| Literature DB >> 27182050 |
Tricia L Gearhart1, Ronald C Montelaro1, Mark E Schurdak2, Chris D Pilcher3, Charles R Rinaldo4, Thomas Kodadek5, Yongseok Park4, Kazi Islam6, Raymond Yurko6, Ernesto T A Marques7, Donald S Burke8.
Abstract
Non-biological synthetic oligomers can serve as ligands for antibodies. We hypothesized that a random combinatorial library of synthetic poly-N-substituted glycine oligomers, or peptoids, could represent a random "shape library" in antigen space, and that some of these peptoids would be recognized by the antigen-binding pocket of disease-specific antibodies. We synthesized and screened a one bead one compound combinatorial library of peptoids, in which each bead displayed an 8-mer peptoid with ten possible different amines at each position (10(8) theoretical variants). By screening one million peptoid/beads we found 112 (approximately 1 in 10,000) that preferentially bound immunoglobulins from human sera known to be positive for anti-HIV antibodies. Reactive peptoids were then re-synthesized and rigorously evaluated in plate-based ELISAs. Four peptoids showed very good, and one showed excellent, properties for establishing a sero-diagnosis of HIV. These results demonstrate the feasibility of constructing sero-diagnostic assays for infectious diseases from libraries of random molecular shapes. In this study we sought a proof-of-principle that we could identify a potential diagnostic antibody ligand biomarker for an infectious disease in a random combinatorial library of 100 million peptoids. We believe that this is the first evidence that it is possible to develop sero-diagnostic assays - for any infectious disease - based on screening random libraries of non-biological molecular shapes.Entities:
Keywords: Biomarker; Diagnostic; ELISA; HIV; Infectious disease; Peptoid
Mesh:
Substances:
Year: 2016 PMID: 27182050 PMCID: PMC4947968 DOI: 10.1016/j.jim.2016.05.001
Source DB: PubMed Journal: J Immunol Methods ISSN: 0022-1759 Impact factor: 2.303
Fig. 1Peptoid Screening and Evaluation Flowcharts. a. Flowchart of peptoid library screening. b. Flowchart of reactive peptoid evaluation and diagnostic peptoid selection.
Fig. 2Diagnostic candidate peptoids can distinguish between individual HIV positive and HIV negative serum samples. a–d. Each point represents the average optical density of duplicate samples of an individual serum sample in the standard peptoid ELISA. Horizontal and vertical lines represent the mean and standard deviation respectively. With all four candidate peptoids in a–d, optical density values obtained were greater on assays using known HIV positive sera than with known HIV negative sera (p < 0.001, Student' t-test).
Fig. 3Analysis of potential diagnostic biomarker peptoid HIV-DxP-1: a. chemical structure and sequence of HIV-DxP-1 (see methods for chemical names of peptoid residues). b. Peptoid ELISA analysis of HIV-DxP-1 against 162 HIV positive and 49 HIV negative individual serum samples. Each symbol represents the average optical density of duplicate runs on an individual serum sample. Horizontal and vertical lines represent the means and standard deviations, respectively. Optical density values obtained were greater on assays using known HIV positive sera than with known HIV negative sera (p < 0.0001, Student' t-test). c. Receiver Operating Characteristic (ROC) curve of peptoid ELISA data showing sensitivity versus (100-specificity) for varying cut-off values.