Thermodynamic Additivity of Sequence Variations: An Algorithm for Creating High Affinity Peptides Without Large Libraries or Structural Information

BACKGROUND: There is a significant need for affinity reagents with high target affinity/specificity that can be developed rapidly and inexpensively. Existing affinity reagent development approaches, including protein mutagenesis, directed evolution, and fragment-based design utilize large libraries and/or require structural information thereby adding time and expense. Until now, no systematic approach to affinity reagent development existed that could produce nanomolar affinity from small chemically synthesized peptide libraries without the aid of structural information. METHODOLOGY/PRINCIPAL FINDINGS: Based on the principle of additivity, we have developed an algorithm for generating high affinity peptide ligands. In this algorithm, point-variations in a lead sequence are screened and combined in a systematic manner to achieve additive binding energies. To demonstrate this approach, low-affinity lead peptides for multiple protein targets were identified from sparse random sequence space and optimized to high affinity in just two chemical steps. In one example, a TNF-α binding peptide with K(d) = 90 nM and high target specificity was generated. The changes in binding energy associated with each variation were generally additive upon combining variations, validating the basis of the algorithm. Interestingly, cooperativity between point-variations was not observed, and in a few specific cases, combinations were less than energetically additive. CONCLUSIONS/SIGNIFICANCE: By using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-α binding peptides reported to date was produced. Most importantly, high affinity was achieved from small, chemically-synthesized libraries without the need for structural information at any time during the process. This is significantly different than protein mutagenesis, directed evolution, or fragment-based design approaches, which rely on large libraries and/or structural guidance. With this algorithm, high affinity/specificity peptide ligands can be developed rapidly, inexpensively, and in an entirely chemical manner

Discovery of High-Affinity Protein Binding Ligands – Backwards

BACKGROUND: There is a pressing need for high-affinity protein binding ligands for all proteins in the human and other proteomes. Numerous groups are working to develop protein binding ligands but most approaches develop ligands using the same strategy in which a large library of structured ligands is screened against a protein target to identify a high-affinity ligand for the target. While this methodology generates high-affinity ligands for the target, it is generally an iterative process that can be difficult to adapt for the generation of ligands for large numbers of proteins. METHODOLOGY/PRINCIPAL FINDINGS: We have developed a class of peptide-based protein ligands, called synbodies, which allow this process to be run backwards--i.e. make a synbody and then screen it against a library of proteins to discover the target. By screening a synbody against an array of 8,000 human proteins, we can identify which protein in the library binds the synbody with high affinity. We used this method to develop a high-affinity synbody that specifically binds AKT1 with a K(d)<5 nM. It was found that the peptides that compose the synbody bind AKT1 with low micromolar affinity, implying that the affinity and specificity is a product of the bivalent interaction of the synbody with AKT1. We developed a synbody for another protein, ABL1 using the same method. CONCLUSIONS/SIGNIFICANCE: This method delivered a high-affinity ligand for a target protein in a single discovery step. This is in contrast to other techniques that require subsequent rounds of mutational improvement to yield nanomolar ligands. As this technique is easily scalable, we believe that it could be possible to develop ligands to all the proteins in any proteome using this approach

A Technology for Developing Synbodies with Antibacterial Activity

<div><p>The rise in antibiotic resistance has led to an increased research focus on discovery of new antibacterial candidates. While broad-spectrum antibiotics are widely pursued, there is evidence that resistance arises in part from the wide spread use of these antibiotics. Our group has developed a system to produce protein affinity agents, called synbodies, which have high affinity and specificity for their target. In this report, we describe the adaptation of this system to produce new antibacterial candidates towards a target bacterium. The system functions by screening target bacteria against an array of 10,000 random sequence peptides and, using a combination of membrane labeling and intracellular dyes, we identified peptides with target specific binding or killing functions. Binding and lytic peptides were identified in this manner and <em>in vitro</em> tests confirmed the activity of the lead peptides. A peptide with antibacterial activity was linked to a peptide specifically binding <em>Staphylococcus aureus</em> to create a synbody with increased antibacterial activity. Subsequent tests showed that this peptide could block <em>S. aureus</em> induced killing of HEK293 cells in a co-culture experiment. These results demonstrate the feasibility of using the synbody system to discover new antibacterial candidate agents.</p> </div

Testing of synbody toxicity and demonstration of a protective effect on human cells in co-culture with <i>S. aureus</i>.

<p>(<b>A</b>) Hemolytic activity of SA synbody and individual peptides on mouse red blood cells. (<b>B</b>) Test of synbody cytotoxicity for HEK293 cells versus original peptides. The synbody was added to cells for 48 hours and cell viability was measured by BrdU proliferation assay. (<b>C</b>) Viability of HEK293 cells in co-culture with <i>S. aureus</i> with and without synbody treatment, as measured by cellular ATP content measurement. Data are normalized to the cellular ATP content of cells only. Synbody (25 µM), peptide (25 µM) or antibiotic control (180 µM) was added to co-culture immediately after mixing. The error bars represent the standard deviation of triplicate measurements. (<b>D</b>) Light microscopy (10×) of cells only, cells in co-culture with <i>S. aureus</i> for 24 hours, cells treated with 25 µM RW peptide and cells treated with 25 µM synbody.</p

Validation of microarray predicted lytic activities of peptides.

<p>(<b>A</b>) Relative growth inhibition of EC, PA, SA, SM and BS by peptides HWK, RWR, DRI, HPW, HKH at 100 µM. End-point measurement after 18 h. “+ C” - positive control kanamycin, 100 µM. L – microarray predicted lytic activity of peptide B – binding activity of peptide. N – no microarray profile. The error bars are standard deviations of triplicate measurements. (<b>B</b>) Cultures were plated after 5 minute incubation with 100 µM of binding or lytic peptides. Pictures were taken after 24 hours growth. Upper plate is negative control for each strain. Peptide-binders: DRI for SA; KQK for BS. Lytic peptides: RWR for SA; HRK for BS.</p

Demonstration of bacteria binding to peptide microarrays.

<p>(<b>A</b>) Binding data of CTO stained EC (x axes) plotted vs negative control (y axes). Both axes show raw median fluorescent signal at 543 nm on a logarithmic scale. Green lines delimit the twofold change. Dark dots outside of two-fold change are binder-candidates. (<b>B</b>) EC binding to peptides annotated in (A) on custom polymer microarray detected by fluorescent microscopy. Upper left image is negative control (non-binding peptide EFSN). Scale – 100 µm. (<b>C</b>) Binding and competition dataset for SM and SA. Annotated dark dots are peptide-binders for EC selected in (A) and demonstrate binding specificity.</p

Selection of binding and lytic peptides from microarray screening.

<p>(<b>A</b>) Scatter plots comparing binding/competition data for SA with intracellular stain CTO (left) and membrane label AF (right). Both axes show raw median fluorescent signal at 543 nm on a logarithmic scale. Green lines delimit the twofold change. Peptide-binders (dark dots) are selected out of twofold change on x axes as those where CTO-cells were competed with excess of non-stained cells and repeated with AF labeled cells. Other peptides out of twofold change on x axes at SA-AF (red dots) are considered lytic as they were not detected with CTO. (<b>B</b>) Microarray binding of CTO stained vs AF labeled EC, PA, SA, SM and BS for peptides HWK, RWR, DRI, HPW (spotted in duplicate). L = lytic peptide (CTO-AF+). B = binding peptide (CTO+AF+). N = no binding. (<b>C</b>) Specificity and uniqueness of bacterial profiles at peptide microarray in binding peptides dataset (<b>D</b>) and for lytic peptides dataset. SA data presented in Venn diagrams have had SM and BS binding peptides filtered out.</p

<i>In vitro</i> characterization of <i>S. aureus</i> synbody.

<p>(<b>A</b>) Structure of <i>S. aureus</i> synbody. (<b>B</b>) Bacterial growth of <i>S. aureus</i> over time after treatment with peptides or synbody. Measurements were taken hourly. Data points represent the average of three independent experiments. Starting <i>S. aureus</i> concentration is ∼2×10⁵ CFU/mL. (<b>C</b>) Test of lytic activity of lytic peptide and synbody. <i>S. aureus</i> was grown until ∼1.7×10⁹ CFU/mL and the peptide or synbody was added to the final concentration of 100 µM at time zero. Data points represent the average of three independent experiments.</p

Bacteria binding to peptide microarrays.

<p>(<b>A</b>) Workflow to develop pathogen specific antibiotics. Bacterial cells are applied to the peptide microarray carrying dyes either in cytoplasm or on the membrane. Intracellular dye Cell Tracker Orange (CTO) identifies peptides that bind bacterial cells without disrupting the cell membrane while the outer membrane label Alexa Fluor 555 identifies either intact or dead cells. Comparing the profiles of a pathogen at the same peptide sequence enables the selection of peptides with binding or lytic activity. After <i>in vitro</i> validation, linking together a peptide with antimicrobial activity and a specific binder for the pathogen produces a synbody. (<b>B</b>) Distinct profiles of CTO stained <i>E.coli</i> O111:B4 (EC), <i>P. aeruginosa</i> (PA), <i>S. aureus</i> (SA), <i>S. mutans</i> (SM), <i>B. subtilis</i> (BS) on representative sub-array (48 peptides from 10,000 total). Cell binding signals are depicted as a false color (green).</p