Ab InitioFolding of Peptides by the Optimal-Bias Monte Carlo Minimization Procedure

Abagyan, Ruben; Totrov, Maxim

doi:10.1006/jcph.1999.6233

Cited by 73 publications

(71 citation statements)

References 42 publications

(89 reference statements)

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“…Although Eq. 4 should be a well-known result, we are not aware of any published reference earlier than Abagyan and collaborators in a completely different context (8,9).…”

Section: Authoritarian Vs Democratic Strategiesmentioning

confidence: 85%

Strong profiling is not mathematically optimal for discovering rare malfeasors

Press

2009

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

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The use of profiling by ethnicity or nationality to trigger secondary security screening is a controversial social and political issue. Overlooked is the question of whether such actuarial methods are in fact mathematically justified, even under the most idealized assumptions of completely accurate prior probabilities, and secondary screenings concentrated on the highest-probablity individuals. We show here that strong profiling (defined as screening at least in proportion to prior probability) is no more efficient than uniform random sampling of the entire population, because resources are wasted on the repeated screening of higher probability, but innocent, individuals. A mathematically optimal strategy would be ''square-root biased sampling,'' the geometric mean between strong profiling and uniform sampling, with secondary screenings distributed broadly, although not uniformly, over the population. Square-root biased sampling is a general idea that can be applied whenever a ''bell-ringer'' event must be found by sampling with replacement, but can be recognized (either with certainty, or with some probability) when seen.screening ͉ square-root biased sampling ͉ rare events

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“…Although Eq. 4 should be a well-known result, we are not aware of any published reference earlier than Abagyan and collaborators in a completely different context (8,9).…”

Section: Authoritarian Vs Democratic Strategiesmentioning

confidence: 85%

Strong profiling is not mathematically optimal for discovering rare malfeasors

Press

2009

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

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“…First, weighted harmonic restraints were imposed between C a of residues 892-910 (H12) of AR LBD and C a of equivalent residues of H12 (residues 535-547) of the crystal structure of the estrogen receptor in antagonist-bound conformation [Protein Data Bank entry 1err (51)]. Then, , , and angles of the residues comprising the AR LBD flexible loop (residues 887-891) were freed, and the energy of the system was globally optimized in internal coordinate space by using the biased probability Monte Carlo (BPMC) procedure (52,53) within the ICM program (Molsoft). This initial crude model was used to dock a small set of known nuclear receptor agonists and antagonists, composed of testosterone, DHT, RU-486, flutamide, hydroxyflutamide, nilutamide, and bicalutamide, using the ligand-docking procedure within the ICM program.…”

Section: Methodsmentioning

confidence: 99%

“…This database was docked at least three times, and the lowest-energy, ligand-bound conformations were retained. Then, for each receptor-ligand complex, conformations of the receptor side chains in the vicinity of the ligand were optimized by global energy minimization with biased probability Monte Carlo in internal coordinate space (52,53). Enrichment factors for identification of target-specific compounds were determined as described (32), with certain modifications.…”

Section: Methodsmentioning

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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“…Eight hundred and three NMR restraints were converted to ICM-readable format and were imposed on the models using a quadratic restraint energy with a strength of 10 kcal/(mol Å 2 ) (Bordner and Abagyan, 2006). Each of the 20 models was then subjected to local minimization, followed by 10 independent cycles of Monte Carlo sampling of both the backbone and side-chain dihedral angles at 600 K in ICM, each simulation lasting for more than 144 h (Abagyan and Totrov, 1999). The lowest energy conformation for each of the 20 models was retained.…”

Section: Methodsmentioning

confidence: 99%

Molecular Approximations between Residues 21 and 23 of Secretin and Its Receptor: Development of a Model for Peptide Docking with the Amino Terminus of the Secretin Receptor

et al. 2007

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The structurally unique amino-terminal domain of class II G protein-coupled receptors is critically important for ligand binding and receptor activation. Understanding the precise role it plays requires detailed insights into the molecular basis of its ligand interactions and the conformation of the ligand-receptor complex. In this work, we used two high-affinity, full-agonist, secretin-like photolabile probes having sites for covalent attachment in positions 21 and 23 and used sequential proteolysis and sequencing of the labeled region of the receptor to identify two new spatial approximation constraints. The position 21 probe labeled receptor residue Arg 15 , whereas the position 23 probe labeled receptor residue Arg 21 . A homology model of the amino-terminal domain of the secretin receptor was developed using the NMR structure of the analogous domain of the corticotropin-releasing factor receptor. This was attached to a homology model of the secretin receptor transmembrane bundle, with the two domains oriented relative to each other based on continuity of the peptide backbone and by imposing a distance restraint recently identified between the amino-terminal WDN sequence and the region of the helical bundle above transmembrane segment six. Secretin was docked to this model using seven sets of spatial approximation constraints identified in previous photoaffinity labeling studies. This model was found to fully accommodate all existing constraints, as well as the two new approximations identified in this work.The secretin receptor is a prototypic member of the class II family of G protein-coupled receptors (GPCRs) that contains several potentially important drug targets, such as receptors for calcitonin, vasoactive intestinal polypeptide, glucagon, glucagon-like peptide, corticotropin-releasing factor (CRF), and parathyroid hormone (Ulrich et al., 1998). Despite recent advances in the study of this receptor family, our understanding of receptor structure-function remains limited. Refinement of our understanding of the molecular basis of agonist ligand binding and activation of the secretin receptor should contribute insights relevant to the entire family and facilitate the design and refinement of potential receptoractive drugs.

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Ab InitioFolding of Peptides by the Optimal-Bias Monte Carlo Minimization Procedure

Cited by 73 publications

References 42 publications

Strong profiling is not mathematically optimal for discovering rare malfeasors

Strong profiling is not mathematically optimal for discovering rare malfeasors

Discovery of antiandrogen activity of nonsteroidal scaffolds of marketed drugs

Molecular Approximations between Residues 21 and 23 of Secretin and Its Receptor: Development of a Model for Peptide Docking with the Amino Terminus of the Secretin Receptor

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