Design

In silico design of the peptide

The interaction between MAM7, Fibronectin, and phosphatidic acid is well-established in the literature. The MAM7 binding site in Fibronectin is located in the 30-kDa N-terminal region [1], which is also used by several other Fn-binding proteins.

However, we found that the structures of the proteins MAM7 and Fibronectin were not well established; hence, we decided to predict the structures for the same.

With the recent advent of various ab initio models, Alphafold has been regarded as one of the most reliable tool in successfully predicting the structures of proteins [2].

The structures for MAM7 and the Heparin-Binding Domains (HBD) of Fibronectin from the trout fish were thus predicted using AlphaFoldII and AlphaFoldI, respectively [2]. These predicted structures were screened for similarities with the existing literature, after which each of the five repeats of the type I module present in Fibronectin was considered as one peptide, respectively. Fibronectin is known to require five continuous repeats to dock with MAM7; therefore, designing a peptide that would bind to only one of these domains was deemed sufficient [1].

This process was iterative in nature, involving multiple cycles of design, build, test, and learn, as described on the engineering success page.

Fibronectin docked with MAM7
Fig 1: Fibronectin docked with MAM7 (visualised on PyMol [3])
Peptide docked with MAM7
Fig 2: Peptide docked with MAM7(visualised on PyMol)

Plasmid design

We planned to carry out the MAM7(BBa_K4200000) expression using E. coli as our chassis with pET-22b(+) as the vector. For peptide production, we plan on using the same vector system and chassis. However small peptides, when expressed using bacterial systems, are unstable in vivo due to their susceptibility to proteolytic degradation. To overcome the issue, the peptide encoding gene is fused with a gene encoding a carrier protein. For our experiments we decided to use the split intein derived from Synechocystis spp GyrB protein as the carrier protein.

What is an Intein, and why are we using it?

Inteins, short for INTervening protEINS, are in-frame interceding polypeptides that are capable of post-translationally excising themselves from the precursor protein through a protein splicing mechanism that is similar to the mechanism of mRNA splicing [5]. After the intein excision, its two flanking regions join together to form the functional protein or peptide sequence. Inteins undergo conditional protein splicing; that is, the splicing reaction is induced either by introducing small molecules like thiols, proteases, ligands, or by altering reaction conditions like temperature and pH [5]. They can be utilised to chemically alter almost any polypeptide backbone [6]. The reason we decided to choose inteins as part of the purification of our peptide is because they not only make the purification easy since they excise themselves from the peptide but they also help stabilize the peptide.

Inteins can be classified into four main categories, full-length inteins, mini-inteins, split inteins, and alanine inteins [5]. For the synthesis of our peptide, we will be using split inteins. Split inteins are composed of two brief intein segments, the N-terminal intein (IN) and the C-terminal intein (IC), which are both in charge of cleaving the respective terminals. Hence, split inteins help in the splicing of proteins at that specific terminal.

Split intein
Fig 3: Split Intein. Source:[6]

We have built our gene circuit in a way where the split intein and the 6X His-tag connected to our peptide at the N terminal. And the Ssp Gyr B split intein we choose will post-translationally self-cleave of our antimicrobial peptide at the N terminal.

Plasmid design for expressing fusion peptide

We planned on assembling the parts encoding the peptide(BBa_K4200313) and intein(BBa_K4200780) using scarless Gibson assembly into the vector pET-22b(+). The final plasmid design and the corresponding gene circuit for the fusion peptide is represented as belows:

Peptide production vector system
Fig 4: Peptide production vector system. Source:[4]
Plasmid gene circuit
Fig 5: Plasmid gene circuit.

The Delivery System

Given the susceptibility of our peptide to degradation and other factors, it was necessary to develop a sturdy delivery mechanism for the pisciculture set-up. From our literature review and human practices visits, we decided on the encapsulation of our antimicrobial peptide using chitosan nanoparticles due to its promising properties.

Once produced in-vitro, our peptide would be subjected to encapsulation for delivery. We would check the Zeta potential of our produced nanoparticles which is essential for determimining the stability of a nanoparticle in a given suspension. The final plan for in-field testing is to subject the inlet tank with a nanoparticle (containing peptide) loaded mesh such that as the water flows, the peptide is subsequently released.

This setup would help maintain a controlled release of our peptide into the water. Here, the bacteria in the water would interact with our peptide by binding to MAM7 present on the surface of the bacteria, thus rendering it harmless. Consequently, the microbiota of the aquaculture system is also maintained.

This mesh would serve as a one-time application, such that when the peptide is fully released out of the nanoparticle, it would have to be replaced with a new one.

But what would happen to the chitosan during the peptide release?

Given the properties of chitosan, being a biodegradable polymer, it would not harm the marine life present in the water. Prior to degradation, the chitosan, if and when ingested by the fish, would enhance their gut microbiota and induce positive immunomodulatory effects, too. [8]

References
  1. A. M. Krachler and K. Orth, ‘Functional characterization of the interaction between bacterial adhesin multivalent adhesion molecule 7 (MAM7) protein and its host cell ligands’, J Biol Chem, vol. 286, no. 45, pp. 38939–38947, Nov. 2011, doi: 10.1074/JBC.M111.291377.
  2. J. Jumper et al., ‘Highly accurate protein structure prediction with AlphaFold’, Nature 2021 596:7873, vol. 596, no. 7873, pp. 583–589, Jul. 2021, doi: 10.1038/s41586-021-03819-2.
  3. PyMOL The PyMOL Molecular Graphics System, Version 2.5.2 Schrödinger, LLC.
  4. Benchling [Biology Software]. (2022). Retrieved from https://benchling.com.
  5. G. N. Basturea, ‘Inteins’, Materials and Methods, vol. 10, Jan. 2020, doi: 10.13070/MM.EN.10.2868.
  6. N. H. Shah and T. W. Muir, ‘Inteins: Nature’s Gift to Protein Chemists’, Chemical science (Royal Society of Chemistry : 2010), vol. 5, no. 1, p. 446, Feb. 2014, doi: 10.1039/C3SC52951G.
  7. “https://www.molecularcloud.org/what-is-Gibson-assembly.html.”
  8. C. Nikapitiya et al., ‘Chitosan nanoparticles: A positive immune response modulator as display in zebrafish larvae against Aeromonas hydrophila infection’, Fish Shellfish Immunol, vol. 76, pp. 240–246, May 2018, doi: 10.1016/J.FSI.2018.03.010.