RESULT

Identification of Artificial SPIKE-Interacted Small Peptides

1. Construction of random library

The random fragment (iGEM_Fragment-1) was synthesized, and the double-strand sequence were obtained by PCR amplification with fragment amplifying primers (Random_Primer1, Random_Primer2). The obtained PCR products were constructed into entry vector pDONR207 by Gateway BP reaction, resulting in the primary library pONR207-random peptide (Figure 1). The pDONR207-random peptide plasmid library was subsequently constructed into the destination vector pGADT7-GW through Gateway LR reaction (Figure 2), resulting in a yeast two-hybrid AD plasmid library. The secondary plasmid library (pGADT7 GW-random peptide) was then transferred into the yeast Y187 strain, resulting in a Y187 AD-tagged random-peptide library (Figure 3).

1. Adaptor information for the Gateway system


Figure 1. The pDONR207 was used for the construction of the Entry library. The cyan bar shows the DNA sequences coding the random peptide library.


Figure 2. The pGADT7 GW was used for the construction of the secondary library. The gray bar shows the DNA sequences coding the random peptide library.


Figure 3. The colonies of AD-random Libray in AH109 strain.


2. The characteraztion of the Yeast two hybrid Library

The 150-bp insert fragments were amplified using primers CCAGATTACGCTCATATGACAAGTTTGTAC and TCATCTGCAGCTCGAGACCACTTTGTACAA. The constructed libraries were sequenced using the Illumina Novaseq 6000 platform to generate raw data, following the manufacturer’s instructions by the company of ANNOROAD Biotech. Co., Ltd. (China). After quality control, about 34.5 GB of clean data were generated. According to the design principles (TCGTCGGGGACAACTTTGTACAAAAAAGTTGGAACC-(NNK)20-TAAGACCCAACTTTCTTGTACAAAGTTGTGCGGCCGCC), 60bp random sequences were extracted from the clean data and translated into amino acid sequences using standard genetic codons. The translation was stopped from the first stop codon, and peptides less than five aa in length after translation were removed. Finally, we obtained 68,190,232 peptides in total and 3,359,176 peptides after removing redundancy. (table1)


Table 1: Statistics of sequencing results


We carried out saturation analyses and found that about 35Gb data was far from covering all the kinds of peptides in our random peptide library (Figure 4A), indicating that our random peptide library has a high diversity and a broad potential for protein interaction investigations and applications.

Figure 4. statistics of the sequencing data about our random peptide library. (A) Statistics of the peptide number of different lengths; (B) Saturation analyses according to randomly sampling from the sequencing data. The abscissa represents the proportion of random sampling including 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90%, and the ordinate represents the number of peptides detected corresponding to the percentage of sampled sequencing data.

3. The screening of SPIKE-RBD binding peptides

AH109 strain was transformed with pGWBD_Spkie-RBD plasmid to obtain the bait construction of yeast two-hybrid. Through the yeast two-hybrid mating system (Figure 5A), 36 positive colonies were finally obtained (Figure 5B). After amplification with AD-REV and AD-FWD primers, 35 PCR fragments were obtained, and then 34 random peptide library sequences were obtained by sanger sequencing (Table 1).

Figure 5. The screening of SPIKE-RBD binding peptides through Y2h. (A) The microscopy of the mating process of AH109 and Y187. (B) The positive clonies of mating yeast gown on the –Trp/-Leu/-His/-Ade SD medium.

Table 1.The aa sequences of 34 SPIKE-RBD binding peptides

We aim to find the strongest short peptide sequence that can interact with the RBD structural domain of Spike, the 34 sequences were aligned, and the conserved domains were obtained (Figure 6). Moreover, the consensus sequence WGPMLWRAFDRAMSAGLGGW was obtained, which is the short peptide that we think has strong interaction with the Spkie RBD domain.

Figure 6. Multiple sequence alignment of the 34 peptides interacted with Spike, the quality score is calculated for each site in the alignment by summing, and a consensus sequence was generated according to the alignment analysis.

Validation of a suicide system

In order to adapt our toxic proteins to the escape system, we made some modifications to the lytic protein.The best concentration is achieved by reducing the toxicity of the toxic protein.We have modified some amino acids so that the bacteria do not die too quickly when expressing the protein.In the end, we chose one option and transferred the gene into a JLU-China's light-controlled parts. Finally, we grow this bacteria under the blue light condition.

Figure 7.the suicide plasmid

The final results showed significant growth suppression within 10 to 20 hours after the insertion of the suicide gene, as we expected

Figure5:The three above:Darkness nurtures The next three:bu lights 100μmol/m2/sCulture for 20 hours

Validation of two-component systems

To determine the detection limit and linear range of our cellular sensors. We established the relationship between the final fluorescence intensity and spike protein concentration by modeling.After obtaining our peptide library sequence, we transfered the sequence into the sensing unit of the PmrB (thanks to the strain provided by Tsinghua) , followed by the Spike protein after the optimized sequence adopted by the yeast doublet previously We did a series of gradient experiments, and the final results were similar to the modeling results.However, the detection limit was too high (that is, the sensitivity of the two-component system to the optimized spike protein was too low) . So we wanted to use peptides that could"Capture" Spike's properties to concentrate the virus.

Figure9:Relationship between spike protein concentration and fluorescence intensity(left:spike induction results under fluorescence microscope. The engineering bacteria was induced with 1.0 μg/mL~3μg/mL spike(after codon optimization). Photos were taken under fluorescence microscope.right:the theoretical relationship between spike protein concentration and fluorescence intensity was made by mathematical modeling method based on the principle of two-component system)

Validation of the escape system

To enrich our virus, we wanted to use the peptides to“Catch” the virus by binding specifically to the spike.But after catching the virus, we also need E. coli to be able to move in a given direction.For ease of use, we chose blue light as our trigger mechanism.


When irradiated with blue light, E. coli can move in a straight line at high speed because of the large amount of expression of this gene, but not in the region irradiated with blue light. E. coli rolls around because of the knock-out of the gene. After some time, E. coli will accumulate in areas without blue light. This is the“Light-repellent” we construct for E. coli. Take advantage of this property,E. coli then“Carries”the protein to areas where there is no blue light.

SOE PCR

First, we simulated the structure of HACE2 based on the RBD with SARS-CoV-2 using UCSF Chimera and predicted the site of enhanced HACE2 For these sites, we designed corresponding primers to modify and synthesize the PCR productsWe recovered the pcr reaction solution and sequenced it to make sure that all the mutations were successful, then we recovered the fragments and constructed the vectors by infusion ligation. Finally we used the constructed vector to perform yeast two-hybrid, but unfortunately we found that the vector we designed did not show their ability to interact with each other in this way after performing yeast two-hybrid, so we communicated with the PI and wondered if the nls had not been added and thus no yeast two-hybrid did not show the results it should, so we modified the experiment again and gave our mutant sequence with the yeast nls fragment RGRGRGRGRGRGRGRGGYRGRARGFAPY*(Table3)

Table 3.ACE2 point mutation predicted enhancement sites

Number Spike binding sites ACE2 Distance change before and after mutation Binding energy Predictions
1 SARS-CoV-2 hACE2 Original sequence -14.38 /
2 Q493 l_21_V 6.541->6.506 -14.15 Stronger
3 F486 K_26_R 5.132->3.8007 -14.38 Stronger
4 F486 T_27_A 9.886->9.387 -14.39 Stronger
5 L455 K_31_R 13.985->11.889 -14.37
stronger
6 F486 N_33_I 8.529->8.309 -14.37 Stronger
7 F486 H_34_R 15.184->12.349 -14.36 Stronger
8 F486 E_37_K 15.175->10.415 -14.35 Stronger
9 F486 T_92_I 7.047->7.0247 -14.38 Stronger

Figure10: Figure E and F show the atomic spacing before and after the mutation of the F486 site of Spike with the site 34 (H) of ACE2.Figure G and H show the atomic spacing before and after the mutation of the L455 site of Spike with the site 31 (K) of ACE2.


Figure11:Figure I and Figure J show theatomic distance between theQ493 site of Spike and the 21(I )site of ACE2 before and after mutation.Figure K and Figure L show theatomic distance between the F486site of Spike and the 27 (T) site ofACE2 before and after mutation.


Figure12: Figure M and Figure N show theatomic distance between the F486site of Spike and the 26 (K) site ofACE2 before and after mutation.Figure 0 and Figure P show theatomic distance between the L455site of Spike and the 92 (T) site ofACE2 before and after mutation.


Figure Qand Figure R show theatomic distance between theF486 site of Spike and the 33 (N)site of ACE2 before and after mutation.Figure S and Figure T show theatomic distance between theL455 site of Spike and the 37 (E)site of ACE2 before and after mutation.