(346z) Advancing Rational Design of Peptide Self-Assembly at Surfaces

Rational control of self-assembly of peptides in interfacial environments is crucial for creating tailored bionanomaterials and to realize their biomedical and commercial applications. [1-3] Recent studies demonstrated promising approaches using designed mutations to control structure and assembly of peptides at graphene and other nanomaterials. [4-7] In designing mutations, both location of the mutation and mutant residues are two critical and possibly dependent choices. The former is to make a targeted structural change and the latter is for realizing it. Knowledge on the free energy of adsorption (∆A_ads) of amino acids or interaction energies of individual residues on surfaces have been used as a guide for selecting mutant residues with some success. [4-7] To further in this selection process and advance designed mutations approach, knowledge on dependence of the ∆A_ads of a residue on other residues is also required. Our goal is to work towards it and we start by developing insights into the dependence of ∆A_ads and structure of a residue on their neighboring residues. For this, we use graphene as our model surface and 36 model tripeptides with motif LNR-CR-Gly, where LNR and CR are variable left neighboring and central residues, respectively.

We consider a combination of strongly adsorbing (aromatic, Arg) and weakly adsorbing (Val, Leu, Ser, Thr) amino acids using ∆A_ads of amino acids estimated in a prior study. [8] Our results indicate that ∆A_ads of tripeptide is not the sum of ∆A_ads of each residue. However, the contributions from the strongly adsorbing amino acids was dominant suggesting that such residues could be used for strengthening peptide-graphene interactions irrespective of their neighboring residues. On the other hand, our structural analysis revealed that the dihedral angles of LNR and CR are more dependent in the adsorbed state than in bulk state. Together with ∆A_ads trends, this implies different backbone structures of a given CR can be realized for a similar ∆A_ads depending on LNR. Therefore, care has to be taken in designing mutations by incorporating context effects to engineer peptide structure at graphene surface. We will present these results and also discuss approaches using machine learning in designing mutations to control peptide assembly at surfaces.

References:

1. Tadi KK, Narayanan TN, Arepalli S, Banerjee K, Viswanathan S, Liepmann D, Ajayan PM, Renugopalakrishnan V. Engineered 2D nanomaterials–protein interfaces for efficient sensors. Mater. Res. 2015 Dec;30(23):3565-74.
2. Wang L, Zhang Y, Wu A, Wei G. Designed graphene-peptide nanocomposites for biosensor applications: A review. Analytica Chimica Acta. 2017 Sep 8;985:24-40.
3. Walsh TR, Knecht MR. Biomolecular Material Recognition in Two Dimensions: Peptide Binding to Graphene, h-BN, and MoS2 Nanosheets as Unique Bioconjugates. Bioconjugate Chem. 2019 Oct 8;30(11):2727-50.
4. No YH, Kim NH, Gnapareddy B, Choi B, Kim YT, Dugasani SR, Lee OS, Kim KH, Ko YS, Lee S, Lee SW. Nature-inspired construction of two-dimensionally self-assembled peptide on pristine graphene. Phys. Chem. Lett. 2017 Aug 17;8(16):3734-9.
5. Zou X, Wei S, Jasensky J, Xiao M, Wang Q, Brooks III CL, Chen Z. Molecular interactions between graphene and biological molecules. Am. Chem. Soc. 2017 Jan 16;139(5):1928-36.
6. Wei S, Zou X, Tian J, Huang H, Guo W, Chen Z. Control of Protein Conformation and Orientation on Graphene. Am. Chem. Soc. 2019 Nov 27;141(51):20335-43.
7. Hayamizu Y, So CR, Dag S, Page TS, Starkebaum D, Sarikaya M. Bioelectronic interfaces by spontaneously organized peptides on 2D atomic single layer materials. Scientific Reports. 2016 Sep 22;6:33778.
8. Dasetty S and Sarupria S., “Adsorption of Amino Acids on Graphene: Assessment of Current Force Fields”, Soft Matter, 15, 2359-2372