Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Jan 25;11:375-387.
doi: 10.1016/j.isci.2018.11.038. Epub 2018 Dec 4.

In Silico Engineering of Synthetic Binding Proteins From Random Amino Acid Sequences

Affiliations
Free PMC article

In Silico Engineering of Synthetic Binding Proteins From Random Amino Acid Sequences

Daniel Burnside et al. iScience. .
Free PMC article

Abstract

Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available.

Keywords: Bioinformatics; Biological Sciences; Protein Family Determination.

Figures

None
Figure 1
Figure 1
An Overview of the InSiPS Genetic Algorithm (A) An initial pool of random protein sequences 150 aa in length is created. Next, the primary loop is entered: sequences in the pool are evaluated, and subsequent generations are created. This process repeats for a minimum of 250 generations until a high-fitness peptide is produced. (B) PPI prediction. Sequences generated by InSiPS are evaluated using the Protein-Protein Interaction Prediction Engine (PIPE) (Pitre et al., 2008). PIPE requires a validated global PPI network as input. Step 1: protein A is compared with all proteins in the known PPI network. A sliding window is used both on A and the proteins in the PPI network until some segment of A, starting at position i, matches a segment of some protein T in the network. All known interactors of T (neighbors) are put into a list to be used in the next step. Step 2: protein B is compared with the proteins in the neighbor's list in the same manner. When a segment of B, starting at position j, is found to match a segment of a protein from this list, the result matrix is incremented at position (i,j). This matrix represents all the segments in proteins A and B that co-occur in experimentally validated PPIs and is used to predict if A and B interact. The interaction algorithm assigns a predicted interaction score between 0 and 1. Any pair scoring over 0.51 is predicted to interact with a specificity of 99.5%. The fitness of a protein sequence is calculated based on predicted interactions with targets-non-targets. (C) Generating the next generation of candidate sequences. First, a copy, mutate, or crossover operation is randomly chosen with a preset probability proportional to the fitness of a sequence as calculated in (B). This process is repeated until the next generation is complete. The algorithm is terminated after a minimum of 250 generations when the fitness score does not improve over 50 consecutive generations.
Figure 2
Figure 2
Strains Expressing Anti-Psk1 and Anti-Pin4 Can Phenocopy Deletion Mutants of the Target Proteins and Alter Target Protein Expression or Assembly (A and B) Viability of cells under strain-specific stress condition shows that anti-Psk1 and anti-Pin4 expression can produce phenotypes that resemble loss of function mutants of the target proteins. (A and B) Average normalized colony-forming unit (CFU) counts from four trials are displayed as mean ± SD. Stress conditions in trial were (A) exposure to UV light for 30 s for the anti-Psk1 trial and (B) exposure to cycloheximide (65 ng/mL) for the Pin4 trial. (C and D) Expression of anti-target SBPs produces growth defects under strain-specific stress conditions that resemble deletion of the target. Three replicates of each culture or condition were grown for 12 h in liquid YPG (Yeast extract/Peptone/Galactose) + drug, media and OD600 was measured hourly. This experiment was repeated three times. Error bars represent SD among replicates, and a polynomial line of best fit is presented. (C) Δpsk1 sensitivity to H2O2 resembles the phenotype of strains expressing anti-Psk1. Cells were grown in media containing 0.75 mM H2O2. (D) Δpin4 sensitivity to 13 μM hygromycin resembles the phenotype of strains expressing anti-Pin4. (E and F) Observed alteration of fluorescence profile of GFP-tagged targets when anti-target proteins are expressed. Aliquots of WT + anti-target protein cells from the same culture were used to inoculate complete media with either 4% galactose (where anti-target SBP is expressed) or 4% glucose (where anti-target SBP is repressed), and overall fluorescent signal from three independent cultures for each condition were measured over time and normalized to the growth rate. See also Figure S2 showing that anti-Pin4 can cause sensitivity to arsenite.
Figure 3
Figure 3
Yeast Two-Hybrid Assay Indicates Physical Interactions between Target and Anti-target Proteins (A and B) (A) Positive Y2H results using uracil reporter assay. A growth curve in minimal media lacking uracil shows that Pin4/anti-Pin4 and Psk1/anti-Psk1 bait-prey combinations grow better than the negative control strain, indicating a PPI between bait and prey proteins through expression of the URA3 reporter. Triplicate trials produced similar positive results, but the results from a single trial are shown. (B) Positive Y2H result for Pin4/anti-Pin4 and Psk1/anti-Psk1 bait-prey combinations based on resistance to 3-aminotriazole (3-AT). Normalized colony-forming unit (CFU) counts for triplicate trials on minimal media lacking histidine +25 mM 3-AT resistance in test bait-prey combinations and in the positive control are presented as mean ± SD. (C) Positive Y2H result for Pin4/anti-Pin4 and Psk1/anti-Psk1 bait-prey combinations using a β-galactosidase reporter. Miller units are used to quantify β-galactosidase activity by measuring the hydrolysis of ortho-Nitrophenyl-β-galactoside (ONPG) spectrophotometrically. Relative β-gal activity (fold change) from triplicate trials is shown relative to negative control ± SD. The Psk1/anti-Psk1 interaction produced a stronger signal than Pin4/anti-Pin4.
Figure 4
Figure 4
Walking Peptide SPOT Arrays Indicate Specific Binding Regions SPOT arrays containing 18-aa-long printed peptides corresponding to subsequences from within the predicted interaction regions of target protein at single amino acids intervals. (A and B) Predicted interaction matrices highlight the predicted interaction regions between target (x axis) and anti-target (y axis). (A) The anti-Psk1/Psk1 interaction site was predicted to occur between residues 1209–1246 of the PSK1 protein. (B) The Pin4/anti-Pin4 interaction site was predicted to occur between residues 472–506 on Pin4. (C and D) SPOT arrays of predicted target binding sites and flanking regions probed with 6xHis-tagged anti-target proteins followed by detection using an anti-His antibody. (C) Specific binding of the anti-Psk1 protein to the target was detected between amino acids 1204–1228 (D) Specific binding of the anti-Pin4 protein to the target was detected between amino acids 436–458. (E and F) Relative binding of SPOT array peptides indicates highly specific binding regions with highest relative binding. See also Figure S3 for analysis of the anti-Psk1 predicted interaction site.
Figure 5
Figure 5
Characterization of Psk1 Interaction Motif and Binding Affinity (A) Permutation array based on relative binding of the anti-Psk1 binding peptide to the Psk1 interaction motif (1207–1224). Peptides that correspond to the WT Psk1 sequence are outlined in white. Spot coloring is based on the relative binding intensity for each permutated position. (B) Binding curve of anti-Psk1 with the Psk1-binding motif. Shown in the diagram are equilibrium isotherms for the Psk1(1207–1224) peptide from fluorescent polarization. (C) Anti-Psk1 recognition motif based on a position-specific scoring matrix created from the permutation array. (D) Sequence homology between the anti-Psk1-binding site of Psk1 and closest homolog Ubi4. Histidine residues at positions 14 and 16 of the anti-Psk1-binding motif are not conserved in the Ubi4 protein sequence.

Similar articles

See all similar articles

References

    1. Benjamin Stranges P., Kuhlman B. A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds. Protein Sci. 2013;22:74–82. - PMC - PubMed
    1. Biyani M., Kawai K., Kitamura K., Chikae M., Biyani M., Ushijima H., Tamiya E., Yoneda T., Takamura Y. PEP-on-DEP: a competitive peptide-based disposable electrochemical aptasensor for renin diagnostics. Biosens. Bioelectron. 2016;84:120–125. - PubMed
    1. Boyken S.E., Chen Z., Groves B., Langan R.A., Oberdorfer G., Ford A., Gilmore J.M., Xu C., DiMaio F., Pereira J.H. De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity. Science. 2016;352:680–687. - PMC - PubMed
    1. Brown J.A., Sherlock G., Myers C.L., Burrows N.M., Deng C., Wu H.I., McCann K.E., Troyanskaya O.G., Brown J.M. Global analysis of gene function in yeast by quantitative phenotypic profiling. Mol. Syst. Biol. 2006;2:2006.0001. - PMC - PubMed
    1. Cherkasov A., Hilpert K., Jenssen H., Fjell C.D., Waldbrook M., Mullaly S.C., Volkmer R., Hancock R.E.W. Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS Chem. Biol. 2009;4:65–74. - PubMed

LinkOut - more resources

Feedback