Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome.

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

A Dual, Systematic Approach to Malaria Diagnostic Biomarker Discovery.

BACKGROUND: The emergence and spread of Plasmodium falciparum parasites that lack HRP2/3 proteins and the resulting decreased utility of HRP2-based malaria rapid diagnostic tests (RDTs) prompted the World Health Organization and other global health stakeholders to prioritize the discovery of novel diagnostic biomarkers for malaria. METHODS: To address this pressing need, we adopted a dual, systematic approach by conducting a systematic review of the literature for publications on diagnostic biomarkers for uncomplicated malaria and a systematic in silico analysis of P. falciparum proteomics data for Plasmodium proteins with favorable diagnostic features. RESULTS: Our complementary analyses led us to 2 novel malaria diagnostic biomarkers compatible for use in an RDT format: glyceraldehyde 3-phosphate dehydrogenase and dihydrofolate reductase-thymidylate synthase. CONCLUSIONS: Overall, our results pave the way for the development of next-generation malaria RDTs based on new antigens by identifying 2 lead candidates with favorable diagnostic features and partially de-risked product development prospects.

Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy.

Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy-Cincinnati Children's Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.

Benzylic oxidation of gemfibrozil-1-O-beta-glucuronide by P450 2C8 leads to heme alkylation and irreversible inhibition.

Gemfibrozil-1-O-beta-glucuronide (GEM-1-O-gluc), a major metabolite of the antihyperlipidemic drug gemfibrozil, is a mechanism-based inhibitor of P450 2C8 in vitro, and this irreversible inactivation may lead to clinical drug-drug interactions between gemfibrozil and other P450 2C8 substrates. In light of this in vitro finding and the observation that the glucuronide conjugate does not contain any obvious structural alerts, the current study was conducted to determine the potential site of GEM-1-O-gluc bioactivation and the subsequent mechanism of P450 2C8 inhibition (i.e., modification of apoprotein or heme). LC/MS analysis of a reaction mixture containing recombinant P450 2C8 and GEM-1-O-gluc revealed that the substrate was covalently linked to the heme prosthetic heme group during catalysis. A combination of mass spectrometry and deuterium isotope effects revealed that a benzylic carbon on the 2',5'-dimethylphenoxy group of GEM-1-O-gluc was covalently bound to the heme of P450 2C8. The regiospecificity of substrate addition to the heme group was not confirmed experimentally, but computational modeling experiments indicated that the gamma-meso position was the most likely site of modification. The metabolite profile, which consisted of two benzyl alcohol metabolites and a 4'-hydroxy-GEM-1-O-gluc metabolite, indicated that oxidation of GEM-1-O-gluc was limited to the 2',5'-dimethylphenoxy group. These results are consistent with an inactivation mechanism wherein GEM-1-O-gluc is oxidized to a benzyl radical intermediate, which evades oxygen rebound, and adds to the gamma-meso position of heme. Mechanism-based inhibition of P450 2C8 can be rationalized by the formation of the GEM-1-O-gluc-heme adduct and the consequential restriction of additional substrate access to the catalytic iron center.

Plasma protein patterns as comprehensive indicators of health.

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Connecting genetic risk to disease end points through the human blood plasma proteome.

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Discovery of a Novel Class of Imidazo[1,2-a]Pyridines with Potent PDGFR Activity and Oral Bioavailability.

The in silico construction of a PDGFRß kinase homology model and ensuing medicinal chemistry guided by molecular modeling, led to the identification of potent, small molecule inhibitors of PDGFR. Subsequent exploration of structure-activity relationships (SAR) led to the incorporation of a constrained secondary amine to enhance selectivity. Further refinements led to the integration of a fluorine substituted piperidine, which resulted in significant reduction of P-glycoprotein (Pgp) mediated efflux and improved bioavailability. Compound 28 displayed oral exposure in rodents and had a pronounced effect in a pharmacokinetic-pharmacodynamic (PKPD) assay.

Malarial kinases: novel targets for in silico approaches to drug discovery.

Malaria, the disease caused by infection with protozoan parasites from the genus Plasmodium, claims the lives of nearly 1 million people annually. Developing nations, particularly in the African Region, bear the brunt of this malaria burden. Alarmingly, the most dangerous etiologic agent of malaria, Plasmodium falciparum, is becoming increasingly resistant to current first-line antimalarials. In light of the widespread devastation caused by malaria, the emergence of drug-resistant P. falciparum strains, and the projected decrease in funding for malaria eradication that may occur over the next decade, the identification of promising new targets for antimalarial drug design is imperative. P. falciparum kinases have been proposed as ideal drug targets for antimalarial drug design because they mediate critical cellular processes within the parasite and are, in many cases, structurally and mechanistically divergent when compared with kinases from humans. Identifying a molecule capable of inhibiting the activity of a target enzyme is generally an arduous and expensive process that can be greatly aided by utilizing in silico drug design techniques. Such methods have been extensively applied to human kinases, but as yet have not been fully exploited for the exploration and characterization of antimalarial kinase targets. This review focuses on in silico methods that have been used for the evaluation of potential antimalarials and the Plasmodium kinases that could be explored using these techniques.

In silico modeling of p450 substrates, inhibitors, activators, and inducers.

Cytochrome P450 enzymes are the predominant mediators of phase I metabolism of exogenous small molecules. As a result of their extensive role in metabolism of xenobiotics, drug compounds, and endogenous compounds, as well as their wide tissue distribution, significant drug discovery resources are spent to avoid interacting with this class of enzymes. Here we review historical and recent in silico modeling of 7 cytochrome P450 enzymes of particular interest, specifically CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4. For each we provide a brief biological background including known inhibitors, substrates, and inducers, as well as details of computational modeling efforts and advances in structural biology. We also provide similar details for 3 nuclear receptors known to regulate gene expression of these enzyme families.

Improving the performance of self-organizing maps via growing representations.

Self-organizing maps (SOMs) are a type of artificial neural network that through training can produce simplified representations of large, high dimensional data sets. These representations are typically used for visualization, classification, and clustering and have been successfully applied to a variety of problems in the pharmaceutical and bioinformatics domains. SOMs in these domains have generally been restricted to static sets of nodes connected in either a grid or hexagonal connectivity and planar or toroidal topologies. We investigate the impact of connectivity and topology on SOM performance, and experiments were performed on fixed and growing SOMs. Three synthetic and two relevant data sets from the chemistry domain were used for evaluation, and performance was assessed on the basis of topological and quantization errors after equivalent training periods. Although we found that all SOMs were roughly comparable at quantizing a data space, there was wide variation in the ability to capture its underlying structure, and growing SOMs consistently outperformed their static counterparts in regards to topological errors. Additionally, one growing SOM, the Neural Gas, was found to be far more capable of capturing details of a target data space, finding lower dimensional relationships hidden within higher dimensional representations.

Induction of decision trees via evolutionary programming.

Decision trees have been used extensively in cheminformatics for modeling various biochemical endpoints including receptor-ligand binding, ADME properties, environmental impact, and toxicity. The traditional approach to inducing decision trees based upon a given training set of data involves recursive partitioning which selects partitioning variables and their values in a greedy manner to optimize a given measure of purity. This methodology has numerous benefits including classifier interpretability and the capability of modeling nonlinear relationships. The greedy nature of induction, however, may fail to elucidate underlying relationships between the data and endpoints. Using evolutionary programming, decision trees are induced which are significantly more accurate than trees induced by recursive partitioning. Furthermore, when assessed on previously unseen data in a 10-fold cross-validated manner, evolutionary programming induced trees exhibit a significantly higher accuracy on previously unseen data. This methodology is compared to single-tree and multiple-tree recursive partitioning in two domains (aerobic biodegradability and hepatotoxicity) and shown to produce less complex classifiers with average increases in predictive accuracy of 5-10% over the traditional method.