Skip to main content

Main menu

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
  • Front Matter
    • Front Matter Portal
    • Journal Club
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Fees and Licenses
  • Submit
  • Submit
  • About
    • Editorial Board
    • PNAS Staff
    • FAQ
    • Accessibility Statement
    • Rights and Permissions
    • Site Map
  • Contact
  • Journal Club
  • Subscribe
    • Subscription Rates
    • Subscriptions FAQ
    • Open Access
    • Recommend PNAS to Your Librarian

User menu

  • Log in
  • My Cart

Search

  • Advanced search
Home
Home
  • Log in
  • My Cart

Advanced Search

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
  • Front Matter
    • Front Matter Portal
    • Journal Club
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Fees and Licenses
  • Submit
Research Article

Xenoprotein engineering via synthetic libraries

Zachary P. Gates, Alexander A. Vinogradov, Anthony J. Quartararo, Anupam Bandyopadhyay, Zi-Ning Choo, Ethan D. Evans, Kathryn H. Halloran, Alexander J. Mijalis, Surin K. Mong, Mark D. Simon, Eric A. Standley, Evan D. Styduhar, Sarah Z. Tasker, Faycal Touti, Jessica M. Weber, Jessica L. Wilson, Timothy F. Jamison, and Bradley L. Pentelute
  1. aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139

See allHide authors and affiliations

PNAS June 5, 2018 115 (23) E5298-E5306; first published May 21, 2018; https://doi.org/10.1073/pnas.1722633115
Zachary P. Gates
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zgates@mit.edu blp@mit.edu
Alexander A. Vinogradov
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anthony J. Quartararo
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anupam Bandyopadhyay
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zi-Ning Choo
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ethan D. Evans
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kathryn H. Halloran
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alexander J. Mijalis
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Surin K. Mong
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mark D. Simon
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eric A. Standley
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Evan D. Styduhar
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sarah Z. Tasker
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Faycal Touti
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jessica M. Weber
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jessica L. Wilson
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Timothy F. Jamison
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bradley L. Pentelute
aDepartment of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: zgates@mit.edu blp@mit.edu
  1. Edited by David Baker, University of Washington, Seattle, WA, and approved April 20, 2018 (received for review December 29, 2017)

  • Article
  • Figures & SI
  • Info & Metrics
  • PDF
Loading

Significance

Combinatorial protein libraries—prepared via molecular biology-based approaches—are invaluable tools for protein engineering. The inclusion of noncanonical amino acids in such libraries is of considerable interest. However, at present no approach competes with chemical synthesis in terms of the variety and number of noncanonical amino acids that can be simultaneously incorporated into a protein molecule. Here, we describe selection from synthetic libraries as a strategy for protein engineering. The approach enables identification of small (∼30 aa), functional protein variants comprising a virtually unlimited variety of noncanonical amino acids. Increasing the throughput of synthetic library screening, which was achieved through this effort, is anticipated to improve the utility of synthetic libraries for identifying polypeptide-based ligands with de novo function.

Abstract

Chemical methods have enabled the total synthesis of protein molecules of ever-increasing size and complexity. However, methods to engineer synthetic proteins comprising noncanonical amino acids have not kept pace, even though this capability would be a distinct advantage of the total synthesis approach to protein science. In this work, we report a platform for protein engineering based on the screening of synthetic one-bead one-compound protein libraries. Screening throughput approaching that of cell surface display was achieved by a combination of magnetic bead enrichment, flow cytometry analysis of on-bead screens, and high-throughput MS/MS-based sequencing of identified active compounds. Direct screening of a synthetic protein library by these methods resulted in the de novo discovery of mirror-image miniprotein-based binders to a ∼150-kDa protein target, a task that would be difficult or impossible by other means.

  • xenoprotein
  • mirror-image miniprotein
  • D-protein
  • protein engineering
  • flow cytometry

Xenoproteins—protein molecules composed of noncanonical amino acids—might exhibit function not readily achieved by the use of proteogenic amino acids alone (1⇓⇓⇓⇓⇓⇓⇓–9) and other favorable properties such as protease stability or altered immunogenicity (10). Chemical protein synthesis is a powerful approach for incorporating a virtually unlimited variety of noncanonical amino acids into a protein molecule for biophysical and structure–function studies (11⇓⇓⇓⇓⇓⇓⇓⇓–20). However, no experimental approach exists to differentiate beneficial versus deleterious mutations in a synthetic protein molecule with significant throughput. Such an approach would facilitate the discovery of functional xenoproteins, since incorporation of noncanonical amino acids into protein molecules is frequently deleterious (21⇓–23).

Identification of functional variants from synthetic combinatorial libraries is a potential solution to the challenge of xenoprotein engineering (24⇓–26). The synthesis of small proteins is straightforward with solid-phase peptide synthesis (SPPS); however, the screening of high-diversity synthetic libraries presents a formidable challenge. Generally, synthetic libraries have been limited to peptide and peptidomimetic structures comprising relatively short polymers with a limited number of varied positions. This is in contrast to molecular biology-based screening and selection strategies, which can routinely examine at least 107–108 variants of large protein molecules, such as antibody fragments, that contain point mutations across the entire polypeptide chain (27).

The one-bead one-compound (OBOC) approach (28) is amenable to the preparation of high-diversity synthetic libraries, provided that sufficiently small resin beads are employed. For example, 1 g of 10-μm TentaGel resin contains ∼2 × 109 beads. The screening of at least ∼3 × 106 compounds by the OBOC approach has been reported (29); however, typical screens examine fewer than 106 compounds (30). The challenge of handling high-diversity OBOC libraries can be attributed to the labor associated with manually screening large numbers of beads (31) and to de novo sequencing of active compounds using the limited amount of material present on 30- or 10-μm beads (4 pmol or 100 fmol, respectively, for amine loadings of 0.2 mmol/g).

We set out to render practical the screening of high-diversity synthetic libraries and to explore the use of synthetic protein libraries for engineering de novo binding activity into a mirror-image miniprotein molecule (MIM). This task was chosen to illustrate the utility of chemical synthesis for xenoprotein engineering, since a MIM is composed entirely of noncanonical amino acids, with the exception of glycine. The overall approach is shown in Fig. 1. Key steps are the synthesis of folded protein variants bound to 30-μm beads, high-throughput analysis of on-bead screens by flow cytometry, and de novo sequencing of identified active compound mixtures by a recently developed liquid chromatography/tandem mass spectrometry (LC-MS/MS) approach (32). These steps were individually optimized and then combined to discover MIM-based binders to a monoclonal antibody target. The results constitute important proof of concept for the synthetic library approach to xenoprotein engineering, which should in principle be applicable to the discovery of functional xenoproteins based on a variety of other small protein scaffolds.

Fig. 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 1.

An approach to identifying binders from synthetic protein libraries. Thirty-micrometer beads displaying xenoprotein variants are prepared by a combination of stepwise SPPS and on-bead folding. The resulting beads are incubated with a protein target bearing a fluorescent label (red star), and beads displaying functional xenoprotein variants are isolated by fluorescence-activated sorting (FACS). The sequences of xenoproteins contained on sorted beads are then determined by de novo MS/MS-based peptide sequencing.

Results and Discussion

EETI-II Is a Robust Scaffold for the Display of Chemical Diversity.

As a molecular scaffold (33) for the generation of a mirror-image protein library we selected the trypsin inhibitor from Ecballium elaterium, EETI-II (Fig. 2). This 29-residue protein molecule is amenable to chemical synthesis (34), is known to oxidatively fold spontaneously despite sequence variation of the trypsin-binding loop (23, 35⇓⇓–38), and has been successfully engineered to bind αvβ3 and αvβ5 integrins based on the Arg–Gly–Asp motif using yeast-surface display (39, 40). The broad tolerance of the EETI-II molecule to loop expansion and sequence variation (38) is typical of cystine knot proteins generally (41), and we anticipated that this feature could be leveraged to prepare synthetic libraries of folded variants for the selection of novel binding proteins. To confirm this important property of the EETI-II molecule, we tested the ability of a number of synthetic EETI-II variants, containing nonnative loop sequences, to fold spontaneously (Fig. 2C and SI Appendix, section 3). In all cases (20 shown), a single major product containing three disulfide bonds was formed. These data suggest that in a combinatorial library of EETI-II variants, many will be present as folded cystine knots, which likely retain the disulfide connectivity and general tertiary structure of the native EETI-II molecule (37, 42).

Fig. 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 2.

EETI-II is a robust scaffold for the display of chemical diversity. (A) Amino acid sequences of EETI-II and a synthetic library scaffold based on mirror-image EETI-II. d-amino acids are in lowercase; cysteine residues (all in disulfide form) are underlined. Residues corresponding to the trypsin-binding loop are shown in green. (B) Cartoon rendering of the EETI-II molecule, based on Protein Data Bank ID code 1W7Z. The trypsin-binding loop is shown in green, and the three disulfide bonds as yellow sticks. (C) LC-MS data showing the spontaneous oxidative folding of a synthetic EETI-II variant with a nonnative trypsin-binding loop sequence. A loss of 6 Da was observed upon treatment with soluble redox buffer (SI Appendix, section 3), consistent with the formation of three disulfide bonds. Calculated and observed monoisotopic masses are indicated. (D) Stability of l- and d-forms of two engineered EETI-II variants to proteinase K. The fraction of intact protein remaining at each time point was determined by LC-MS–based quantitation (average of two measurements).

The susceptibility of l-polypeptides to protease digestion is a major drawback to their use in vivo, which has motivated the development of binding molecules based on d-polypeptides (43, 44) and d-proteins (10). Cystine knot proteins sometimes exhibit protease resistance, but this is not general (45). To demonstrate a significant advantage of d-protein molecules, we investigated the stability of several mirror-image EETI-II variants to proteinase K. Minimal degradation was observed over a period of 24 h, whereas the corresponding l-proteins survived less than 1 h under identical conditions (Fig. 2D and SI Appendix, section 4). Taken together, these data support the notion that mirror-image EETI-II is a robust scaffold for the generation of binding molecules, which may be of practical utility in biotechnology.

Protein-Based Fluorophores Enable Flow Cytometry Analysis of On-Bead Assays.

Flow cytometry is a powerful technique for high-throughput analysis of cell-surface display libraries (46⇓–48), and we sought to adopt its use for bead-based libraries, to increase screening throughput (49⇓–51). An initial goal was to maximize the fluorescence contrast between beads displaying a known protein ligand versus beads displaying nonbinding ligands, after incubation with a fluorescently labeled target protein. We used SPPS to prepare 30-μm TentaGel beads functionalized with the streptavidin (SA)-binding peptide StrepTag II (52) and assayed them for binding to a variety of commercially available SA conjugates alongside samples of beads displaying random peptides. SA conjugates were chosen based on compatibility with 633-nm excitation, to minimize bead autofluorescence, and included SA-Alexa Fluor 633 (SA-AF633), SA-allophycocyanin (SA-APC), and SA-quantum dot 655 (SA-QD).

Of the fluorophores examined, only the protein-based APC gave acceptable contrast between StrepTag II beads and library beads (Fig. 3A and SI Appendix, section 6). The comparatively poor performance of AF633 and QD fluorophores was due to a combination of 10- to 15-fold higher background fluorescence of the library beads, and approximately fivefold lower fluorescence of the bound StrepTag II beads. The propensity of highly charged, small-molecule fluorophores such as AF to bind nonspecifically to library beads has been noted (53) and may be responsible for the high level of background binding observed with SA-AF633.

Fig. 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 3.

Flow cytometry is amenable to monitoring on-bead binding assays. (A) Fluorescence histograms showing the contrast between library beads (cyan) and StrepTag II beads (maroon) achieved by the use of different fluorescent SA reagents. (B) Fluorescence histograms showing protein target-dependent binding to beads functionalized with the indicated peptide ligands. (C) A plot of the fluorescence means obtained by treatment of biotin-functionalized beads (four different bead loadings) with increasing concentrations of SA-APC. Gly-functionalized beads served as a negative control. (D) A plot of the fluorescence means obtained by treatment of IgG1 binder-functionalized beads with increasing concentrations of a fixed molar ratio of SA-APC and biotinylated polyclonal IgG1. Library beads served as a negative control.

SA-APC enabled detection of protein-specific binding to beads functionalized with ligands to a variety of different protein targets. For example, incubation of beads functionalized with either the nine-residue linear epitope (HA epitope) of an anti-hemagglutinin monoclonal antibody (clone 12CA5; anti-HA mAb 12CA5) (Fig. 3B, Middle) or an IgG1 Fc-binding peptide (Fig. 3B, Right) (54) with either SA-APC or a mixture of SA-APC and the appropriate biotinylated protein target resulted in target-dependent gains in fluorescence intensity. The ∼100-fold difference in fluorescence intensity observed for bound versus unbound beads was comparable to that observed in cell-surface binding assays (55); therefore, we concluded that flow cytometry is suitable for the analysis of on-bead binding assays, and potentially for on-bead screens (discussed below).

On-Bead Binding Assays Are Highly Sensitive.

For cell-surface binding assays, the fluorescence intensity of a cell displaying a bound ligand is directly proportional to ligand expression level (47, 55). To account for this effect, which could otherwise lead to the identification of weak binders displayed at high copy number, target binding is normalized based on binding to a coexpressed affinity tag, in a two-color experiment. We sought to determine whether a similar procedure would be required for beads, which reportedly exhibit substantial variation in amine loading (56) (moles of synthetic product per individual bead). To address this question, we prepared biotinylated TentaGel samples of varying biotin loading and assayed them by flow cytometry after incubation with SA-APC.

The fluorescence intensity of bound biotin beads varied only fivefold over a 1,000-fold range of biotin loadings (Fig. 3C), and a similar outcome was obtained for beads functionalized with varying levels of StrepTag II (SI Appendix, Fig. S38). These outcomes were not due to fluorescence quenching at high bead loadings: When incubated with mixtures of unlabeled SA and SA-APC, the fluorescence intensity of biotin-functionalized beads varied proportionally to the ratio of fluorescent and unlabeled SA (SI Appendix, Fig. S39). We concluded that a strategy to normalize bead fluorescence based on amine loading would not be required for success with bead-based screens, since the fluorescence intensity of a bound bead was largely invariant with bead loading. This result could be understood if the amount of protein target present on a bound bead were small relative to the lowest bead loading studied. Prior studies of on-bead binding assays found that only ∼0.002 μmol/g of the ligand present on a bead is involved in binding (57), which supports this interpretation.

For a variety of particle-based screen technologies, avidity effects can result in the identification of weak ligands. For example, SA-binding peptides with Kd of ∼300 μM (58) are readily detected with both on-bead assays (28, 59) and by phage display (60). A loading of 2 μmol/g was selected for use in library synthesis, to minimize the identification of weak binders to the SA-APC component of stain reagents (59). Fig. 3D shows the differentiation of IgG1 binder-functionalized beads of 2 μmol/g loading from library beads, over a range of stain reagent concentrations.

Functional EETI-II Can be Prepared on Beads.

We used an on-bead binding assay to test the combination of SPPS and on-bead folding for preparing functional miniproteins on beads, which was an important prerequisite for MIM library preparation. l-EETI-II was prepared by Fmoc chemistry SPPS on beads uniformly attenuated to 20 μmol/g amine loading, to minimize oxidative polymerization during on-bead folding (61) while retaining sufficient material for liquid chromatography-mass spectrometry (LC-MS) characterization (discussed below). Following SPPS and removal of side-chain protecting groups, beads were treated with the same conditions used for solution-based folding studies (SI Appendix, section 7). As a negative control, beads functionalized with EETI-II trypsin-binding loop sequence only were evaluated. These were expected not to bind trypsin, since intact disulfide bonds are required for trypsin binding (62).

EETI-II beads exhibited trypsin-dependent on-bead binding relative to library beads. In contrast, beads functionalized with trypsin-binding loop only were indistinguishable from library beads under the same conditions (Fig. 4 A and B and SI Appendix, section 8). These results were suggestive of the presence of folded EETI-II on beads, since the binding loop alone was insufficient for trypsin binding.

Fig. 4.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 4.

Folded EETI-II can be prepared on beads. (A) Fluorescence histograms showing the contrast between library beads (cyan) and either EETI-II beads (maroon, Right) or trypsin-binding loop beads (maroon, Left) after incubation with a mixture of SA-APC (50 nM) and biotinylated trypsin (100 nM). (B) Fluorescence means obtained by treatment of the same samples with a fixed concentration of SA-APC (50 nM) and one of two different concentrations of biotinylated trypsin. Trypsin-dependent binding was observed for the EETI-II beads only. (C) LC-MS data showing the spontaneous oxidative folding of EETI-II while bound to beads. (D) LC-MS data showing the spontaneous oxidative folding of an analog of the engineered EETI-II variant 2.5F, while bound to beads; † denotes a three-disulfide-containing product and * denotes a two-disulfide-containing product. For C and D, the indicated mass values correspond to monoisotopic masses.

As a second means of characterizing synthetic EETI-II on beads, we used LC-MS to follow the progress of on-bead oxidative folding. EETI-II was synthesized on beads functionalized with a PAM ester linker (63), which was stable to the conditions of side-chain deprotection but cleavable by stronger acid in a separate step. By cleavage of bead-bound EETI-II either before or after a folding treatment, we were able to evaluate the outcome of on-bead folding by LC-MS.

On-bead folding conditions effected the conversion of EETI-II to a single three-disulfide-linked product (64), as for the solution-based folding studies (Fig. 4C). To investigate the prospect of preparing folded EETI-II variants with expanded loops—necessary to accommodate large libraries—we examined an analog of the engineered EETI-II variant 2.5F (39) in the same assay. In this case, the desired three-disulfide product formed in an approximately one-to-three ratio with a two-disulfide intermediate (Fig. 4D). These data support the notion that folded, synthetic EETI-II variants with randomized loop sequences can be prepared on beads. However, the possibility of synthetic coproducts participating in screens must be kept in mind, as for any bead or cell surface-based screen. In the case of libraries based on EETI-II, the two-disulfide folding intermediate may be a common and significant coproduct.

High-Diversity Mirror-Image EETI-II Libraries Were Prepared on Bilayer Beads.

Having demonstrated the preparation of beads displaying functional EETI-II, we proceeded to construct a mirror-image EETI-II-based library, for use in screening (Fig. 5). Several considerations factored into the design of this strategy. First was the need to display a low density of synthetic protein in the accessible portion of the bead, while maintaining an adequate amount of material for sequencing. This need suggested the use of spatially segregated resin particles (59), which were prepared by an enzymatic shaving approach (65) (SI Appendix, section 9). Second was our inability to reliably assign de novo sequences to MS/MS spectra obtained from polypeptides longer than ∼15 aa residues. This limitation suggested the use of a “coding structure” of amino acid sequence identical to the varied portion of the MIM, contained in the bead interior, and coupled to a cleavable linker for release postscreening. The coding structure was installed by removal of an Aloc group on the bead interior, after SPPS of the MIM “constant region” (Fig. 5).

Fig. 5.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 5.

(A–C) Strategy for the preparation of a mirror-image EETI-II-based library.

Manual split-pool synthesis (28) of the MIM library was performed using 5 g of resin, which comprised more than 2 × 108 individual beads (SI Appendix, section 10). For each split, 15 protected d-amino acid monomers were employed (Cys, Arg, Ile, Asn, and Gln were excluded). The use of 15 possible monomers at each of nine varied positions equated to a theoretical diversity of 3.8 × 1010 compounds—roughly 200 times the number of beads used for synthesis. Thus, the totality of possible sequences was undersampled, as for many combinatorial protein libraries (27). We hypothesized that higher diversity would increase the odds of identifying consensus binding sequence motifs without a major penalty, since nonredundant libraries could still be redundant with respect to such motifs.

Protein Target-Dependent Binding Limits the Enrichment Attainable by Fluorescence-Activated Bead Sorting.

With mirror-image EETI-II-based libraries in hand, we sought to determine the enrichment that could be achieved by flow cytometry selection for on-bead binding, where enrichment is defined as (positive beads/total beads)sorted/(positive beads/total beads)analyzed (46). Flow cytometry routinely achieves enrichments of ∼10,000 (46, 66) for cell-surface display libraries. However, the sorting of beads is thought to be complicated by high background due to autofluorescence (67). To understand the severity of this issue, and other variables that might affect enrichment, we studied the frequency of sorted beads (>6,000 fluorescence counts) for two different bead samples—underivatized beads and library beads—under a variety of assay conditions (Table 1 and SI Appendix, section 11.1). Sort frequency sets an upper bound on enrichment, which can be expressed as (total beadsanalyzed/total beadssorted) × (positive beadssorted/positive beadsanalyzed).

View this table:
  • View inline
  • View popup
Table 1.

Binding of protein targets to chemical functionality on beads limits the enrichment attainable by flow cytometry selection

Enrichment was limited primarily by the binding of protein targets to chemical functionality on beads. The process of solid-phase synthesis did result in a 10-fold increase in the frequency of autofluorescent beads, as determined by comparison of library beads and underivatized beads in the absence of fluorescent protein (Table 1, entries 1 and 2); however, incubation of underivatized beads with either of two SA-APC conjugates resulted in more than 103-fold gains in sort frequency (Table 1, entries 3 and 4). Library beads displayed a higher degree of protein binding compared with underivatized beads, with sort frequencies 103- to 104-fold above the autofluorescence level (Table 1, entries 5 and 6 vs. entry 2). These sort frequencies corresponded to maximum enrichments of 15 or 5 for anti-HA mAb 12CA5 or thrombin, respectively, which were sufficiently poor as to make sorting untenable.

To minimize nonspecific binding of protein targets, we conducted assays in complex media. For both underivatized beads and library beads, use of FBS buffer resulted in lower sort frequencies compared with BSA alone (Table 1, entries 7–10). In FBS, library beads exhibited only slightly higher sort frequencies compared with underivatized beads, corresponding to maximum enrichment factors of ∼2 × 103. Enrichment factors on this order are close to those obtained for the sorting of bacterial and yeast cells, suggesting that acceptable conditions for on-bead screening had been identified. However, since functional MIM variants were anticipated to be less frequent than one in several thousand, we concluded that a preenrichment step would be required for a successful screen. Magnetic bead enrichment was explored, since this procedure is used to reduce the initial size of cell surface display libraries before sorting (68) and is capable of retaining even modest-affinity (Kd ∼ micromolar) binders with high yield (69).

A Two-Stage Magnetic Bead Selection/On-Bead Screen Improves Enrichment for Active Beads.

We investigated the utility of magnetic bead selection for improving enrichments achieved in screens of the mirror-image EETI-II library against anti-HA mAb 12CA5 and human α-thrombin, respectively. Library beads (400 mg, ∼2 × 107 beads) were incubated with SA-coated magnetic microparticles conjugated to the appropriate biotinylated target protein (SI Appendix, section 11.3). Retained library beads were isolated with the goal of maximizing recovery and incubated with the appropriate SA-APC conjugate in secondary screens. Two screens were carried out with anti-HA mAb, to assess reproducibility.

Magnetic bead selections for thrombin or 12CA5 binding retained 1–6% of library beads, corresponding to maximum enrichments of 16–90. Combined with the enrichment obtained by subsequent on-bead screens, overall maximum enrichments of up to 8.2 × 104 were obtained (Table 2). To evaluate whether the actual enrichments achieved were sufficient to identify functional variants, the material present on sorted beads was sequenced as described (32) (SI Appendix, section 11).

View this table:
  • View inline
  • View popup
Table 2.

Two-stage magnetic bead selection/on-bead screens can yield overall enrichments approaching 105

For the first of two screens against 12CA5, 9 of 16 sequence assignments contained a motif, yp*e*d/e, where * is any amino acid (SI Appendix, Fig. S55). This was in contrast to assignments from a single-pass screen of 1.5 × 106 beads, which lacked discernable similarity (SI Appendix, Fig. S53), suggesting that the magnetic bead selection preferentially retained positive beads. For the replicate 12CA5 screen, seven yp*e*d/e-containing sequences were obtained; however, these comprised a smaller fraction of the total (180), consistent with the lower overall enrichment obtained for the replicate screen (SI Appendix, Figs. S57 and S58). Likewise, no obvious similarity was discernable among sequences obtained for the thrombin screen, which had the lowest overall enrichment (SI Appendix, Fig. S60). These results highlight the importance of the magnetic bead selection and suggest that the protocol has yet to be optimized (70). Significantly higher enrichments can probably be achieved, since magnetic bead selection can enrich yeast cells 104-fold with near-quantitative yield (69).

Sequence Motifs Are Predictive of Reproducible On-Bead Binding.

To assess platform fidelity, we synthesized a select group of candidate 12CA5 binders for evaluation in an on-bead binding assay (SI Appendix, section 12). This experiment is analogous to the use of cell-surface binding assays to characterize active clones following selection by cell-surface display and would demonstrate the feasibility of our approach for identifying rare beads based on a reproducible screen criterion. Candidate sequences containing the yp*e*d motif were selected for evaluation, alongside several unrelated sequences.

With one exception, compounds containing the yp*e*d motif gave reproducible, 12CA5-dependent binding, similar to positive control beads functionalized with the HA epitope (Fig. 6, Table 3, and SI Appendix, Fig. S61). In contrast, candidate compounds lacking the yp*e*d motif were indistinguishable from negative control. These findings demonstrate the potential of our platform for isolating rare beads displaying active compounds and confirm the commonsense premise that emergence of a consensus binding sequence is indicative of a successful screen.

Fig. 6.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 6.

Replicate preparations of identified active library beads exhibit reproducible on-bead binding. Comparable 12CA5-dependent binding was observed for beads functionalized with either (A) HA epitope or (B) putative 12CA5-binding MIMs.

View this table:
  • View inline
  • View popup
Table 3.

On-bead binding activity of putative 12CA5-binding MIMs

Orthogonal Binding Assays Confirm the Activity of Mirror-Image EETI-II-Based Binders.

To confirm the activity of the identified MIM-based 12CA5 binders, select MIMs were synthesized for evaluation in off-bead assays (Fig. 7A and SI Appendix, section 13). In a biolayer interferometry assay (BLI), all MIMs tested exhibited a robust binding response to 12CA5 (SI Appendix, section 14.1). For three MIMs examined further, strong responses were also observed over a range of 12CA5 dilutions (SI Appendix, section 14.2). Individual kinetic traces were fit to a 1:1 binding model, which for compound 1 yielded average values of kon = 4.5 (±1.9) × 104 M−1 s−1, koff = 2.2 (±0.8) × 10−3 s−1, and Kd = 50 nM (22–120 nM) (Fig. 7B). Equilibrium binding responses were consistent with the Kd values obtained from kinetic analysis (50% of Rmax achieved at 22 nM), which provided an elementary test of self-consistency for the BLI data (71).

Fig. 7.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 7.

Orthogonal binding assays confirm the activity of 12CA5-binding MIMs. (A) LC-MS analysis of representative biotinylated 12CA5-binding MIM 1. (B) BLI assay, showing association of exogenous 12CA5 to immobilized MIM 1. (C) FP competition binding assay, showing the displacement of fluorescent HA epitope by unlabeled HA epitope (red) and MIM 1 (black).

Binding activity was investigated further using a fluorescence polarization-monitored competition assay (SI Appendix, section 15). MIM 1 was evaluated first and competed with fluorescent HA epitope for binding to 12CA5 (Fig. 7C, black trace). Based on the IC50 obtained from self-competition with unlabeled HA epitope (red trace), a Kd value of 460 ± 100 nM was obtained—∼10-fold weaker than the value obtained from BLI. Based on reported discrepancies between the results of surface-based and other binding assays (72⇓–74), and the ability of the fluorescence polarization assay to accurately measure the affinity of fluorescent HA epitope (SI Appendix, Fig. S79), we evaluated the remaining MIMs using the competition assay (Table 4 and SI Appendix, section 15.3). Affinities ranged from 460 nM to 6.1 μM; binders containing aromatic residues in the * positions of the sequence motif yp*e*d displayed the highest affinity.

View this table:
  • View inline
  • View popup
Table 4.

Affinity of mirror-image EETI-II–based binders (compounds 1–7) to 12CA5

‟Hot-Spot” Residues Determine Binding Affinity of MIMs for 12CA5.

Identification of a sequence motif associated with 12CA5 binding implies that the identified residues are “hot spots,” since they are conserved among MIM-based binders. The interactions of l-peptide HA epitope Tyr98-Ala108 (YPYDVPDYALA) with anti-HA mAbs are also facilitated by a limited number of hot-spot residues: Asp101, Asp104, and Tyr105 (75⇓–77). The d-polypeptide yp*e*d binding motif bears a striking resemblance to the l-peptide epitope, in terms of both sequence similarity (yp*e vs. YP*D) and overall side-chain composition. However, the hot-spot residues are different (yp*e*d vs. D**DY). In crystal structures of anti-HA mAbs complexed with HA peptide Tyr100-Ala108, the side chain of Tyr105 is sandwiched between side chains of the mAb H3 loop (78, 79). Energetically, this is the most important interaction in the mAb–peptide complex. For the MIM-based binders, we speculate that the conserved d-tyrosine residue fulfills a similar interaction, and that the d-polypeptide traverses the mAb binding “cleft” formed by loops H2 and H3 in opposite direction of the l-peptide epitope.

To investigate a possible role of constraint in facilitating the binding interaction between 12CA5 and the MIM-based binders, select MIMs were exhaustively reduced/alkylated for evaluation in the fluorescence polarization (FP) assay (compounds 1 and 5; SI Appendix, section 16.1). When assayed side by side with the corresponding MIMs containing intact disulfide bonds, these “unfolded” variants exhibited approximately twofold weaker binding affinity (SI Appendix, section 16.2). The minimal role of constraint in stabilizing the anti-HA/engineered mirror-image EETI-II interface is in contrast to interactions between engineered cyclic peptide binders and targets such as integrin (80) or SA (60), and for the interaction of an engineered thioredoxin loop with an anti-hapten mAb (81), where constraint significantly improves binding affinity. Constraint may be comparatively unimportant in facilitating binding to mAbs that target linear epitopes, as for the mAb 12CA5 studied here (82). Potentially, the β-Ala residues play a role in offsetting a potential benefit of constraint. Further studies on a wide range of protein targets will be required to evaluate the general utility of constraint for increasing the binding activity of engineered knottins.

Significance.

The methods described here form the basis of a platform for the discovery of xenoprotein molecules with de novo binding activity. As illustrated by the identification of mirror-image EETI-II-based “mimotopes” (83), discovery of MIM-based binding molecules is one application of this strategy. Mirror-image peptide and protein-based binding molecules are of longstanding interest in biotechnology (84). However, to date their discovery by mirror-image phage or cell-surface display has required chemical synthesis of the pertinent target molecule (43, 85, 86), which has restricted the practical size limit of proteins that could realistically be targeted. Direct screening of synthetic MIM libraries is a potential solution to this limitation. Further work will be required to evaluate the general utility of engineered knottins for this application, and of other small protein scaffolds based on helical bundles (87), β-sheets (88), or de novo designs (89⇓–91).

For de novo protein engineering, higher-diversity libraries yield generally superior outcomes. For example, the affinities of antibodies identified by selection from nonimmune libraries improve markedly with increasing library size (66, 92). Thus, extending the throughput by which synthetic libraries can be screened is expected to considerably extend their value. In this work, throughput was achieved by interfacing a multistage selection and screening procedure with a high-throughput LC-MS/MS–based sequencing approach, applicable to large numbers of small beads (32). Both of these steps were necessary to practically explore the utility of synthetic protein libraries for de novo engineering and should facilitate progress in combinatorial chemistry generally. Our work establishes significant parallels between flow cytometry analysis of bead-based (49) versus cell-surface display libraries (46, 47, 93) and highlights the importance of multistage, high-yield selection procedures in the case of beads, since synthetic libraries cannot be propagated and resorted to improve enrichment.

Other uses of synthetic protein libraries may be found. For example, screens for retention of binding could be used to identify positions of an engineered binding protein—identified through a molecular biology-based method—that are compatible with noncanonical amino acids. Such an approach could be useful for modifying the protease stability of biotherapeutics, which is currently achieved through rational design (94). In the future, synthetic libraries may be used to empirically evaluate the success of de novo xenoprotein designs, as has recently been achieved with yeast surface display and de novo designs based on proteogenic amino acids (91, 95). The combination of design and synthetic library screening could prove extremely powerful, with experimental feedback informing design strategy improvements.

Conclusion

The potential of synthetic protein libraries for identifying functional xenoproteins has been demonstrated. By using a combination of magnetic bead enrichment and flow cytometry, the screening of at least 2 × 107 synthetic protein variants was rendered feasible on the timescale of hours. This is a significant improvement over state-of-the-art bead-based screening methods, which examine ∼100-fold fewer compounds—of smaller molecular weight (∼1 kDa)—in a typical screen (51, 96). Throughput could be further improved by the use of even smaller beads, which in principle contain sufficient material for MS/MS-based sequencing (32). With continued development, and as synthetic methods continue to evolve, synthetic protein libraries may become a powerful tool for the discovery of folded, nonnatural polymers with biological function.

Methods

Peptide Synthesis.

Thirty-micrometer TentaGel M NH2 microspheres (M30352) (97) (Rapp Polymere GmbH) and Fmoc chemistry SPPS were employed throughout. For library synthesis, a protocol for Boc-chemistry SPPS (98) was adapted for use with manual Fmoc SPPS. Fmoc deprotection was achieved by treatment with 20% piperidine in dimethylformamide (30 s flow wash; 2 × 3-min batch wash). For the synthesis of defined peptidyl resins, a manual flow-based method for Fmoc SPPS (99) was employed. Methods for the preparation of spatially segregated and attenuated loading resin beads are described in SI Appendix.

Flow Cytometry.

All studies employed a FACS Aria III cytometer (BD Biosciences) equipped with 488-, 561-, and 633-nm lasers, and a 130-μm nozzle (operated at 10 psi sheath pressure). All experiments using SA-APC–based stain reagents employed 633-nm excitation and 660-nm (20-nm bandwidth) detection. Fluorescence measurements were recorded based on a forward scatter threshold. Detector voltages and laser delays were set using a sample of biotin-functionalized TentaGel that had been stained with SA-APC (the fluorescence mean of this sample was set to ∼100,000 counts—the middle of the dynamic range). Before analysis, all bead samples were passed through a 70-μm cell strainer to minimize inlet tube clogs.

Acknowledgments

We thank Anne Fischer and D. Tyler McQuade (Defense Advanced Research Projects Agency, DARPA) for their support and guidance and Jeremiah Johnson, Stephen Kent, John Lampe, Thomas Nielsen, Glenn Paradis, Amy Rabideau, Michael Santos, K. Dane Wittrup, and Chi Zhang for encouragement and insightful discussions. This work was supported by DARPA Award 023504-001 (to B.L.P. and T.F.J.) and a STAR Postdoctoral fellowship from Novo Nordisk (to A.B. and Z.P.G.). We acknowledge use of the Biophysical Instrumentation Facility at Massachusetts Institute of Technology (MIT) (NIH S10 OD016326; Deborah Pheasant, Director) and the Swanson Biotechnology Center High Throughput Screening Facility at MIT’s Koch Institute (Jaime Cheah, Core Leader).

Footnotes

  • ↵1To whom correspondence may be addressed. Email: zgates{at}mit.edu or blp{at}mit.edu.
  • Author contributions: Z.P.G., A.A.V., A.J.Q., A.B., E.D.E., M.D.S., T.F.J., and B.L.P. designed research; Z.P.G., A.A.V., A.J.Q., A.B., Z.-N.C., E.D.E., K.H.H., A.J.M., S.K.M., M.D.S., E.A.S., E.D.S., S.Z.T., F.T., J.M.W., J.L.W., and B.L.P. performed research; Z.P.G., A.A.V., A.J.Q., A.B., and B.L.P. analyzed data; and Z.P.G., A.A.V., and B.L.P. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1722633115/-/DCSupplemental.

Published under the PNAS license.

References

  1. ↵
    1. Wang L,
    2. Schultz PG
    (2004) Expanding the genetic code. Angew Chem Int Ed Engl 44:34–66.
    OpenUrlCrossRefPubMed
  2. ↵
    1. Jackson JC,
    2. Duffy SP,
    3. Hess KR,
    4. Mehl RA
    (2006) Improving nature’s enzyme active site with genetically encoded unnatural amino acids. J Am Chem Soc 128:11124–11127.
    OpenUrlPubMed
  3. ↵
    1. Ugwumba IN, et al.
    (2011) Improving a natural enzyme activity through incorporation of unnatural amino acids. J Am Chem Soc 133:326–333.
    OpenUrlPubMed
  4. ↵
    1. Liu CC, et al.
    (2008) Protein evolution with an expanded genetic code. Proc Natl Acad Sci USA 105:17688–17693.
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Liu CC, et al.
    (2009) Evolution of proteins with genetically encoded “chemical warheads” J Am Chem Soc 131:9616–9617.
    OpenUrlCrossRefPubMed
  6. ↵
    1. Young TS, et al.
    (2011) Evolution of cyclic peptide protease inhibitors. Proc Natl Acad Sci USA 108:11052–11056.
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Hammerling MJ, et al.
    (2014) Bacteriophages use an expanded genetic code on evolutionary paths to higher fitness. Nat Chem Biol 10:178–180.
    OpenUrlCrossRefPubMed
  8. ↵
    1. Kang M, et al.
    (2014) Evolution of iron(II)-finger peptides by using a bipyridyl amino acid. ChemBioChem 15:822–825.
    OpenUrlCrossRefPubMed
  9. ↵
    1. Xiao H, et al.
    (2015) Exploring the potential impact of an expanded genetic code on protein function. Proc Natl Acad Sci USA 112:6961–6966.
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Uppalapati M, et al.
    (2016) A potent D-protein antagonist of VEGF-A is nonimmunogenic, metabolically stable, and longer-circulating in Vivo. ACS Chem Biol 11:1058–1065.
    OpenUrl
  11. ↵
    1. Baca M,
    2. Kent SBH
    (1993) Catalytic contribution of flap-substrate hydrogen bonds in “HIV-1 protease” explored by chemical synthesis. Proc Natl Acad Sci USA 90:11638–11642.
    OpenUrlAbstract/FREE Full Text
  12. ↵
    1. Smith R,
    2. Brereton IM,
    3. Chai RY,
    4. Kent SBH
    (1996) Ionization states of the catalytic residues in HIV-1 protease. Nat Struct Biol 3:946–950.
    OpenUrlCrossRefPubMed
  13. ↵
    1. Romanelli A,
    2. Shekhtman A,
    3. Cowburn D,
    4. Muir TW
    (2004) Semisynthesis of a segmental isotopically labeled protein splicing precursor: NMR evidence for an unusual peptide bond at the N-extein-intein junction. Proc Natl Acad Sci USA 101:6397–6402.
    OpenUrlAbstract/FREE Full Text
  14. ↵
    1. Wu Z, et al.
    (2004) Total chemical synthesis of N-myristoylated HIV-1 matrix protein p17: Structural and mechanistic implications of p17 myristoylation. Proc Natl Acad Sci USA 101:11587–11592.
    OpenUrlAbstract/FREE Full Text
  15. ↵
    1. Shogren-Knaak M, et al.
    (2006) Histone H4-K16 acetylation controls chromatin structure and protein interactions. Science 311:844–847.
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Valiyaveetil FI,
    2. Leonetti M,
    3. Muir TW,
    4. Mackinnon R
    (2006) Ion selectivity in a semisynthetic K+ channel locked in the conductive conformation. Science 314:1004–1007.
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Torbeev VY, et al.
    (2011) Protein conformational dynamics in the mechanism of HIV-1 protease catalysis. Proc Natl Acad Sci USA 108:20982–20987.
    OpenUrlAbstract/FREE Full Text
  18. ↵
    1. Liu Z, et al.
    (2014) Structure of the branched intermediate in protein splicing. Proc Natl Acad Sci USA 111:8422–8427.
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Murakami M, et al.
    (2016) Chemical synthesis of erythropoietin glycoforms for insights into the relationship between glycosylation pattern and bioactivity. Sci Adv 2:e1500678.
    OpenUrlFREE Full Text
  20. ↵
    1. Thompson RE, et al.
    (2017) Tyrosine sulfation modulates activity of tick-derived thrombin inhibitors. Nat Chem 9:909–917.
    OpenUrl
  21. ↵
    1. Montclare JK,
    2. Tirrell DA
    (2006) Evolving proteins of novel composition. Angew Chem Int Ed Engl 45:4518–4521.
    OpenUrlPubMed
  22. ↵
    1. Reinert ZE,
    2. Lengyel GA,
    3. Horne WS
    (2013) Protein-like tertiary folding behavior from heterogeneous backbones. J Am Chem Soc 135:12528–12531.
    OpenUrl
  23. ↵
    1. Simon MD, et al.
    (2016) D-Amino acid scan of two small proteins. J Am Chem Soc 138:12099–12111.
    OpenUrl
  24. ↵
    1. DeGrado WF,
    2. Sosnick TR
    (1996) Protein minimization: Downsizing through mutation. Proc Natl Acad Sci USA 93:5680–5681.
    OpenUrlFREE Full Text
  25. ↵
    1. Bianchi E, et al.
    (1995) A conformationally homogeneous combinatorial peptide library. J Mol Biol 247:154–160.
    OpenUrlCrossRefPubMed
  26. ↵
    1. Epton R
    1. Lowe G,
    2. Quarrell R
    (1994) Targeted libraries of CMTI-I mutants and the identification of potential serine proteinase inhibitors. Innovations and Perspectives in Solid Phase Synthesis, ed Epton R (Mayflower Worldwide Limited, Birmingham, UK), pp 377–380.
  27. ↵
    1. Packer MS,
    2. Liu DR
    (2015) Methods for the directed evolution of proteins. Nat Rev Genet 16:379–394.
    OpenUrlCrossRefPubMed
  28. ↵
    1. Lam KS, et al.
    (1991) A new type of synthetic peptide library for identifying ligand-binding activity. Nature 354:82–84.
    OpenUrlCrossRefPubMed
  29. ↵
    1. Wu X,
    2. Upadhyaya P,
    3. Villalona-Calero MA,
    4. Briesewitz R,
    5. Pei D
    (2013) Inhibition of Ras–effector interactions by cyclic peptides. MedChemComm 4:378–382.
    OpenUrl
  30. ↵
    1. Obexer R,
    2. Walport LJ,
    3. Suga H
    (2017) Exploring sequence space: Harnessing chemical and biological diversity towards new peptide leads. Curr Opin Chem Biol 38:52–61.
    OpenUrlCrossRefPubMed
  31. ↵
    1. Das S, et al.
    (2015) A general synthetic approach for designing epitope targeted macrocyclic peptide ligands. Angew Chem Int Ed Engl 54:13219–13224.
    OpenUrl
  32. ↵
    1. Vinogradov AA, et al.
    (2017) Library design-facilitated high-throughput sequencing of synthetic peptide libraries. ACS Comb Sci 19:694–701.
    OpenUrl
  33. ↵
    1. Binz HK,
    2. Amstutz P,
    3. Plückthun A
    (2005) Engineering novel binding proteins from nonimmunoglobulin domains. Nat Biotechnol 23:1257–1268.
    OpenUrlCrossRefPubMed
  34. ↵
    1. Le-Nguyen D,
    2. Nalis D,
    3. Castro B
    (1989) Solid phase synthesis of a trypsin inhibitor isolated from the Cucurbitaceae Ecballium elaterium. Int J Pept Protein Res 34:492–497.
    OpenUrlPubMed
  35. ↵
    1. Le Nguyen D, et al.
    (1990) Molecular recognition between serine proteases and new bioactive microproteins with a knotted structure. Biochimie 72:431–435.
    OpenUrlCrossRefPubMed
  36. ↵
    1. Christmann A,
    2. Walter K,
    3. Wentzel A,
    4. Krätzner R,
    5. Kolmar H
    (1999) The cystine knot of a squash-type protease inhibitor as a structural scaffold for Escherichia coli cell surface display of conformationally constrained peptides. Protein Eng 12:797–806.
    OpenUrlCrossRefPubMed
  37. ↵
    1. Mong SK, et al.
    (2017) Heterochiral knottin protein: Folding and solution structure. Biochemistry 56:5720–5725.
    OpenUrl
  38. ↵
    1. Lahti JL,
    2. Silverman AP,
    3. Cochran JR
    (2009) Interrogating and predicting tolerated sequence diversity in protein folds: Application to E. Elaterium trypsin inhibitor-II cystine-knot miniprotein. PLOS Comput Biol 5:e1000499.
    OpenUrlCrossRefPubMed
  39. ↵
    1. Kimura RH,
    2. Levin AM,
    3. Cochran FV,
    4. Cochran JR
    (2009) Engineered cystine knot peptides that bind alphavbeta3, alphavbeta5, and α5β1 integrins with low-nanomolar affinity. Proteins 77:359–369.
    OpenUrlCrossRefPubMed
  40. ↵
    1. Kimura RH,
    2. Cheng Z,
    3. Gambhir SS,
    4. Cochran JR
    (2009) Engineered knottin peptides: A new class of agents for imaging integrin expression in living subjects. Cancer Res 69:2435–2442.
    OpenUrlAbstract/FREE Full Text
  41. ↵
    1. Craik DJ,
    2. Du J
    (2017) Cyclotides as drug design scaffolds. Curr Opin Chem Biol 38:8–16.
    OpenUrlCrossRef
  42. ↵
    1. Kryshtafovych A, et al.
    (2014) Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th critical assessment of techniques for protein structure prediction experiment CASP10. Proteins 82:26–42.
    OpenUrlCrossRefPubMed
  43. ↵
    1. Schumacher TN, et al.
    (1996) Identification of D-peptide ligands through mirror-image phage display. Science 271:1854–1857.
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Garton M, et al.
    (2018) Method to generate highly stable D-amino acid analogs of bioactive helical peptides using a mirror image of the entire PDB. Proc Natl Acad Sci USA 115:1505–1510.
    OpenUrlAbstract/FREE Full Text
  45. ↵
    1. Werle M, et al.
    (2006) The potential of cystine-knot microproteins as novel pharmacophoric scaffolds in oral peptide drug delivery. J Drug Target 14:137–146.
    OpenUrlCrossRefPubMed
  46. ↵
    1. Georgiou G, et al.
    (1997) Display of heterologous proteins on the surface of microorganisms: From the screening of combinatorial libraries to live recombinant vaccines. Nat Biotechnol 15:29–34.
    OpenUrlCrossRefPubMed
  47. ↵
    1. Boder ET,
    2. Wittrup KD
    (1997) Yeast surface display for screening combinatorial polypeptide libraries. Nat Biotechnol 15:553–557.
    OpenUrlCrossRefPubMed
  48. ↵
    1. Chao G, et al.
    (2006) Isolating and engineering human antibodies using yeast surface display. Nat Protoc 1:755–768.
    OpenUrlCrossRefPubMed
  49. ↵
    1. Needels MC, et al.
    (1993) Generation and screening of an oligonucleotide-encoded synthetic peptide library. Proc Natl Acad Sci USA 90:10700–10704.
    OpenUrlAbstract/FREE Full Text
  50. ↵
    1. Müller K, et al.
    (1996) Rapid identification of phosphopeptide ligands for SH2 domains. Screening of peptide libraries by fluorescence-activated bead sorting. J Biol Chem 271:16500–16505.
    OpenUrlAbstract/FREE Full Text
  51. ↵
    1. Mendes KR, et al.
    (2017) High-throughput identification of DNA-encoded IgG ligands that distinguish active and latent Mycobacterium tuberculosis infections. ACS Chem Biol 12:234–243.
    OpenUrl
  52. ↵
    1. Schmidt TGM,
    2. Koepke J,
    3. Frank R,
    4. Skerra A
    (1996) Molecular interaction between the strep-tag affinity peptide and its cognate target, streptavidin. J Mol Biol 255:753–766.
    OpenUrlCrossRefPubMed
  53. ↵
    1. Jee J-E, et al.
    (2014) Investigating fluorescent dyes in fluorescence-assisted screenings. Chem Commun (Camb) 50:15220–15223.
    OpenUrl
  54. ↵
    1. DeLano WL,
    2. Ultsch MH,
    3. de Vos AM,
    4. Wells JA
    (2000) Convergent solutions to binding at a protein-protein interface. Science 287:1279–1283.
    OpenUrlAbstract/FREE Full Text
  55. ↵
    1. VanAntwerp JJ,
    2. Wittrup KD
    (2000) Fine affinity discrimination by yeast surface display and flow cytometry. Biotechnol Prog 16:31–37.
    OpenUrlCrossRefPubMed
  56. ↵
    1. Doran TM, et al.
    (2014) Utility of redundant combinatorial libraries in distinguishing high and low quality screening hits. ACS Comb Sci 16:259–270.
    OpenUrlCrossRefPubMed
  57. ↵
    1. Hintersteiner M,
    2. Buehler C,
    3. Auer M
    (2012) On-bead screens sample narrower affinity ranges of protein-ligand interactions compared to equivalent solution assays. ChemPhysChem 13:3472–3480.
    OpenUrlPubMed
  58. ↵
    1. Weber PC,
    2. Pantoliano MW,
    3. Thompson LD
    (1992) Crystal structure and ligand-binding studies of a screened peptide complexed with streptavidin. Biochemistry 31:9350–9354.
    OpenUrlCrossRefPubMed
  59. ↵
    1. Wang X,
    2. Peng L,
    3. Liu R,
    4. Xu B,
    5. Lam KS
    (2005) Applications of topologically segregated bilayer beads in ‘one-bead one-compound’ combinatorial libraries. J Pept Res 65:130–138.
    OpenUrlCrossRefPubMed
  60. ↵
    1. Giebel LB, et al.
    (1995) Screening of cyclic peptide phage libraries identifies ligands that bind streptavidin with high affinities. Biochemistry 34:15430–15435.
    OpenUrlCrossRefPubMed
  61. ↵
    1. Munson MC,
    2. Barany G
    (1993) Synthesis of α-Conotoxin SI, a bicyclic tridecapeptide amide with two disulfide bridges: Illustration of novel protection schemes and oxidation strategies. J Am Chem Soc 115:10203–10210.
    OpenUrl
  62. ↵
    1. Wentzel A,
    2. Christmann A,
    3. Krätzner R,
    4. Kolmar H
    (1999) Sequence requirements of the GPNG β-turn of the Ecballium elaterium trypsin inhibitor II explored by combinatorial library screening. J Biol Chem 274:21037–21043.
    OpenUrlAbstract/FREE Full Text
  63. ↵
    1. Alexander M,
    2. Kent SBH,
    3. Engelhard M,
    4. Merrifield RB
    (1978) A new synthetic route to tert-butyloxycarbonylaminoacyl-4-(oxymethyl)phenylacetamidomethyl-resin, an improved support for solid-phase peptide synthesis. J Org Chem 43:2845–2852.
    OpenUrlCrossRef
  64. ↵
    1. Johnson ECB,
    2. Durek T,
    3. Kent SBH
    (2006) Total chemical synthesis, folding, and assay of a small protein on a water-compatible solid support. Angew Chem Int Ed Engl 45:3283–3287.
    OpenUrlPubMed
  65. ↵
    1. Vágner J, et al.
    (1996) Enzyme-mediated spatial segregation on individual polymeric support beads: Application to generation and screening of encoded combinatorial libraries. Proc Natl Acad Sci USA 93:8194–8199.
    OpenUrlAbstract/FREE Full Text
  66. ↵
    1. Feldhaus MJ, et al.
    (2003) Flow-cytometric isolation of human antibodies from a nonimmune Saccharomyces cerevisiae surface display library. Nat Biotechnol 21:163–170.
    OpenUrlCrossRefPubMed
  67. ↵
    1. Townsend J, et al.
    (2010) 3-nitro-tyrosine as an internal quencher of autofluorescence enhances the compatibility of fluorescence based screening of OBOC combinatorial libraries. Comb Chem High Throughput Screen 13:422–429.
    OpenUrlPubMed
  68. ↵
    1. Yeung YA,
    2. Wittrup KD
    (2002) Quantitative screening of yeast surface-displayed polypeptide libraries by magnetic bead capture. Biotechnol Prog 18:212–220.
    OpenUrlCrossRefPubMed
  69. ↵
    1. Ackerman M, et al.
    (2009) Highly avid magnetic bead capture: An efficient selection method for de novo protein engineering utilizing yeast surface display. Biotechnol Prog 25:774–783.
    OpenUrlCrossRefPubMed
  70. ↵
    1. Mendes K,
    2. Ndungu JM,
    3. Clark LF,
    4. Kodadek T
    (2015) Optimization of the magnetic recovery of hits from one-bead one-compound library screens. ACS Comb Sci 17:506–517.
    OpenUrlCrossRef
  71. ↵
    1. Schuck P,
    2. Minton AP
    (1996) Kinetic analysis of biosensor data: Elementary tests for self-consistency. Trends Biochem Sci 21:458–460.
    OpenUrlCrossRefPubMed
  72. ↵
    1. Bondeson K,
    2. Frostell-Karlsson A,
    3. Fägerstam L,
    4. Magnusson G
    (1993) Lactose repressor-operator DNA interactions: Kinetic analysis by a surface plasmon resonance biosensor. Anal Biochem 214:245–251.
    OpenUrlCrossRefPubMed
  73. ↵
    1. Kelley RF,
    2. O’Connell MP
    (1993) Thermodynamic analysis of an antibody functional epitope. Biochemistry 32:6828–6835.
    OpenUrlCrossRefPubMed
  74. ↵
    1. Schuck P
    (1997) Use of surface plasmon resonance to probe the equilibrium and dynamic aspects of interactions between biological macromolecules. Annu Rev Biophys Biomol Struct 26:541–566.
    OpenUrlCrossRefPubMed
  75. ↵
    1. Houghten RA
    (1985) General method for the rapid solid-phase synthesis of large numbers of peptides: Specificity of antigen-antibody interaction at the level of individual amino acids. Proc Natl Acad Sci USA 82:5131–5135.
    OpenUrlAbstract/FREE Full Text
  76. ↵
    1. Pinilla C,
    2. Appel JR,
    3. McPherson SE,
    4. Houghten RA
    (1992) Comparison of structural and functional approaches for the study of peptide-mAb interactions. Peptides: Chemistry and Biology, eds Smith JA, Rivier JE (ESCOM, Leiden, The Netherlands), pp 867–868.
  77. ↵
    1. Churchill MEA, et al.
    (1994) Crystal structure of a peptide complex of anti-influenza peptide antibody Fab 26/9. Comparison of two different antibodies bound to the same peptide antigen. J Mol Biol 241:534–556.
    OpenUrlCrossRefPubMed
  78. ↵
    1. Rini JM,
    2. Schulze-Gahmen U,
    3. Wilson IA
    (1992) Structural evidence for induced fit as a mechanism for antibody-antigen recognition. Science 255:959–965.
    OpenUrlAbstract/FREE Full Text
  79. ↵
    1. Schulze-Gahmen U,
    2. Rini JM,
    3. Wilson IA
    (1993) Detailed analysis of the free and bound conformations of an antibody. X-ray structures of Fab 17/9 and three different Fab-peptide complexes. J Mol Biol 234:1098–1118.
    OpenUrlCrossRefPubMed
  80. ↵
    1. O’Neil KT, et al.
    (1992) Identification of novel peptide antagonists for GPIIb/IIIa from a conformationally constrained phage peptide library. Proteins 14:509–515.
    OpenUrlCrossRefPubMed
  81. ↵
    1. James LC,
    2. Roversi P,
    3. Tawfik DS
    (2003) Antibody multispecificity mediated by conformational diversity. Science 299:1362–1367.
    OpenUrlAbstract/FREE Full Text
  82. ↵
    1. Souriau C,
    2. Chiche L,
    3. Irving R,
    4. Hudson P
    (2005) New binding specificities derived from Min-23, a small cystine-stabilized peptidic scaffold. Biochemistry 44:7143–7155.
    OpenUrlCrossRefPubMed
  83. ↵
    1. Geysen HM,
    2. Rodda SJ,
    3. Mason TJ
    (1986) A priori delineation of a peptide which mimics a discontinuous antigenic determinant. Mol Immunol 23:709–715.
    OpenUrlCrossRefPubMed
  84. ↵
    1. Robson B
    (1996) Doppelgänger proteins as drug leads. Nat Biotechnol 14:892–893.
    OpenUrlCrossRefPubMed
  85. ↵
    1. Mandal K, et al.
    (2012) Chemical synthesis and X-ray structure of a heterochiral D-protein antagonist plus vascular endothelial growth factor protein complex by racemic crystallography. Proc Natl Acad Sci USA 109:14779–14784.
    OpenUrlAbstract/FREE Full Text
  86. ↵
    1. Levinson AM, et al.
    (2017) Total chemical synthesis and folding of all-L and all-D variants of oncogenic KRas(G12V). J Am Chem Soc 139:7632–7639.
    OpenUrlCrossRefPubMed
  87. ↵
    1. Braisted AC,
    2. Wells JA
    (1996) Minimizing a binding domain from protein A. Proc Natl Acad Sci USA 93:5688–5692.
    OpenUrlAbstract/FREE Full Text
  88. ↵
    1. Patel S,
    2. Mathonet P,
    3. Jaulent AM,
    4. Ullman CG
    (2013) Selection of a high-affinity WW domain against the extracellular region of VEGF receptor isoform-2 from a combinatorial library using CIS display. Protein Eng Des Sel 26:307–315.
    OpenUrlCrossRefPubMed
  89. ↵
    1. Bhardwaj G, et al.
    (2016) Accurate de novo design of hyperstable constrained peptides. Nature 538:329–335.
    OpenUrlCrossRefPubMed
  90. ↵
    1. Baker EG,
    2. Bartlett GJ,
    3. Porter Goff KL,
    4. Woolfson DN
    (2017) Miniprotein design: Past, present, and prospects. Acc Chem Res 50:2085–2092.
    OpenUrl
  91. ↵
    1. Chevalier A, et al.
    (2017) Massively parallel de novo protein design for targeted therapeutics. Nature 550:74–79.
    OpenUrlCrossRef
  92. ↵
    1. Griffiths AD,
    2. Duncan AR
    (1998) Strategies for selection of antibodies by phage display. Curr Opin Biotechnol 9:102–108.
    OpenUrlCrossRefPubMed
  93. ↵
    1. Francisco JA,
    2. Campbell R,
    3. Iverson BL,
    4. Georgiou G
    (1993) Production and fluorescence-activated cell sorting of Escherichia coli expressing a functional antibody fragment on the external surface. Proc Natl Acad Sci USA 90:10444–10448.
    OpenUrlAbstract/FREE Full Text
  94. ↵
    1. Checco JW, et al.
    (2015) Targeting diverse protein-protein interaction interfaces with α/β-peptides derived from the Z-domain scaffold. Proc Natl Acad Sci USA 112:4552–4557.
    OpenUrlAbstract/FREE Full Text
  95. ↵
    1. Rocklin GJ, et al.
    (2017) Global analysis of protein folding using massively parallel design, synthesis, and testing. Science 357:168–175.
    OpenUrlAbstract/FREE Full Text
  96. ↵
    1. Lian W,
    2. Upadhyaya P,
    3. Rhodes CA,
    4. Liu Y,
    5. Pei D
    (2013) Screening bicyclic peptide libraries for protein-protein interaction inhibitors: Discovery of a tumor necrosis factor-α antagonist. J Am Chem Soc 135:11990–11995.
    OpenUrlCrossRefPubMed
  97. ↵
    1. Bayer E
    (1991) Towards the chemical synthesis of proteins. Angew Chem Int Ed Engl 30:113–129.
    OpenUrlCrossRef
  98. ↵
    1. Schnölzer M,
    2. Alewood P,
    3. Jones A,
    4. Alewood D,
    5. Kent SBH
    (1992) In situ neutralization in Boc-chemistry solid phase peptide synthesis. Rapid, high yield assembly of difficult sequences. Int J Pept Protein Res 40:180–193.
    OpenUrlCrossRefPubMed
  99. ↵
    1. Simon MD, et al.
    (2014) Rapid flow-based peptide synthesis. ChemBioChem 15:713–720.
    OpenUrlCrossRefPubMed
PreviousNext
Back to top
Article Alerts
Email Article

Thank you for your interest in spreading the word on PNAS.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Xenoprotein engineering via synthetic libraries
(Your Name) has sent you a message from PNAS
(Your Name) thought you would like to see the PNAS web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Xenoprotein engineering via synthetic libraries
Zachary P. Gates, Alexander A. Vinogradov, Anthony J. Quartararo, Anupam Bandyopadhyay, Zi-Ning Choo, Ethan D. Evans, Kathryn H. Halloran, Alexander J. Mijalis, Surin K. Mong, Mark D. Simon, Eric A. Standley, Evan D. Styduhar, Sarah Z. Tasker, Faycal Touti, Jessica M. Weber, Jessica L. Wilson, Timothy F. Jamison, Bradley L. Pentelute
Proceedings of the National Academy of Sciences Jun 2018, 115 (23) E5298-E5306; DOI: 10.1073/pnas.1722633115

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Request Permissions
Share
Xenoprotein engineering via synthetic libraries
Zachary P. Gates, Alexander A. Vinogradov, Anthony J. Quartararo, Anupam Bandyopadhyay, Zi-Ning Choo, Ethan D. Evans, Kathryn H. Halloran, Alexander J. Mijalis, Surin K. Mong, Mark D. Simon, Eric A. Standley, Evan D. Styduhar, Sarah Z. Tasker, Faycal Touti, Jessica M. Weber, Jessica L. Wilson, Timothy F. Jamison, Bradley L. Pentelute
Proceedings of the National Academy of Sciences Jun 2018, 115 (23) E5298-E5306; DOI: 10.1073/pnas.1722633115
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Mendeley logo Mendeley

Article Classifications

  • Biological Sciences
  • Biophysics and Computational Biology
  • Physical Sciences
  • Chemistry
Proceedings of the National Academy of Sciences: 115 (23)
Table of Contents

Submit

Sign up for Article Alerts

Jump to section

  • Article
    • Abstract
    • Results and Discussion
    • Conclusion
    • Methods
    • Acknowledgments
    • Footnotes
    • References
  • Figures & SI
  • Info & Metrics
  • PDF

You May Also be Interested in

Setting sun over a sun-baked dirt landscape
Core Concept: Popular integrated assessment climate policy models have key caveats
Better explicating the strengths and shortcomings of these models will help refine projections and improve transparency in the years ahead.
Image credit: Witsawat.S.
Model of the Amazon forest
News Feature: A sea in the Amazon
Did the Caribbean sweep into the western Amazon millions of years ago, shaping the region’s rich biodiversity?
Image credit: Tacio Cordeiro Bicudo (University of São Paulo, São Paulo, Brazil), Victor Sacek (University of São Paulo, São Paulo, Brazil), and Lucy Reading-Ikkanda (artist).
Syrian archaeological site
Journal Club: In Mesopotamia, early cities may have faltered before climate-driven collapse
Settlements 4,200 years ago may have suffered from overpopulation before drought and lower temperatures ultimately made them unsustainable.
Image credit: Andrea Ricci.
Steamboat Geyser eruption.
Eruption of Steamboat Geyser
Mara Reed and Michael Manga explore why Yellowstone's Steamboat Geyser resumed erupting in 2018.
Listen
Past PodcastsSubscribe
Birds nestling on tree branches
Parent–offspring conflict in songbird fledging
Some songbird parents might improve their own fitness by manipulating their offspring into leaving the nest early, at the cost of fledgling survival, a study finds.
Image credit: Gil Eckrich (photographer).

Similar Articles

Site Logo
Powered by HighWire
  • Submit Manuscript
  • Twitter
  • Facebook
  • RSS Feeds
  • Email Alerts

Articles

  • Current Issue
  • Special Feature Articles – Most Recent
  • List of Issues

PNAS Portals

  • Anthropology
  • Chemistry
  • Classics
  • Front Matter
  • Physics
  • Sustainability Science
  • Teaching Resources

Information

  • Authors
  • Editorial Board
  • Reviewers
  • Subscribers
  • Librarians
  • Press
  • Site Map
  • PNAS Updates
  • FAQs
  • Accessibility Statement
  • Rights & Permissions
  • About
  • Contact

Feedback    Privacy/Legal

Copyright © 2021 National Academy of Sciences. Online ISSN 1091-6490