Antimicrobial peptides (AMPs) represent a promising alternative to conventional antibiotics. When fused with endolysins, AMPs can enhance the activity of these enzymes, notably against Gram-negative bacteria. The AMP component facilitates penetration through the outer membrane, thereby enabling the endolysin to more effectively traverse this barrier and reach the peptidoglycan cell wall, where it exerts its lytic function.

The mechanism of AMP insertion into the outer membrane of bacteria is still not well understood and most mechanistic studies of AMP focus on the interaction between AMP and the inner membrane. Therefore, we provided AMP-MD, an MD simulation platform that studies atomic-scale membrane insertion of peptides.

We provided the technical details of our MD simulations in our modeling page and the MD inputs and scripts with a tutorial on the iGEM Münster Gitlab Repository for future iGEM teams to run their own MD simulation systems consisting of an outer membrane and one or more AMPs. Using similar modeling procedures enhances the comparability and consistency of their results, paving the way for an in-depth understanding of different modes of action in AMP research.

Through our mechanistic studies we described the insertion process of the AMP CM15 into the E. coli R3 O111 membrane in detail and analyzed how this CM15 increases the membrane permeation of other molecules and also itself. This increase in membrane permeation through the peptide was first published by Hancock (Hancock, 1997) and has never been shown in an MD simulation study before. By showing and understanding this mechanism through our AMP-MD simulations, we provide future iGEM teams that work on AMPs a better foundation when designing their own AMPs or enhancing their effectiveness against gram-negative bacteria. Our simulation platform is adaptable to other species of bacteria and also other peptides or structural protein motives, enabling future iGEM teams to find and model the perfect combination for their specific use case.

We aimed to establish a cell-free endolysin expression platform enabling the reliable and rapid identification of lead variants, particularly targeting Gram-negative bacteria. Building upon this, we developed a modular toolbox designed to facilitate endolysin modification and the generation of target-optimized variants. This toolbox is available for future iGEM teams to support continued development in this field.

Background

Endolysins are bacteriophage-encoded enzymes that hydrolyze the bacterial peptidoglycan (PG) from within the host cell, inducing cell lysis and releasing progeny phages during the final stage of their replication cycle. The structural characteristics of endolysins are thereby dependent upon the bacterial target. Endolysins acting on Gram-positive bacteria typically exhibit a modular structure compromising an N-terminal enzymatically active domain (EAD) and a C-terminal cell wall binding domain (CBD). Conversely, Gram-negative-specific endolysins possess a globular structure comprising only an EAD, as they do not require the CBD to recognize the substrate due to the presence of an outer membrane. The various EADs typically exhibit either glucosaminidase/muramidase, amidase, or endopeptidase activity, thereby facilitating PG degradation. Due to their high specificity and potent lytic activity, endolysins are increasingly explored as alternative therapeutics to antibiotics (Gontijo, Jorge and Brocchi, 2021; Zheng and Zhang, 2024).

The cationic and amphipathic properties of antimicrobial peptides (AMPs) make them particularly promising candidates for fusion with endolysins to enhance their activity against Gram-negative bacteria. In these bacteria, the outer membrane typically prevents endolysins from reaching and degrading the peptidoglycan layer. AMPs, through electrostatic interactions with negatively charged bacterial surface components, can increase membrane permeability, thereby enabling endolysins to access and hydrolyze the peptidoglycan (Carratalá et al., 2023).

Our cloning strategy is based on Golden Gate Assembly (GGA) with specific positional tags allowing for modular assembly and rapid exchange of individual components, facilitating the efficient generation of new lead endolysin variants. All positional tags used in the system are summarized in Table 1.

We employed the ALiCE® cell-free expression system as the foundation of our production platform and designed the toolbox around the pALiCE01 plasmid, which is optimized for cytosolic expression in ALiCE®. Figure 1 exhibits an exemplary plasmid map of the pALICE01_K14_PlyEc2_StrepII plasmid.

Figure 1: Plasmid map of pALICE01_K14_PlyEc2_StrepII with required primers for the GGA. Further information about the primers are available in Table 3.

By simply modifying the first positional tag (containing the start codon) and adjusting the backbone primers, the fusion construct cassette (AMP–linker–endolysin) can however easily be adapted to alternative plasmid backbones.

Table 1: Description of the position tags utilized for the GGA of pALICE01_K14_PlyEc2_StrepII plasmid

Position	Crossover	Sequence
1	Backbone → K14+linker	CCATGA
2	AMP+linker → Endolysin	TCAGCA
3	Endolysin → Backbone	CTCGAG

For our platform we used the parts listed in Table 2. Please note that in the standard Sushi S1 and K14 parts no linker is included.

Table 2: Part contribution for our toolbox approach.

Part number	Name	Function
BBa_25XU6JMW	K14	AMP
BBa-K5057004	Sushi S1	AMP
BBa_25H8EKRX	PlyEc2	Endolysin
BBa_25UOAFAH	PlyYouna2	Endolysin
BBa_2568G2XZ	K14_linker_PlyEc2 fusion construct	Fusion construct cassette

Further, all the necessary primers for the assembly of the pALICE01_AMP_Endolysin_StrepII plasmid via Golden Gate Assembly can be found in Table 3.

Table 3: Primers utilized for the Golden Gate Assembly of pALICE01_AMP_Endolysin_StrepII plasmid

Primer	Sequence
GGA.bb_pALICE01_c-term_Strep-II.FOR	TATGGTCTCAGGTACCAAGCTCTTCTGGTTTG
GGA.bb_pALICE01_c-term_Strep-II_SushiS1.REV	TATGGTCTCACCATGGTAGTTGTAGAATGTAAAATGTAATGT
GGA.bb_pALICE01_c-term_Strep-II_K14.REV	TATGGTCTCATCATGGTAGTTGTAGAATGTAAAATGTAATG
GGA1_ATG_SushiS1_linker2.FOR	TATGGTCTCAATGGGGTTCAAACTGAAAGGCA
GGA1_ATG_K14_linker2.FOR	TATGGTCTCAATGAAGTGGAAGCTTTTCAAAAAAATCG
GGA1_ATG_AMP_linker2.REV	TATGGTCTCATGCTGATGCGCCGGCTCC
GGA2_plyEc2.FOR	TATGGTCTCAAGCAAATCAAACACAATTCCAGAAGG
GGA2_plyEc2.REV	TATGGTCTCACGAGCACCACCAGTACCGATTTTG
GGA2_plyYouna2.FOR	TATGGTCTCAAGCAGCAAGTAAACCATTGTTGGTG
GGA2_plyYouna2.REV	TATGGTCTCACGAGCCTCCTCTTCACTGTGACG
GGA3_Strep-II-tag_GGGS_c-term.FOR	TATGGTCTCACTCGAGGGTGGTGGTG
GGA3_Strep-II-tag_GGGS_c-term.REV	TATGGTCTCATACCTTATTTCTCAAATTGGGGATGTGAC

Carratalá, J.V. et al. (2023) “Design strategies for positively charged endolysins: Insights into Artilysin development,” Biotechnology Advances, 69, p. 108250. Available at:
https://doi.org/10.1016/j.biotechadv.2023.108250 .

Gontijo, M.T.P., Jorge, G.P. and Brocchi, M. (2021) “Current Status of Endolysin-Based Treatments against Gram-Negative Bacteria,” Antibiotics, 10(10), p. 1143. Available at:
https://doi.org/10.3390/antibiotics10101143 .

Hancock, R.E. (1997) “Peptide antibiotics,” The Lancet, 349(9049), pp. 418–422. Available at:
https://doi.org/10.1016/S0140-6736(97)80051-7 .

Zheng, T. and Zhang, C. (2024) “Engineering strategies and challenges of endolysin as an antibacterial agent against Gram-negative bacteria,” Microbial Biotechnology, 17(4), p. e14465. Available at:
https://doi.org/10.1111/1751-7915.14465 .