Ph4Dock:  Pharmacophore-Based Protein−Ligand Docking

Goto, Junki; Kataoka, Ryoichi; Hirayama, Noriaki

doi:10.1021/jm0493818

Cited by 95 publications

(86 citation statements)

References 16 publications

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“…18 The second step involved docking simulation of the selected compounds to the PAI-1 molecule by the program Ph4Dock. 19 The crystal structure (1A7C) of the complex between PAI-1 and the inhibitory reactive-center loop peptide was obtained from the Protein Data Bank 20 and used as the target structure for the docking study. After removal of water molecules and binding peptide, hydrogen atoms were added in accord with the standard protonation states of acidic and basic residues in proteins and their positions were optimized.…”

Section: Virtual Screeningmentioning

confidence: 99%

See 1 more Smart Citation

Inhibition of Plasminogen Activator Inhibitor-1

Izuhara

Takahashi

Nangaku

et al. 2008

ATVB

Self Cite

115

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Objective-Serine protease inhibitors (serpin) play a central role in various pathological processes including coagulation, fibrinolysis, malignancy, and inflammation. Inhibition of serpins may prove therapeutic. As yet, however, only very few small molecule serpin inhibitors have been reported. For the first time, we apply a new approach of virtual screening to discover novel, orally active, small molecule serpin inhibitors and report their effectiveness. Methods and Results-We focused on a clinically important serpin, plasminogen activator inhibitor-1 (PAI-1), whose crystal structure has been described. We identify novel, orally active molecules able to enter into the strand 4 position (s4A) of the A ␤-sheet of PAI-I as a mock compound. In vitro they specifically inhibit the PAI-1 activity and enhance fibrinolysis activity. In vivo the most effective molecule (TM5007) inhibits coagulation in 2 models: a rat arteriovenous (AV) shunt model and a mouse model of ferric chloride-induced testicular artery thrombosis. It also prevents the fibrotic process initiated by bleomycin in mouse lung. Conclusions-The present study demonstrates beneficial in vitro and in vivo effects of novel PAI-1 inhibitors. Our methodology proves to be a useful tool to obtain effective inhibitors of serpin activity. Serpins consist of a ␤-sheets-rich body. They contain an exposed mobile reactive center loop (RCL) 2 which, once cleaved by a target serine protease, inserts its N terminus part into the strand 4 position (s4A) of the A ␤-sheet, triggering its antiprotease activity. Small molecule compounds able to enter into the s4A position of the A ␤-sheet as a mock molecule may thus prevent the biological activity of the serpin.Up to now, however, only very few small molecule serpin inhibitors have been described, none of which are in clinical use. Most have been discovered by high-throughput random screening (HTS) of a large chemical library, 3-5 a rather inefficient strategy. In the present study, a new approach of virtual screening based on the 3-dimensional structure of a serpin, (PAI-1), was used to discover novel, orally active, small molecule compounds able to inhibit the target molecule.PAI-1 regulates the plasminogen activation system through inhibition of its target serine proteases, tissue-type and urokinase-type plasminogen activator (tPA and uPA). 1 Studies in humans and animals have demonstrated that PAI-1 expression is enhanced in various disorders such as thrombosis, fibrotic diseases, atherosclerosis, radiation damage, and cancer progression. 6 PAI-1 has been linked with fibrin deposition evolving into organ fibrosis and atherosclerosis, or with striking alterations of cell adhesion and migration mediating cancer progression. 7 The absence of PAI-1 in PAI-1 knockout mice markedly attenuates these pathological processes. 8 -12 Inhibition of PAI-1 by a neutralizing antibody 13 provides similar promising results in animal experiments. Small molecule PAI-1 inhibitors, active orally, should prove useful to treat not only thrombot...

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Section: Virtual Screeningmentioning

confidence: 99%

“…Docking simulation was then undertaken by a program Ph4Dock 19 for the remaining compounds. The crystal structure (1A7C) of the complex of PAI-1 with its inhibitory RCL peptide 20 was used, knowing that the 14-aa peptide corresponding to the N terminus of the RCL of PAI-1 inhibits the in vitro activity of PAI-1.…”

Section: Virtual Screeningmentioning

confidence: 99%

Inhibition of Plasminogen Activator Inhibitor-1

Izuhara

Takahashi

Nangaku

et al. 2008

ATVB

Self Cite

115

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“…2.0, the Cambridge crystallographic data centre, U.K. 15) ) with the standard default settings. Pharmacophore Docking of Substrates Pharmacophore Dock (Ph4Dock, 16) ) of MOE (ver. 2003.02, Chemical Computing Group: Montreal, Canada) was performed for losartan and typical CYP3A4 substrates (saquinavir, 17) indinavir, 18) quinidine, 19) nifedipine, 20) terfenadine, 21) ritonavir, 22) dexamethasone, 23) verapamil, 24) erythromycin, 25) testosterone, 25) tamoxifen, 26) fentanyl, 27) amiodarone, 28) and colchicine 29) ).…”

Section: Methodsmentioning

confidence: 99%

Characterization of the CYP3A4 Active Site by Homology Modeling

Tanaka

Okuda

Yamamoto

2004

CHEMICAL & PHARMACEUTICAL BULLETIN

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Cytochrome P450s (CYPs) are membrane-associated heme proteins and hepatic microsomal enzymes, which participate in drug metabolism and detoxification. Among more than 50 human CYPs, an isoform, CYP3A4, is a major enzyme responsible for drug-drug interactions that is concerned in at least 50% of drug metabolism.1) In addition, CYP3A4 is expressed at a high level in the human liver. 2)Thus, it is important to know the CYP3A4 inhibitory potency of new drug candidates at an early stage of drug discovery. Although many detailed studies about CYP3A4 inhibitory pharmacophores have been published, 3) precise interactions between inhibitors (or substrates) and CYP3A4 have not been defined well. Recently, the crystal structure of CYP2C5 was determined by Williams et al.,4) which is the first structure of a mammalian microsomal CYP. To understand the molecular basis of CYP3A4 inhibition well, a CYP3A4 homology model was constructed, and inhibitors and substrates were docked into the model. In this report we will describe some interesting features of the putative active site of CYP3A4. ExperimentalHomology Modeling The amino acid sequence of CYP3A4 was taken from the ExPASy web site (http://tw.expasy.org/). The amino acid sequence alignment used for the homology modeling was obtained as follows. The amino acid sequences for which 3D structures were available at the time (Pseudomonas-Putida P450 CAM ,9,10) and mammalian CYP2C5 4) ) were aligned according to their 3D structures using the homology module of Insight II (ver. 2000, Accelrys Inc., San Diego, California, U.S.A.). Subsequently, amino acid sequences of human metabolic CYPs were aligned by sequence homology using ClustalW.11) The combined alignments were manually revised so that insertions and deletions did not get into the portions corresponding to probable secondary structures, as far as possible, and the following four characteristic motifs of CYPs were aligned: WXXXR (where W is tryptophan, R is arginine, and X is any amino acid) in the alpha-helix C, EXXR (where E is glutamate, R is arginine, and X is any amino acid) in the alphahelix K, ZXXPXXZXPXXZ (where P is proline, Z is aromatic amino acid, and X is any amino acid) after the alpha-helix KЈ and FXXGXXXCXG (where F is phenylalanine, G is glycine, C is cysteine, and X is any amino acid) before the alpha-helix L. 12)According to the alignment (Fig. 1), a homology model of CYP3A4 was constructed based on the crystal structure of mammalian CYP2C5 (PDB code:1DT6) using the homology module of Insight II. Although the sequence identities between CYP102 (BM3) and CYP3A4 versus CYP2C5 and CYP3A4 are very similar (22%), CYP2C5 was chosen as a template since the mammalian CYP was presumed to be more suitable than a prokaryotic one. Using the Search/Generate-Loops function of Insight II, conformations of the insertion and deletion parts in the alignment were obtained referring to the known 3-D structures. After some manual adjustments to remove large steric hindrances, the whole structure was subjected to energy minimiz...

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“…Adding more flexibility led to "place and join" and incremental construction algorithms that avoid combinatorial explosions in searching conformational space [35]. Notable examples of docking programs that use point complementary matching are: FTDOCK by Gabb, Jackson and Sternberg [69], FRED (Fast Rigid Exhaustive Docking) developed by OpenEye [70,71], PhDOCK [72] and Ph4DOCK [73], DragHome by Schafferhans and Klebe [74], and Schneidman-Duhovny's PatchDOCK [75]. Distance geometry-based methods adopt a form of shape matching algorithms; programs of note include the earliest incarnations of DOCK (1982) and the Wallqvist and Covell program ADAM [76].…”

Section: Docking Algorithmsmentioning

confidence: 99%

Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

Spyrakis

Cozzini

Kellogg

2016

Nuclear Receptor Research

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Abstract. During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3][4][5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest.

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Ph4Dock: Pharmacophore-Based Protein−Ligand Docking

Cited by 95 publications

References 16 publications

Inhibition of Plasminogen Activator Inhibitor-1

Inhibition of Plasminogen Activator Inhibitor-1

Characterization of the CYP3A4 Active Site by Homology Modeling

Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

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