Templates for design of inhibitors for serine proteases: Application of the program dock to the discovery of novel inhibitors for thrombin

Massova, Irina; Martin, Philip D.; Bulychev, Alexey; Kocz, Remek; Doyle, M. A.; Edwards, Brian F.P.; Mobashery, Shahriar

doi:10.1016/s0960-894x(98)00444-2

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…In this study, 5 of the 26 soluble ligand candidates tested for binding proved to be inhibitors of human thrombin. This success rate is similar to those achieved by other groups, which typically also employ some post-scoring filtering (Shoichet et al, 1993;Massova et al, 1998;Fox and Haaksma, 2000;Schapira et al, 2000Schapira et al, , 2001Doman et al, 2002;Gruneberg et al, 2002;Mizutani and Itai, 2004;Polgar et al, 2005;Zhou et al, 2006). For comparison, experimental high-throughput screening without structure or ligand-based screening typically has a hit rate of less than 1% (Doman et al, 2002;Polgar et al, 2005;Marx et al, 2006;Schepetkin et al, 2006).…”

Section: Discussionsupporting

confidence: 81%

“…Increases in efficiency and reliability of computational tools have enabled virtual screening to become a routine method in pharmaceutical drug discovery, complementing in vitro highthroughput screening (Schneider and Bohm, 2002;Shoichet et al, 2002;Waszkowycz, 2002;Jorgensen, 2004). Inhibitors have been identified by computational screening of compound databases for protein tyrosine phosphatase-1B (Doman et al, 2002), nuclear hormone receptors (Schapira et al, 2000(Schapira et al, , 2001, human carbonic anhydrase (Gruneberg et al, 2002), thymidylate synthase (Shoichet et al, 1993), integrin alphavbeta3 (Zhou et al, 2006), acetylcholinesterase (Mizutani and Itai, 2004), glycogen synthase kinase-3beta (Polgar et al, 2005), and thrombin (Massova et al, 1998;Bohm et al, 1999;Fox and Haaksma, 2000;Howard et al, 2006). Typically, structure-based virtual screening (in which ligand candidates are docked into the atomic structure of a protein's binding site and evaluated by the resulting protein interactions) and ligand-based screening (in which ligand (www.interscience.wiley.com) DOI:10.1002/jmr.942 candidates are aligned and scored for similarity to a prototypical known ligand) are used independently (Oprea and Matter, 2004;McGaughey et al, 2007).…”

Section: Introduction Virtual Screeningmentioning

confidence: 99%

“…A similar approach was used to identify and link small fragments of micromolar potency to create nanomolar inhibitors for thrombin (Howard et al, 2006). DOCK has also been used to screen the CSD for new inhibitors for thrombin (Massova et al, 1998). Three of their top-scoring compounds were found to be competitive inhibitors of thrombin with K i values in the range of 170-1700 mM.…”

Section: Introduction Virtual Screeningmentioning

confidence: 99%

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Scoring ligand similarity in structure‐based virtual screening

Zavodszky

Rohatgi

Voorst

et al. 2009

J of Molecular Recognition

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Scoring to identify high-affinity compounds remains a challenge in virtual screening. On one hand, protein-ligand scoring focuses on weighting favorable and unfavorable interactions between the two molecules. Ligand-based scoring, on the other hand, focuses on how well the shape and chemistry of each ligand candidate overlay on a three-dimensional reference ligand. Our hypothesis is that a hybrid approach, using ligand-based scoring to rank dockings selected by protein-ligand scoring, can ensure that high-ranking molecules mimic the shape and chemistry of a known ligand while also complementing the binding site. Results from applying this approach to screen nearly 70 000 National Cancer Institute (NCI) compounds for thrombin inhibitors tend to support the hypothesis. EON ligand-based ranking of docked molecules yielded the majority (4/5) of newly discovered, low to mid-micromolar inhibitors from a panel of 27 assayed compounds, whereas ranking docked compounds by protein-ligand scoring alone resulted in one new inhibitor. Since the results depend on the choice of scoring function, an analysis of properties was performed on the top-scoring docked compounds according to five different protein-ligand scoring functions, plus EON scoring using three different reference compounds. The results indicate that the choice of scoring function, even among scoring functions measuring the same types of interactions, can have an unexpectedly large effect on which compounds are chosen from screening. Furthermore, there was almost no overlap between the top-scoring compounds from protein-ligand versus ligand-based scoring, indicating the two approaches provide complementary information. Matchprint analysis, a new addition to the SLIDE (Screening Ligands by Induced-fit Docking, Efficiently) screening toolset, facilitated comparison of docked molecules' interactions with those of known inhibitors. The majority of interactions conserved among top-scoring compounds for a given scoring function, and from the different scoring functions, proved to be conserved interactions in known inhibitors. This was particularly true in the S1 pocket, which was occupied by all the docked compounds.

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Section: Discussionsupporting

confidence: 81%

Section: Introduction Virtual Screeningmentioning

confidence: 99%

Section: Introduction Virtual Screeningmentioning

confidence: 99%

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Scoring ligand similarity in structure‐based virtual screening

Zavodszky

Rohatgi

Voorst

et al. 2009

J of Molecular Recognition

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“…The program DOCK, one of the most widely used docking programs, has been applied in many published studies [73][74][75][76][77][78]. Usually, the DOCK AMBER force field score is applied.…”

Section: Successful Identification Of Novel Leads Through Virtual Scrmentioning

confidence: 99%

Prediction of Non‐bonded Interactions in Drug Design

Böhm

2003

Methods and Principles in Medicinal Chemistry

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“…While the reproduction of experimentally solved complex structures is an accepted way of testing and comparing docking programs [51,52], surprisingly few virtual screening applications have been published. Compound selection based on docking calculations, for example, has been done for thrombin [53,54], thymidylate synthase [55,56], DHFR enzymes [57] and HIV protease [58,59]. However, only in rare cases [13], such studies are conclusive as to the efficiency of the docking tools in enriching subsets of a database in active compounds.…”

Section: Introductionmentioning

confidence: 99%

Modifications of the scoring function in FlexX for virtual screening applications

Ståhl

Virtual Screening: An Alternative or Complement to High Throughput Screening?

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A modification of the hydrogen bond score in the docking program FlexX is presented. Hydrogen bonds formed in inaccessible regions of protein cavities thereby gain larger weight than others formed at the protein surface. The modified scoring function is tested with thrombin as a target. Secondly, a recently published knowledge-based scoring function is compared to the FlexX scoring function in several database ranking experiments.

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Templates for design of inhibitors for serine proteases: Application of the program dock to the discovery of novel inhibitors for thrombin

Cited by 9 publications

References 13 publications

Scoring ligand similarity in structure‐based virtual screening

Scoring ligand similarity in structure‐based virtual screening

Prediction of Non‐bonded Interactions in Drug Design

Modifications of the scoring function in FlexX for virtual screening applications

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