

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“…Although this ranking can still be better than random, typically only a few compounds are selected from those scoring best, and many of them often turn out to be inactive. 73,74 Several options are available for combining dockingbased virtual screening with pharmacophore-based virtual screening:…”

Section: Pharmacophore Methods In Docking Simulationsmentioning

confidence: 99%

Pharmacophore modeling: advances, limitations, and current utility in drug discovery

Qing¹,

Lee²,

Raeymaecker³

et al. 2014

JRLCR

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Pharmacophore modeling is a successful yet very diverse subfield of computer-aided drug design. The concept of the pharmacophore has been widely applied to the rational design of novel drugs. In this paper, we review the computational implementation of this concept and its common usage in the drug discovery process. Pharmacophores can be used to represent and identify molecules on a 2D or 3D level by schematically depicting the key elements of molecular recognition. The most common application of pharmacophores is virtual screening, and different strategies are possible depending on the prior knowledge. However, the pharmacophore concept is also useful for ADME-tox modeling, side effect, and off-target prediction as well as target identification. Furthermore, pharmacophores are often combined with molecular docking simulations to improve virtual screening. We conclude this review by summarizing the new areas where significant progress may be expected through the application of pharmacophore modeling; these include protein-protein interaction inhibitors and protein design. Keywords: ADME-tox, computer-aided drug design, pharmacophore fingerprint, protein design, virtual screening What is computer-aided drug design?Drug design is an expensive and laborious process of developing new medicine. This process has its origin in herbal remedies dating back millennia.1 Only since the last century have drugs had a (semi)synthetic origin.2 The first hit compounds often lack both potency and safety, and must therefore be optimized. While historically this was a trial-and-error process, 3,4 soon rational strategies were developed to improve potency. 5,6 As with any data handling procedures, computers have become a more prominent and ubiquitous tool in drug discovery since the 1980s. The crossover between computational and pharmaceutical research is typically designated computer-aided drug design (CADD). 8,9 CADD covers a broad range of applications spanning the drug discovery pipeline, although these are highly clustered in the early phases. The main purpose of CADD is to speed up and rationalize the drug design process while reducing costs. 10 The aim of the earliest phase in drug discovery is to identify the first hit compounds, which is sometimes attempted by high-throughput screening (HTS), the testing of many thousands of compounds with a suitable activity assay. The in silico counterpart of in vitro HTS is referred to as virtual screening and aims at filtering libraries of molecules using computational methods to prioritize those most likely to be active for a given

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CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

Cited by 138 publications

References 43 publications

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

Evaluation of the Wilma-SIE Virtual Screening Method in Community Structure–Activity Resource 2013 and 2014 Blind Challenges

Pharmacophore modeling: advances, limitations, and current utility in drug discovery

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