Docking of small ligands to low‐resolution and theoretically predicted receptor structures

Wojciechowski, Marek; Skolnick, Jeffrey

doi:10.1002/jcc.1165

Cited by 31 publications

(41 citation statements)

References 39 publications

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“…12 In Figure 9, we plot the percent of correct specific contacts for 298 proteins (native structures) for the new program versus Dolores (the test set is described in more detail in the section "test sets," it is there denoted as set "3"). The number of cases in which more than 50% of the specific contacts are correctly predicted is very different for the two algorithms: the Dolores program only achieves this level of accuracy for 27 out of 298 protein-ligand pairs.…”

Section: Known Ligand and Unknown Binding Site: Results For Unconstramentioning

confidence: 99%

“…A set of 298 proteins, derived from the set of 318 proteins, for which results of the Dolores method are published. 12 We term this the Dolores test set. We used the program Reduce 22 to place hydrogen atoms on the PDB structures of the proteins.…”

Section: Test Setsmentioning

confidence: 99%

“…The results of such a calculation are shown in Figure 9, which compares the results of the method presented in this article to those obtained using the program Dolores. 12 To be comparable to the program Dolores, we plotted the percentage of the correct specific contacts, using rigid body docking. Although the number of cases in which no contacts are correctly predicted is much higher than in the Jain data set, it shows that even the raw-coordinate data can be useful for docking.…”

Section: Unconstrained Dockingmentioning

confidence: 99%

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A scoring function for docking ligands to low‐resolution protein structures

Bindewald

Skolnick

2005

J Comput Chem

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We present a docking method that uses a scoring function for protein-ligand docking that is designed to maximize the docking success rate for low-resolution protein structures. We find that the resulting scoring function parameters are very different depending on whether they were optimized for high- or low-resolution protein structures. We show that this docking method can be successfully applied to predict the ligand-binding site of low-resolution structures. For a set of 25 protein-ligand complexes, in 76% of the cases, more than 50% of ligand-contacting residues are correctly predicted (using receptor crystal structures where the binding site is unspecified). Using decoys of the receptor structures having a 4 A RMSD from the native structure, for the same set of complexes, in 72% of the cases, we obtain at least one correctly predicted ligand-contacting residue. Furthermore, using an 81-protein-ligand set described by Jain, in 76 (93.8%) cases, the algorithm correctly predicts more than 50% of the ligand-contacting residues when native protein structures are used. Using 3 A RMSD from native decoys, in all but two cases (97.5%), the algorithm predicts at least one ligand-binding residue correctly. Finally, compared to the previously published Dolores method, for 298 protein-ligand pairs, the number of cases in which at least half of the specific contacts are correctly predicted is more than four times greater.

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Section: Known Ligand and Unknown Binding Site: Results For Unconstramentioning

confidence: 99%

Section: Test Setsmentioning

confidence: 99%

Section: Unconstrained Dockingmentioning

confidence: 99%

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A scoring function for docking ligands to low‐resolution protein structures

Bindewald

Skolnick

2005

J Comput Chem

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“…When the atomic potential is used, the number of correct predictions increases to 50 of 65. The resulting structure can be used for further analyses such as functional annotation by matching three-dimensional active-site motifs (24) or for low-resolution ligand docking (25).…”

Section: Discussionmentioning

confidence: 99%

TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints

Kihara

Koliński

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

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139

113

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The successful prediction of protein structure from amino acid sequence requires two features: an efficient conformational search algorithm and an energy function with a global minimum in the native state. As a step toward addressing both issues, a threadingbased method of secondary and tertiary restraint prediction has been developed and applied to ab initio folding. Such restraints are derived by extracting consensus contacts and local secondary structure from at least weakly scoring structures that, in some cases, can lack any global similarity to the sequence of interest. Furthermore, to generate representative protein structures, a reduced lattice-based protein model is used with replica exchange Monte Carlo to explore conformational space. We report results on the application of this methodology, termed TOUCHSTONE, to 65 proteins whose lengths range from 39 to 146 residues. For 47 (40) proteins, a cluster centroid whose rms deviation from native is below 6.5 (5) Å is found in one of the five lowest energy centroids. The number of correctly predicted proteins increases to 50 when atomic detail is added and a knowledge-based atomic potential is combined with clustered and nonclustered structures for candidate selection. The combination of the ratio of the relative number of contacts to the protein length and the number of clusters generated by the folding algorithm is a reliable indicator of the likelihood of successful fold prediction, thereby opening the way for genome-scale ab initio folding.T he inability to predict routinely the tertiary structure of a protein from its amino acid sequence remains one of the most challenging unsolved problems in biophysics. Contemporary approaches to this problem can be divided roughly into three categories of increasing complexity: (i) homology modeling (1, 2), (ii) threading (3, 4), and (iii) ab initio folding (5-9). The first two methods use the structures of already solved proteins as templates. The third, the ab initio method, does not require that an example of the fold of the protein of interest be previously solved. In principle, such an approach is very powerful; however, significant unresolved issues remain. First, there are problems with the search algorithms used to explore the protein's conformational space (10). Second, the energy functions used to evaluate the fitness of a given conformation cannot, in general, distinguish the native structure from alternative, protein-like decoys (11). To compensate for the imperfections in the energy functions, another way of selecting representative folds is required, with clustering of the structures being a promising approach (7-9). Finally, for a folding algorithm to be practical, one has to develop criteria that allow one to estimate the likelihood that a given prediction will be successful.In this article, we address each of these issues and present the results on the application of our ab initio method to a representative 65-protein test set. To restrict the protein's conformational space, we employ the SICHO (SId...

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“…High resolution structures seem not necessary for docking experiments [53], and several reports describe the successful use of homology models [54]. Even if a close protein homologue is not experimentally available, docking proved to be efficient in virtual screening, as demonstrated with GPCR for which the only template is bovine rhodopsin.…”

Section: Resultsmentioning

confidence: 97%

Refined Docking as a Valuable Tool for Lead Optimization: Application to Histamine H₃ Receptor Antagonists

Levoin

Calmels

Poupardin-Olivier

et al. 2008

Archiv der Pharmazie

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Drug-discovery projects frequently employ structure-based information through protein modeling and ligand docking, and there is a plethora of reports relating successful use of them in virtual screening. Hit/lead optimization, which represents the next step and the longest for the medicinal chemist, is very rarely considered. This is not surprising because lead optimization is a much more complex task. Here, a homology model of the histamine H(3) receptor was built and tested for its ability to discriminate ligands above a defined threshold of affinity. In addition, drug safety is also evaluated during lead optimization, and "antitargets" are studied. So, we have used the same benchmarking procedure with the HERG channel and CYP2D6 enzyme, for which a minimal affinity is strongly desired. For targets and antitargets, we report here an accuracy as high as at least 70%, for ligands being classified above or below the chosen threshold. Such a good result is beyond what could have been predicted, especially, since our test conditions were particularly stringent. First, we measured the accuracy by means of AUC of ROC plots, i. e. considering both false positive and false negatives. Second, we used as datasets extensive chemical libraries (nearly a thousand ligands for H(3)). All molecules considered were true H(3) receptor ligands with moderate to high affinity (from microM to nM range). Third, the database is issued from concrete SAR (Bioprojet H(3) BF2.649 library) and is not simply constituted by few active ligands buried in a chemical catalogue.

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Docking of small ligands to low‐resolution and theoretically predicted receptor structures

Cited by 31 publications

References 39 publications

A scoring function for docking ligands to low‐resolution protein structures

A scoring function for docking ligands to low‐resolution protein structures

TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints

Refined Docking as a Valuable Tool for Lead Optimization: Application to Histamine H₃ Receptor Antagonists

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Docking of small ligands to low‐resolution and theoretically predicted receptor structures

Cited by 31 publications

References 39 publications

A scoring function for docking ligands to low‐resolution protein structures

A scoring function for docking ligands to low‐resolution protein structures

TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints

Refined Docking as a Valuable Tool for Lead Optimization: Application to Histamine H3 Receptor Antagonists

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Refined Docking as a Valuable Tool for Lead Optimization: Application to Histamine H₃ Receptor Antagonists