Biomedical subjects

Jeremy L Jenkins

Publications and source records attributed to Jeremy L Jenkins.

11 recordsLinked to original sources

PubMed2006

Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Adenosine Triphosphate↗

PubMed2006

Streamlining lead discovery by aligning in silico and high-throughput screening.

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.

Bayes Theorem↗

PubMed2004

A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes.

A primary goal of 3D similarity searching is to find compounds with similar bioactivity to a reference ligand but with different chemotypes, i.e., "scaffold hopping". However, an adequate description of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3D chemical descriptions in which clustering techniques traditionally used in data mining are exploited to create "fuzzy" molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one or more active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for actives taken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks. Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled by FEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoic acid). Importantly, the method excels in comparison to commonly used 2D similarity methods (DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.

Cyclooxygenase 2↗

PubMed2003

Unexpected binding mode for 2'-phosphoadenosine-based nucleotide inhibitors in complex with human angiogenin revealed by heteronuclear NMR spectroscopy.

Human angiogenin (Ang) is a tumor-promoting RNase in the pancreatic RNase superfamily. Efforts to develop nucleotide-based inhibitors of Ang as potential anticancer drugs have been hampered by the lack of direct structural information on Ang-nucleotide complexes. Here, we have used heteronuclear NMR spectroscopy with (15)N- and (15)N/(13)C-labeled Ang to map the interactions of Ang with the phosphate ion, seven adenosine mononucleotides (the 2'-, 3'-, and 5'-monophosphates, the 2',5'- and 3',5'-diphosphates, the 5'-diphosphate, and the 2'-monophospho-5'-diphosphate), and the dinucleotide 2'-deoxyuridine 3'-pyrophosphate (P' --> 5') adenosine-2'-phosphate (dUppA-2'-p). The 2'-phosphate based derivatives, which bind more tightly than the corresponding 3'-phosphate isomers, induced characteristic large resonance perturbations of the backbone amide proton of Leu(115), the backbone (15)N of His(114), and the Gln(12) side-chain NH(2) group in the Ang active site. In contrast, adenosine derivatives with only 3'- or 5'-phosphates produced much less dramatic perturbations of Leu(115) and His(114) resonances, along with modest perturbations of additional residues both within and beyond the active site. Measurements of NOEs together with molecular docking analyses revealed the three-dimensional structures of the complexes of Ang with adenosine 2',5'-diphosphate and dUppA-2'-p; the binding modes of these inhibitors differ substantially from those predicted in earlier studies. Most notably, the 2'-phosphate rather than the 5'-phosphate occupies the P(1) catalytic subsite of Ang, and the side chain of His(114) has undergone a conformational transition that positions it outside P(1) and allows it to form stacking interactions with the adenine ring of the inhibitor. Strikingly, the 2'-deoxyuridine moiety of dUppA-2'-p makes only a few contacts with Ang, and these involve residues outside the B(1) subsite where the pyrimidine ring of substrates normally binds.

Adenine Nucleotides↗

PubMed2003

Identification of small-molecule inhibitors of human angiogenin and characterization of their binding interactions guided by computational docking.

Angiogenin (ANG) is a potent inducer of angiogenesis and an RNase A homologue whose ribonucleolytic activity is essential for its biological action. Recently, we reported the identification of small non-nucleotide inhibitors of the enzymatic activity of ANG by high-throughput screening (HTS) [Kao, R. Y. T., et al. (2002) Proc. Natl. Acad. Sci. U.S.A. 99, 10066-10071]. Two of the inhibitors that were obtained, National Cancer Institute compound NSC-65828 [8-amino-5-(4'-hydroxybiphenyl-4-ylazo)naphthalene-2-sulfonate] and ChemBridge compound C-181431 [4,4'-dicarboxy-3,3'-bis(naphthylamido)diphenylmethanone], were judged to be suitable for further development, and one of these (NSC-65828) was shown to possess antitumor activity in mice. Here we have used computational docking as a guide for the identification of available NSC-65828 and C-181431 analogues that bind more tightly to ANG, and for the characterization of inhibitor binding modes. Numerous analogues were found to have greater avidity than the HTS compounds or any small nucleotide inhibitors; four were considered to be of interest as potential leads (K(i) = 5-25 microM). Two of these analogues bind more tightly to ANG than to RNase A, and are the first small molecules shown to exhibit this selectivity. The predicted binding orientations of the HTS compounds and the new lead inhibitors were evaluated by determining the effects of ANG active site mutations on inhibitory potency. The results with ANG variants R5A, H8A, N68A, and des(121-123) are highly consistent with the docking models. Affinity changes observed with Q12A and Q117G reveal aspects of active site function that are not apparent from the free ANG crystal structure or from the modeled complexes. These findings should prove to be useful in the design of more effective and specific ANG antagonists.

Amino Acid Substitution↗

PubMed2003

Inhibition of mammalian ribonucleases by endogenous adenosine dinucleotides.

The most potent low molecular weight inhibitors of pancreatic RNase superfamily enzymes reported to date are synthetic derivatives of adenosine 5(')-pyrophosphate. Here we have investigated the effects of six natural nucleotides that also incorporate this moiety (NADP(+), NADPH, ATP, Ap(3)A, Ap(4)A, and Ap(5)A) on the activities of RNase A and two of its homologues, eosinophil-derived neurotoxin and angiogenin. With eosinophil-derived neurotoxin and angiogenin, Ap(5)A is comparable to the tightest binding inhibitors identified previously (K(i) values at pH 5.9 are 370 nM and 100 microM, respectively); it ranks among the strongest small antagonists of RNase A as well (K(i)=230 nM). The K(i) for NADPH with angiogenin is similar to that of Ap(5)A. These findings suggest that Ap(5)A and NADPH may serve as useful new leads for inhibitor design. Examination of inhibition under physiological conditions indicates that NADPH, ATP, and Ap(5)A may suppress intracellular RNase activity significantly in vivo.

Adenine Nucleotides↗

PubMed2003

Virtual screening to enrich hit lists from high-throughput screening: a case study on small-molecule inhibitors of angiogenin.

"Hit lists" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.

Binding Sites↗

PubMed2002

A small-molecule inhibitor of the ribonucleolytic activity of human angiogenin that possesses antitumor activity.

The results of previous preclinical and clinical studies have identified angiogenin (ANG) as a potentially important target for anticancer therapy. Here we report the design and implementation of a high-throughput screening assay to identify small molecules that bind to the ribonucleolytic active site of ANG, which is critically involved in the induction of angiogenesis by this protein. Screening of 18,310 compounds from the National Cancer Institute (NCI) Diversity Set and ChemBridge DIVERSet yielded 15 hits that inhibit the enzymatic activity of ANG with K(i) values <100 microM. One of these, NCI compound 65828 [8-amino-5-(4'-hydroxybiphenyl-4-ylazo)naphthalene-2-sulfonate; K(i) = 81 microM], was selected for more detailed studies. Minor changes in ANG or ligand structure markedly reduced potency, demonstrating that inhibition reflects active-site rather than nonspecific binding; these observations are consistent with a computationally generated model of the ANG.65828 complex. Local treatment with modest doses of 65828 significantly delayed the formation of s.c. tumors from two distinct human cancer cell types in athymic mice. ANG is the likely target involved because (i) a 65828 analogue with much lower potency against the enzymatic activity of ANG failed to exert any antitumor effect, (ii) tumors from 65828-treated mice had fewer interior blood vessels than those from control mice, and (iii) 65828 appears to have no direct effect on the tumor cells. Our findings provide considerable support for the targeting of the enzymatic active site of ANG as a strategy for developing new anticancer drugs.

Amino Acid Substitution↗

PubMedDate not supplied

Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers.

High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining approaches: Laplacian-modified naive Bayes, recursive partitioning, and support vector machine (SVM) classifiers with increasing stochastic noise in the form of false positives and false negatives. All three of the data mining methods at hand tolerated increasing levels of false positives even when the ratio of misclassified compounds to true active compounds was 5:1 in the training set. False negatives in the ratio of 1:1 were tolerated as well. SVM outperformed the other two methods in capturing active compounds and scaffolds in the top 1%. A Murcko scaffold analysis could explain the differences in enrichments among the four data sets. This study demonstrates that data mining methods can add a true value to the screen even when the data is contaminated with a high level of stochastic noise.

Journal Article↗

PubMedDate not supplied

Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases.

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified naïve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as "antineoplastic", "kinase inhibitor", or "anti-inflammatory", the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.

Bayes Theorem↗

PubMedDate not supplied

"Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept?

Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons ("Bayes affinity fingerprints"). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database. The comparison of structures is performed via the Pearson correlation coefficient of activity classes, that is, the order in which two structures are similar to the panel activity classes. Compound retrieval on a recently published data set could be improved by as much as 24% relative (9% absolute). Knowledge about the shape of the "bioactive chemical universe" is thus beneficial to identifying similar bioactivities. Principal component analysis was employed to further analyze activity space with the objective to define orthogonal ligand bioactive chemical space, leading to nine major (roughly orthogonal) activity axes. Employing only those nine activity classes, retrieval rates are still comparable to original Bayes affinity fingerprints; thus, the concept of orthogonal bioactive ligand chemical space was validated as being an information-rich but low-dimensional representation of bioactivity space. Correlations between activity classes are a major determinant to gauge whether the desired multitarget activity of drugs is (on the basis of current knowledge) a feasible concept because it measures the extent to which activities can be optimized independently, or only by strongly influencing one another.

Algorithms↗