We report the complete de novo design of a four-helix bundle protein that selectively binds the nonbiological DPP-Fe(III) metalloporphyrin cofactor (DPP-Fe(III) = 5, 15-Di[(4-carboxymethyleneoxy)phenyl]porphinato iron(III)). A tetrameric, D2-symmetric backbone scaffold was constructed to encapsulate two DPP-Fe(III) units through bis(His) coordination. The complete sequence was determined with the aid of the statistical computational design algorithm SCADS. The 34-residue peptide was chemically synthesized. UV-vis and CD spectroscopy, size-exclusion chromatography, and analytical ultracentrifugation indicated the peptide undergoes a transition from a predominantly random coil monomer to an alpha-helical tetramer upon binding DPP-Fe(III). EPR spectroscopy studies indicated the axial imidazole ligands were oriented in a perpendicular fashion, as defined by second-shell interactions that were included in the design. The 1-D 1H NMR spectrum of the assembled protein displayed features of a well-packed interior. The assembled protein possessed functional redox properties different from those of structurally similar systems containing the heme cofactor. The designed peptide demonstrated remarkable cofactor selectivity with a significantly weaker binding affinity for the natural heme cofactor. These findings open a path for the selective incorporation of more elaborate cofactors into designed scaffolds for constructing molecularly well-defined nanoscale materials.

There is a critical need for compounds that target cell surface integrin receptors for applications in cancer therapy and diagnosis. We used directed evolution to engineer the Ecballium elaterium trypsin inhibitor (EETI-II), a knottin peptide from the squash family of protease inhibitors, as a new class of integrin-binding agents. We generated yeast-displayed libraries of EETI-II by substituting its 6-amino acid trypsin binding loop with 11-amino acid loops containing the Arg-Gly-Asp (RGD) integrin binding motif and randomized flanking residues. These libraries were screened in a high-throughput manner by fluorescence-activated cell sorting to identify mutants that bound to αvβ3 integrin. Select peptides were synthesized and were shown to compete for natural ligand binding to integrin receptors expressed on the surface of U87MG glioblastoma cells with half-maximal inhibitory concentration (IC50) values of 10-30 nM. Receptor specificity assays demonstrated that engineered knottin peptides bind to both αvβ3 and αvβ5 integrins with high affinity. Interestingly, we also discovered a peptide that binds with high affinity to αvβ3, αvβ5, and α5β1 integrins. This finding has important clinical implications since all three of these receptors can be co-expressed on tumors. In addition, we showed that engineered knottin peptides inhibit tumor cell adhesion to the extracellular matrix protein vitronectin, and in some cases fibronectin, depending on their integrin binding specificity. Collectively, these data validate EETI-II as a scaffold for protein engineering, and highlight the development of unique integrin-binding peptides with potential for translational applications in cancer.

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11090563/-/DC1.

BackgroundIncreasing efforts and financial resources are being invested in early cancer detection research. Blood assays detecting tumor biomarkers promise noninvasive and financially reasonable screening for early cancer with high potential of positive impact on patients' survival and quality of life. For novel tumor biomarkers, the actual tumor detection limits are usually unknown and there have been no studies exploring the tumor burden detection limits of blood tumor biomarkers using mathematical models. Therefore, the purpose of this study was to develop a mathematical model relating blood biomarker levels to tumor burden.Methods and FindingsUsing a linear one-compartment model, the steady state between tumor biomarker secretion into and removal out of the intravascular space was calculated. Two conditions were assumed: (1) the compartment (plasma) is well-mixed and kinetically homogenous; (2) the tumor biomarker consists of a protein that is secreted by tumor cells into the extracellular fluid compartment, and a certain percentage of the secreted protein enters the intravascular space at a continuous rate. The model was applied to two pathophysiologic conditions: tumor biomarker is secreted (1) exclusively by the tumor cells or (2) by both tumor cells and healthy normal cells. To test the model, a sensitivity analysis was performed assuming variable conditions of the model parameters. The model parameters were primed on the basis of literature data for two established and well-studied tumor biomarkers (CA125 and prostate-specific antigen [PSA]). Assuming biomarker secretion by tumor cells only and 10% of the secreted tumor biomarker reaching the plasma, the calculated minimally detectable tumor sizes ranged between 0.11 mm3 and 3,610.14 mm3 for CA125 and between 0.21 mm3 and 131.51 mm3 for PSA. When biomarker secretion by healthy cells and tumor cells was assumed, the calculated tumor sizes leading to positive test results ranged between 116.7 mm3 and 1.52 × 106 mm3 for CA125 and between 27 mm3 and 3.45 × 105 mm3 for PSA. One of the limitations of the study is the absence of quantitative data available in the literature on the secreted tumor biomarker amount per cancer cell in intact whole body animal tumor models or in cancer patients. Additionally, the fraction of secreted tumor biomarkers actually reaching the plasma is unknown. Therefore, we used data from published cell culture experiments to estimate tumor cell biomarker secretion rates and assumed a wide range of secretion rates to account for their potential changes due to field effects of the tumor environment.ConclusionsThis study introduced a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays. Assuming physiological data on CA125 and PSA from the literature, the model predicted detection limits of tumors that were in qualitative agreement with the actual clinical performance of both biomarkers. The model may be helpful in future estimation of minimal detectable ...