Docking molecules by families to increase the diversity of hits in database screens: Computational strategy and experimental evaluation

Su, Andrew I.; Lorber, David M.; Weston, G. S.; Baase, W.A.; Matthews, Brian W.; Shoichet, Brian K.

doi:10.1002/1097-0134(20010201)42:2<279::aid-prot150>3.0.co;2-u

Cited by 50 publications

(42 citation statements)

References 40 publications

(72 reference statements)

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“…To our knowledge, this is the first time that this question is being addressed, although the study of Su et al shares some similarities with ours, in that they also used only ligand family representatives when ranking their docking scores, in an attempt to increase the diversity of the top-ranking ligands. 27 We have found that the ranks (and energies) of our naïvely constructed structural representatives correlate well with the mean ranks of the clusters they represent. However, there is great variation in the quality of the clusters, which is reflected in an equally varied distribution of ranks and energies of the compounds within a cluster.…”

Section: Discussionmentioning

confidence: 75%

“…This selection of human metabolites lowers the number of docking simulations that need to be performed in order to obtain the maximum amount of information about the nature of a protein's cognate ligand with minimum computation. Our approach shares some similarity with that suggested by Su et al 27 Their approach involved defining families of ligands sharing the same scaffold and docking all members of all families to a given protein, but only ranking the best-scoring member of each ligand family. This method aimed at improving diversity in the top ranks by allowing only one member of each family in the ranked list.…”

Section: Introductionmentioning

confidence: 93%

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Molecular Docking for Substrate Identification: The Short-Chain Dehydrogenases/Reductases

Favia

Nobeli

Glaser

et al. 2008

Journal of Molecular Biology

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Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases. We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking >900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures. Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naive for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.

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Section: Discussionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 93%

Molecular Docking for Substrate Identification: The Short-Chain Dehydrogenases/Reductases

Favia

Nobeli

Glaser

et al. 2008

Journal of Molecular Biology

View full text Add to dashboard Cite

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“…Correspondingly, the L99A cavity only binds relatively small, hydrophobic ligands, typically aryl hydrocarbons, of which 56 have been characterized. 31,32 Importantly for this study, the cavity can undergo significant conformational changes, ranging from movement of side-chains to unwinding of a short helix, to accommodate larger ligands. This highly simplified site removes several of the ambiguities that come when docking to more complicated sites, and yet retains some of the specificity determinants of these sites, making it a model binding site for testing docking algorithms (see Wei et al 33 and Morton et al 31 for further discussion).…”

Section: Introductionmentioning

confidence: 99%

Testing a Flexible-receptor Docking Algorithm in a Model Binding Site

Wei

Weaver

Ferrari

et al. 2004

Journal of Molecular Biology

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183

202

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“…The fast docking intermediate step has been implemented and validated (see Methods) using L99A lysozyme-ligand complexes for which binding data and X-ray structures are available [20,23,25]. The L99A lysozyme mutant is a very useful model to study binding to protein cavities because it has been thoroughly characterised both structurally [20], in its ability to bind ligands [25,67], and as a tool for testing scoring functions and new docking algorithms [68,69] The best docking and scoring conditions found for a training set of 12 L99A lysozyme ligands (6 binders and 6 non-binders) are listed in Table 1. The Consensus Scoring function provided by the Cerius 2 program (see Methods) performed well with the training set and then with the 40 lysozyme-related ligands (see Methods), but when applied to the compounds in the NCI database (supplemented with the 40 lysozyme-related ligands) that had been selected using the first step filters, it produced only a small enrichment.…”

Section: Resultsmentioning

confidence: 99%

Design of Ligand Binding to an Engineered Protein Cavity Using Virtual Screening and Thermal Up-shift Evaluation

Machicado

López-Llano

Cuesta‐López

et al. 2005

J Comput Aided Mol Des

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Proteins could be used to carry and deliver small compounds. As a tool for designing ligand binding sites in protein cores, a three-step virtual screening method is presented that has been optimised using existing data on T4 lysozyme complexes and tested in a newly engineered cavity in flavodoxin. The method can pinpoint, in large databases, ligands of specific protein cavities. In the first step, physico-chemical filters are used to screen the library and discard a majority of compounds. In the second step, a flexible, fast docking procedure is used to score and select a smaller number of compounds as potential binders. In the third step, a finer method is used to dock promising molecules of the hit list into the protein cavity, and an optimised free energy function allows discarding the few false positives by calculating the affinity of the modelled complexes. To demonstrate the portability of the method, several cavities have been designed and engineered in the flavodoxin from Anabaena PCC 7119, and the W66F/L44A double mutant has been selected as a suitable host protein. The NCI database has then been screened for potential binders, and the binding to the engineered cavity of five promising compounds and three tentative non-binders has been experimentally tested by thermal up-shift assays and spectroscopic titrations. The five tentative binders (some apolar and some polar), unlike the three tentative non-binders, are shown to bind to the host mutant and, importantly, not to bind to the wild type protein. The three-step virtual screening method developed can thus be used to identify ligands of buried protein cavities. We anticipate that the method could also be used, in a reverse manner, to identify natural or engineerable protein cavities for the hosting of ligands of interest.

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Docking molecules by families to increase the diversity of hits in database screens: Computational strategy and experimental evaluation

Cited by 50 publications

References 40 publications

Molecular Docking for Substrate Identification: The Short-Chain Dehydrogenases/Reductases

Molecular Docking for Substrate Identification: The Short-Chain Dehydrogenases/Reductases

Testing a Flexible-receptor Docking Algorithm in a Model Binding Site

Design of Ligand Binding to an Engineered Protein Cavity Using Virtual Screening and Thermal Up-shift Evaluation

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