Systematic Design of Thermostable MNEI Variants

A key motivation for our work was that while Monellin is exceptionally sweet, its instability under heat prevents it from being used in real-world applications. Previous attempts at improving the thermostability of MNEI, i.e. the single-chain engineered chain, have often focused on only a handful of mutations, for example, site-directed mutagenesis, which leaves most of the protein's mutational landscape unexplored (Liu et al., 2016).

To address this, we modeled and ranked mutations using DDG scanning with Rosetta (Leaver-Fay et al., 2010). All residues were exhaustively subjected to a comprehensive Rosetta mutational analysis, in which all possible single and double amino acid substitutions were explored computationally using the pmut_scan_parallel protocol in Rosetta (Point Mutant (“Pmut”) Scan Application, Pmut_Scan_Parallel, n.d.), which estimates the change in free energy (ΔΔG) upon mutation and provides a quantitative ranking of the impacts of the mutations on the stability of the protein (Barlow et al., 2018). In parallel, this also provides the ability to perform in silico alanine scanning to identify critical residues contributing to its conformational stability. By integrating the results of these two methods, we narrowed down thousands of possible substitutions into three monellin variants, which are predicted to have substantially improved thermostability while preserving the protein’s structural integrity.

These figures illustrate the results of our systematic mutational analysis of monellin using the Rosetta pmut_scan_parallel protocol. For the double mutant dataset, the top panel displays all possible combinations across the identified hotspot residues, with clustering highlighting patterns of stability. While the majority of double mutants were destabilizing, as shown in red, several clusters, particularly those involving residue 88, showed markedly negative ΔΔG values as shown in blue, indicating strong predicted stabilizing effects. By filtering down to the top 50 and then the top 10 lowest ΔΔG values, we observed that nearly all of the most promising double mutants were concentrated around a small subset of sites, with residue 88 emerging as especially critical for thermostability gains.

By documenting not only the resulting parts but also the reasoning and workflow that led to the generation of these variants, we provide future iGEM teams with a clear blueprint for designing and expressing proteins optimized for withstanding harsh conditions.

Revisit and Optimisation of Super Optimal Broth for E. coli Growth

The Super Optimal Broth (SOB) has been commonly used as a more nutritious broth in comparison to Luria-Bertani (LB) broth. With the addition of a higher concentration of tryptone and additional salts such as MgCl₂, MgSO₄, and KCl, SOB medium was initially developed to enhance transformation efficiency (Hanahan, 1983).

Currently, SOB medium is adopted in a wide range of biotechnology applications, including overnight cultures for the preparation of competent cells, plasmids and proteins (Green & Sambrook, 2020; Sohoni et al., 2015; Tabatabaee et al., 2013). This has motivated our team to review and optimise the medium on commonly used modern strains, e.g.. E. coli NiCo21(DE3) to further enhance protein production.

At baseline without substantial plasmid or protein production metabolic burden, Mg²⁺ concentrations were tested at 0, 10, 20, 30, and 40 mM with a constant K⁺ concentration of 2.5 mM, and K⁺ concentrations were tested at 0, 5, 7.5, and 10 mM with a constant Mg²⁺ concentration of 20 mM. Initial results suggest that at minimal metabolic burden, SOB medium effectively supports the growth of E. coli NiCo21(DE3) while altering the salt concentration may have a negative impact on growth. Further testing on IPTG-induced E. coli NiCo21(DE3) with plasmids for production, i.e. with metabolic burden, will be required to further assess the effect of salt concentrations.

Parts Used in Our Project

The table below lists the parts used in our project this year.

Molecular Dynamics Study of MNEI and its variants

Molecular Dynamics was performed with GROMACS to assess the stability of MNEI and its mutants shortlisted after point mutation scan. In brief, the structures were parameterized by CHARMM27 and subjected to simulation with water TIP3P for 10ns. The system was neutralized with Na+ and Cl- ions. Root Mean Square Deviation (RMSD) was evaluated to determine the deviation of conformation over time. For comparison, preprothaumatin (K801080), another sweet protein derived from Thaumatococcus daniellii, was selected for comparison. Note that structures of all proteins were prepared with AlphaFold and truncation was performed to remove flexible domains for stability assessment in MD.

Wild Type MNEI

MNEI R88A

MNEI R39M R88A

MNEI W3D F52C

Preprothaumatin

All MNEI and its variants moved similarly, and the total system energy remained fairly stable, highlighting the biophysical relevance of the molecular dynamic simulations. RMSD remained fairly stable with the exception of the R39M_R88A double mutant showing more variation transiently during the 5-6 ns in the simulation. Compared to the MNEIs, the Preprothaumatin fluctuated less at around 0.15 nm. Whilst all other MNEIs are in the range of 0.2 nm. Further analysis on thermostability by raising the simulation temperature and increasing the simulation time may further reveal molecular features of MNEI and its variants. Overall, these results show the potential of the novel variants of MNEI and should be further validated through experimental biophysical approaches such as isothermal titration calorimetry. 

Structural Alignment between AlphaFold predicted and CryoEM solved Taste receptor 1 complex

Sequence used for AF3 prediction (extracted frin PDB ID 9NOR):
T1R2:
MKTIIALSYIFCLVFAYPYDVPDYAAAAEPAENSDFYLPGDYLLGGLFSLHANMKGIVHLNFLQVPMCKEYEVKVIGYNLMQAMRFAVEEINNDSSLLPGVLLGYEIVDVCYISNNVQPVLYFLAHEDNLLPIQEDYSNYISRVVAVIGPDNSESVMTVANFLSLFLLPQITYSAISDELRDKVRFPALLRTTPSADHHIEAMVQLMLHFRWNWIIVLVSSDTYGRDNGQLLGERVARRDICIAFQETLPTLQPNQNMTSEERQRLVTIVDKLQQSTARVVVVFSPDLTLYHFFNEVLRQNFTGAVWIASESWAIDPVLHNLTELRHLGTFLGITIQSVPIPGFSEFREWGPQAGPPPLSRTSQSYTCNQECDNCLNATLSFNTILRLSGERVVYSVYSAVYAVAHALHSLLGCDKSTCTKRVVYPWQLLEEIWKVNFTLLDHQIFFDPQGDVALHLEIVQWQWDRSQNPFQSVASYYPLQRQLKNIQDISWHTINNTIPMSMCSKRCQSGQKKKPVGIHVCCFECIDCLPGTFLNHTEDEYECQACPNNEWSYQSETSCFKRQLVFLEWHEAPTIAVALLAALGFLSTLAILVIFWRHFQTPIVRSAGGPMCFLMLTLLLVAYMVVPVYVGPPKVSTCLCRQALFPLCFTICISCIAVRSFQIVCAFKMASRFPRAYSYWVRYQGPYVSMAFITVLKMVIVVIGMLATGLSPTTRTDPDDPKITIVSCNPNYRNSLLFNTSLDLLLSVVGFSFAYMGKELPTNYNEAKFITLSMTFYFTSSVSLCTFMSAYSGVLVTIVDLLVTVLNLLAISLGYFGPKCYMILFYPERNTPAYFNSMIQGYTMRRD

T1R3:
MKTIIALSYIFCLVFADYKDDDDKAAAAPLCLSQQLRMKGDYVLGGLFPLGEAEEAGLRSRTRPSSPVCTRFSSNGLLWALAMKMAVEEINNKSDLLPGLRLGYDLFDTCSEPVVAMKPSLMFLAKAGSRDIAAYCNYTQYQPRVLAVIGPHSSELAMVTGKFFSFFLMPQVSYGASMELLSARETFPSFFRTVPSDRVQLTAAAELLQEFGWNWVAALGSDDEYGRQGLSIFSALAAARGICIAHEGLVPLPRADDSRLGKVQDVLHQVNQSSVQVVLLFASVHAAHALFNYSISSRLSPKVWVASEAWLTSDLVMGLPGMAQMGTVLGFLQRGAQLHEFPQYVKTHLALATDPAFCSALGEREQGLEEDVVGQRCPQCDCITLQNVSAGLNHHQTFSVYAAVYSVAQALHNTLQCNASGCPAQDPVKPWQLLENMYNLTFHVGGLPLRFDSSGNVDMEYDLKLWVWQGSVPRLHDVGRFNGSLRTERLKIRWHTSDNQKPVSRCSRQCQEGQVRRVKGFHSCCYDCVDCEAGSYRQNPDDIACTFCGQDEWSPERSTRCFRRRSRFLAWGEPAVLLLLLLLSLALGLVLAALGLFVHHRDSPLVQASGGPLACFGLVCLGLVCLSVLLFPGQPSPARCLAQQPLSHLPLTGCLSTLFLQAAEIFVESELPLSWADRLSGCLRGPWAWLVVLLAMLVEVALCTWYLVAFPPEVVTDWHMLPTEALVHCRTRSWVSFGLAHATNATLAFLCFLGTFLVRSQPGRYNRARGLTFAMLAYFITWVSFVPLLANVQVVLRPAVQMGALLLCVLGILAAFHLPRCYLLMRQPGLNTPEFFLGGGPGDAQGQNDGNTGNQGKHE

AlphaFold3 prediction of the recently published Cryo-EM structure of taste receptor. Most domains of the taste receptor 1 complex are predicted confidently, with pLDDT in the range of 70-90. Although the iPTM score is 0.66, which is at the lower bound for non-failed interaction prediction, further comparative structural analysis, involving the comparison between AlphaFold3 predicted structure and Cryo-EM solved structure, revealed that the prediction is comparable to the experimentally solved structure. Instead of aligning the whole prediction to the solved Cryo-EM structure, the structure is divided into three parts and aligned separately, VFD region (Residue 1-504 of T1R2, Residue 1-506 of T1R3), linker region (Residue 505-567 of T1R2, Residue 507-565 of T1R3) and the TM region (Residue 568-848 of T1R2, Residue 566-859 of T1R3). Only the VFD and TM region is studied as VFD is the putative MNEI binding partner (Leone et al., 2016) and the TM region is key to downstream signalling (Shi et al., 2025).

References

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• Green, M. R., & Sambrook, J. (2020). The Inoue Method for Preparation and Transformation of Competent Escherichia coli: “Ultracompetent” Cells. Cold Spring Harbor Protocols, 2020(6), pdb.prot101196. https://doi.org/10.1101/pdb.prot101196
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• Liu, Q., Li, L., Yang, L., Liu, T., Cai, C., & Liu, B. (2016). Modification of the sweetness and stability of Sweet-Tasting protein Monellin by gene mutation and protein engineering. BioMed Research International, 2016, 1–7. https://doi.org/10.1155/2016/3647173
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• Tabatabaee, A., Siadat, S. D., Moosavi, S. F., Aghasadeghi, M. R., Memarnejadian, A., Pouriayevali, M. H., & Yavari, N. (2013, September 1). Overexpression and Purification of C-terminal Fragment of the Passenger Domain of Hap Protein from Nontypeable Haemophilus influenzae in a Highly Optimized Escherichia coli Expression System. https://pmc.ncbi.nlm.nih.gov/articles/PMC3732867/
• Ahmad, R., Xie, J., & Maudsley, S. (2020). G protein-coupled receptors in taste physiology and pharmacology. Frontiers in Pharmacology, 11, Article 587664. https://doi.org/10.3389/fphar.2020.587664
• Kim, U.-K., & Drayna, D. (2006). Variation in the human TAS1R taste receptor genes. Chemical Senses, 31(7), 599–611. https://doi.org/10.1093/chemse/bjj065
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• Shi, Z., Xu, W., Wu, L., Yue, X., Liu, S., Ding, W., Zhang, J., Meng, B., Zhao, L., Liu, X., Liu, J., Liu, Z., & Hua, T. (2025). Structural and functional characterization of human sweet taste receptor. Nature. https://doi.org/10.1038/s41586-025-09302-6