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Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

ABSTRACT: Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set agents to 5495 proteins. Two learning sets were developed. One set was from the Carcinogenic Potency Database, where chemicals tested for rat carcinogenesis along with Salmonella mutagenicity data were provided. The second was from Malacarne et al. who developed a learning set of nonalerting compounds based on rodent cancer bioassay data and Ashby's structural alerts. When the rat cancer models were categorized based on mutagenicity, the traditional fragment model outperformed the ligand-based model. However, when the learning sets were composed solely of nonmutagenic or nonalerting carcinogens and noncarcinogens, the fragment model demonstrated a concordance of near 50%, whereas the ligand-based models demonstrated a concordance of 71% for nonmutagenic carcinogens and 74% for nonalerting carcinogens. Overall, these findings suggest that expert system analysis of virtual chemical protein interactions may be useful for developing predictive SAR models for nonmutagenic carcinogens. Moreover, a more practical approach for developing SAR models for carcinogenesis may include fragment-based models for chemicals testing positive for mutagenicity and ligand-based models for chemicals devoid of DNA reactivity.

SUBMITTER: Cunningham AR

PROVIDER: S-EPMC3463155 | biostudies-other | 2012 Oct

REPOSITORIES: biostudies-other

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Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

Cunningham Albert R AR Carrasquer C Alex CA Qamar Shahid S Maguire Jon M JM Cunningham Suzanne L SL Trent John O JO

Carcinogenesis 20120607 10

Structure-activity relationship (SAR) models are powerful tools to investigate the mechanisms of action of chemical carcinogens and to predict the potential carcinogenicity of untested compounds. We describe the use of a traditional fragment-based SAR approach along with a new virtual ligand-protein interaction-based approach for modeling of nonmutagenic carcinogens. The ligand-based SAR models used descriptors derived from computationally calculated ligand-binding affinities for learning set ag ...[more]

PMID: 22678118

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Project description:Local protein structure representations that incorporate long-range contacts between residues are often considered in protein structure comparison but have found relatively little use in structure prediction where assembly from single backbone fragments dominates. Here, we introduce the concept of local descriptors of protein structure to characterize local neighborhoods of amino acids including short- and long-range interactions. We build a library of recurring local descriptors and show that this library is general enough to allow assembly of unseen protein structures. The library could on average re-assemble 83% of 119 unseen structures, and showed little or no performance decrease between homologous targets and targets with folds not represented among domains used to build it. We then systematically evaluate the descriptor library to establish the level of the sequence signal in sets of protein fragments of similar geometrical conformation. In particular, we test whether that signal is strong enough to facilitate correct assignment and alignment of these local geometries to new sequences. We use the signal to assign descriptors to a test set of 479 sequences with less than 40% sequence identity to any domain used to build the library, and show that on average more than 50% of the backbone fragments constituting descriptors can be correctly aligned. We also use the assigned descriptors to infer SCOP folds, and show that correct predictions can be made in many of the 151 cases where PSI-BLAST was unable to detect significant sequence similarity to proteins in the library. Although the combinatorial problem of simultaneously aligning several fragments to sequence is a major bottleneck compared with single fragment methods, the advantage of the current approach is that correct alignments imply correct long range distance constraints. The lack of these constraints is most likely the major reason why structure prediction methods fail to consistently produce adequate models when good templates are unavailable or undetectable. Thus, we believe that the current study offers new and valuable insight into the prediction of sequence-structure relationships in proteins.

Project description:Peptidergic signaling regulates cardiac contractility; thus, identifying molecular switches, ligand-receptor contacts, and antagonists aids in exploring the underlying mechanisms to influence health. Myosuppressin (MS), a decapeptide, diminishes cardiac contractility and gut motility. Myosuppressin binds to G protein-coupled receptor (GPCR) proteins. Two Drosophila melanogaster myosuppressin receptors (DrmMS-Rs) exist; however, no mechanism underlying MS-R activation is reported. We predicted DrmMS-Rs contained molecular switches that resembled those of Rhodopsin. Additionally, we believed DrmMS-DrmMS-R1 and DrmMS-DrmMS-R2 interactions would reflect our structure-activity relationship (SAR) data. We hypothesized agonist- and antagonist-receptor contacts would differ from one another depending on activity. Lastly, we expected our study to apply to other species; we tested this hypothesis in Rhodnius prolixus, the Chagas disease vector. Searching DrmMS-Rs for molecular switches led to the discovery of a unique ionic lock and a novel 3-6 lock, as well as transmission and tyrosine toggle switches. The DrmMS-DrmMS-R1 and DrmMS-DrmMS-R2 contacts suggested tissue-specific signaling existed, which was in line with our SAR data. We identified R. prolixus (Rhp)MS-R and discovered it, too, contained the unique myosuppressin ionic lock and novel 3-6 lock found in DrmMS-Rs as well as transmission and tyrosine toggle switches. Further, these motifs were present in red flour beetle, common water flea, honey bee, domestic silkworm, and termite MS-Rs. RhpMS and DrmMS decreased R. prolixus cardiac contractility dose dependently with EC50 values of 140 nM and 50 nM. Based on ligand-receptor contacts, we designed RhpMS analogs believed to be an active core and antagonist; testing on heart confirmed these predictions. The active core docking mimicked RhpMS, however, the antagonist did not. Together, these data were consistent with the unique ionic lock, novel 3-6 lock, transmission switch, and tyrosine toggle switch being involved in mechanisms underlying TM movement and MS-R activation, and the ability of MS agonists and antagonists to influence physiology.

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Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

Publications

Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors.

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