Developing a Dynamic Pharmacophore Model for HIV-1 Integrase

Carlson, Heather A.; Masukawa, Kevin M.; Rubins, Kathleen H.; Bushman, Frederic D.; Jorgensen, William; Lins, Roberto D.; Briggs, James M.; McCammon, J. Andrew

doi:10.1021/jm990322h

Cited by 262 publications

(312 citation statements)

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“…Computational prediction of the induced fit upon binding of a ligand is a critical unsolved problem (35,36). Recently, several successful flexible receptor docking protocols were proposed for different systems (37,38).…”

Section: Discussionmentioning

confidence: 99%

Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

Bisson

Cheltsov

Bruey-Sédano

et al. 2007

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

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Finding good drug leads de novo from large chemical libraries, real or virtual, is not an easy task. High-throughput screening is often plagued by low hit rates and many leads that are toxic or exhibit poor bioavailability. Exploiting the secondary activity of marketed drugs, on the other hand, may help in generating drug leads that can be optimized for the observed side-effect target, while maintaining acceptable bioavailability and toxicity profiles. Here, we describe an efficient computational methodology to discover leads to a protein target from safe marketed drugs. We applied an in silico ''drug repurposing'' procedure for identification of nonsteroidal antagonists against the human androgen receptor (AR), using multiple predicted models of an antagonist-bound receptor. The library of marketed oral drugs was then docked into the best-performing models, and the 11 selected compounds with the highest docking score were tested in vitro for AR binding and antagonism of dihydrotestosterone-induced AR transactivation. The phenothiazine derivatives acetophenazine, fluphenazine, and periciazine, used clinically as antipsychotic drugs, were identified as weak AR antagonists. This in vitro biological activity correlated well with endocrine side effects observed in individuals taking these medications. Further computational optimization of phenothiazines, combined with in vitro screening, led to the identification of a nonsteroidal antiandrogen with improved AR antagonism and marked reduction in affinity for dopaminergic and serotonergic receptors that are the primary target of phenothiazine antipsychotics.androgen receptor ͉ drug design ͉ prostate cancer C urrent approaches for discovery of novel chemical leads against a molecular target rely heavily on high-throughput screening (HTS) and to a lesser extent on virtual ligand screening (VLS) techniques. HTS has provided rapid lead identification for numerous drug targets (1-8); however, HTS also has major drawbacks, including a significant level of false positives and false negatives and low hit rates for many targets (9). Successful leads from HTS can also suffer from poor bioavailability and unwanted toxicity profiles of compounds. These problems result partially from the nature of the chemical libraries used for HTS. Furthermore, because the pharmacological properties of most compound libraries are largely unknown, there is an additional high risk that optimization of hits identified with HTS will not be sufficient for their evolution into real drugs.In contrast, retrospective analysis of marketed drugs reveals that their physicochemical and structural properties are clustered around preferred values and scaffolds (10). In addition, some chemical motifs are associated with high biological activity and often confer activity against more than one target/receptor (11-16). These motifs have been referred to as ''privileged structures'' (11). These observations lead to an assumption that the chemical space of potential drugs is limited. Consequently, currently marketed...

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Section: Discussionmentioning

confidence: 99%

Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

Bisson

Cheltsov

Bruey-Sédano

et al. 2007

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

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“…The structure solved by Maignan et al, 18 that was used to develop Static Model 1, is very similar to the MD structures. 2 It is reasonable that this static model has the more similar performance to the dynamic model. Table 3 presents a comparison of the performance of the dynamic models and Static Model 1.…”

Section: Discussionmentioning

confidence: 91%

“…Furthermore, we are grateful for the structures from the MD simulations provided by Prof. James M. Briggs and his student Roberto D. Lins, who both appear as authors in the original paper presenting the dynamic pharmacophore model. 2 We are also indebted to Prof. William L. Jorgensen for providing the BOSS program (MUSIC), the pepz utility, and the ChemEdit program. H.A.C.…”

Section: Acknowledgmentmentioning

confidence: 96%

“…[4][5][6][7][8][9][10][11][12] Our previous study demonstrated that a "static" pharmacophore model, based on the crystal structure used to initiate the MD study, exhibited a poorer performance than the dynamic model when fitting known inhibitors of the integrase. 2 HIV-1 integrase is an interesting test case for developing dynamic methods because it has an active site that is shallow, solvent exposed, and minimally restricted in conformational sampling. Also, the flexibility of the active site loop is required for catalytic activity; this must be incorporated into any reliable pharmacophore model of the system.…”

Section: Introductionmentioning

confidence: 99%

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Method for Including the Dynamic Fluctuations of a Protein in Computer-Aided Drug Design

1999

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We have recently presented a new pharmacophore design method that allows for the incorporation of the inherent flexibility of a target active site. The flexibility of the enzymatic system is described by collecting many conformations of the uncomplexed protein; this ensemble of conformational states can come from a molecular dynamics (MD) simulation, multiple crystal structures, or many NMR structures. Binding sites for functional groups that complement the active site are determined through multiple-copy calculations. These calculations are conducted for each protein conformation, providing a large collection of potential binding sites. The Cartesian coordinates from each protein conformation are overlaid through RMS fitting of essential catalytic residues, and the pharmacophore model is described by binding regions that are conserved over many protein conformations. Previously, we developed a "dynamic" pharmacophore model for HIV-1 integrase using 11 conformations of the protein from an MD simulation; the MUSIC procedure was used to calculate binding positions for methanol molecules in each configuration of the active site. Here we present "static" pharmacophore models developed with a single conformation of the protein from two new crystal structures (standard protocol for multiple-copy methods). The static models do not perform as well as the previous dynamic model in fitting known inhibitors of HIV-1 integrase. To test the applicability of the dynamic pharmacophore method and the assumption that any reliable source of protein conformations is applicable, we have now developed a second dynamic pharmacophore model based on the two crystal structures also used for the development of the static models. Though the dynamic model based on the two crystal structures does not fit as many known inhibitors as the previous dynamic model, it is a significant improvement over the static models. Even better performance is expected with the addition of new crystal structures as they become available. However, it is notable that using only two structures leads to great improvement in the models.

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“…Fragment positioning methods such as GRID [1], MCSS [2][3][4], SPROUT [5], MUSIC [6], LUDI [7,8], and Superstar [9] have been in use for over two decades now and are typically employed during the early stage of lead optimization. These methods determine energetically favorable binding site positions for various functional group types or chemical fragments based on molecular mechanics or knowledge-based potentials.…”

mentioning

confidence: 99%

Challenges of fragment screening

Joseph‐McCarthy

2009

J Comput Aided Mol Des

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Developing a Dynamic Pharmacophore Model for HIV-1 Integrase

Cited by 262 publications

References 73 publications

Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

Method for Including the Dynamic Fluctuations of a Protein in Computer-Aided Drug Design

Challenges of fragment screening

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