High throughput screening via molecular docking and scoringWe screen a number of the candidate proteins from the protein database which can bind with cortisol and present them along with their binding energies via an AutoDock Vina based tool. The binding affinity results of top five candidate PDBs using molecular docking is listed in Table 1.Table 1 List of binding energies of top five candidate proteins screened from protein database.The interaction diagrams of the top three candidate proteins are presented in Fig. 2. These candidates were selected for further MD calculations and validation by performing umbrella sampling and computing the potential of mean force in presence of a solvent. 6NWL and 2VDY were not considered despite its higher binding energy as its binding pocket was hidden and > 100 amino acids long.Fig. 2AutoDock Vina screening of top three candidate proteins with their corresponding interaction diagrams with cortisol.The remaining list of thirty candidate proteins, considered along with their binding affinity, is presented for completeness in Figure S2. These candidate proteins were subsequently ranked according to their binding affinity. A continuous sequence of amino acids was selected as candidate bioreceptors based on their active binding pockets. This biomimetic procedure enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology42.Validation of the top candidates via steered molecular modellingWe performed SMD simulation of the top protein–ligand complex in eccrine sweat solution43. The results are presented for the evolution of the potential energy and RMSD of the native protein 2V95 and cortisol complex. Figure 3 shows the RMSD values, while Fig. 4 shows the binding affinity. The RMSD values after stabilization are within 0.6 nm which demonstrates the stability of the protein 2V95. We treat the native protein and cortisol complex as a benchmark in our work.Finally, we employed WHAM, for extracting the PMF. The values of the PMF converge to a stable value at approximately 4 nm on the selected reaction coordinate as shown in Fig. 4. The binding energy (ΔG) is then simply the difference between the highest and the lowest values of the PMF curve. The difference between the highest and lowest values of PMF is approximately -10.2 kcal/mol. The RMSD of the native protein 2V95 is presented in supporting information Figure S5A and other candidate proteins are similarly modelled and presented in supporting information figures S5B and S5C. The results presented in supporting information in figures S10A and S10B for represent similar binding energy and serve as a validation for the selected SMD methodology and umbrella sampling settings. Fig. 3RMSD of 2V95 native protein bound with cortisol over 100ns demonstrating stability.Fig. 4PMF of 2V95 native protein with cortisol demonstrating binding force over pull distance i.e. reaction coordinate.A close inspection of the energetically favourable binding conformations of the native protein (2V95) bound to cortisol offers a physically contiguous sequence of amino acid residues (from 220 to 257) as opposed to the other candidate proteins and therefore selected for further consideration for peptide design.Protocol for selection of the baseline peptide and comparison with corticosteroid-binding globulins (CBGs)The candidate bioreceptor peptide is identified by selecting a few contiguous sequences of amino acids (Table S2 in supporting information) from the active binding sites of the 2V95 protein, with the imposed constraint that each sequence should be less than 50 amino acids in length. The pre-determined binding affinity value with cortisol was imposed as another constraint as depicted in Figure S6 in supporting information. The selected sequences are listed with their corresponding binding energy with cortisol and compared with CBGs. Finally, a peptide with a sequence of 38 amino acids is selected with a cysteine residue at its end. It is represented by the single letter sequence CQLIQMDYVGNGTAFFILPDQGQMDTVIAALSRDTIDR. The selection ensures a good binding with cortisol with a relatively smaller sequence thereby, ensuring ease of synthesis at a lower cost.Baseline peptide binding energies with competing speciesThe baseline peptide is then modelled with glucose, progesterone and testosterone. The binding energies of these interfering species are listed in Table 2. Figure 5 shows the interaction diagram of progesterone and glucose with cortisol. It was important to explore the binding affinity of the selected peptide with progesterone since CBGs are known to bind well with this hormone20. As we see, the binding energies of the peptide with all three of these species are much lower than the binding energy with cortisol.Table 2 Interaction diagrams of baseline peptide compared with interfering ligands.Fig. 5Interaction diagram of interfering species such as progesterone and glucose with cortisol.Similarity analysis of the baseline peptide using smart BLASTThe baseline peptide is further compared with the proteins 2V95 (371 amino acids long) and 2VDY (373 amino acids long), which are rat and human CBGs respectively. These two proteins were chosen for comparison due to their relatively higher binding affinity with cortisol and the presence of a physically contiguous sequence of interacting amino acids in the binding region. The comparison was performed using the Basic Local Alignment Search Tool (BLAST)44,45 tool hosted by the National Center for Biotechnology Information (NCBI) online server. The native peptide was found to have > 75% similarity with these two proteins, especially in their active binding sites for cortisol. The relevant screenshots from smart BLAST demonstrating similarity between baseline peptide and protein 2V95 and other candidate proteins are shown in Figure S3A and Figure S3B respectively in supporting information. When the selected baseline peptide is subsequently compared with other proteins from the database smart BLAST, it is observed that there is “landmark match” with both CBGs from as illustrated in Figure S3B. This is an extremely promising result considering our biomimetic route of peptide selection as opposed to ab initio peptide design via various extremely computationally intensive combinatorial methods25. The structures of these two large proteins have evolved to cater to a large number of design requirements, resulting in macromolecular structures that are more than 370 amino acid sequences long. Furthermore, the natural design considerations for a CBG and that of a bioreceptor are different. We argue that the entire macromolecular structure of the native proteins is not necessary for biosensor applications. In this work, we propose a lean peptide design approximately one tenth of the sequence length of native CBG proteins with comparable binding affinity and tertiary structure as a baseline candidate to develop the intended biosensor. The design considerations for our proposed biosensor are limited to bioreceptor development. Therefore, we have only focussed on parameters such as binding affinity with target ligand, solubility, sequence length, tertiary structure, and ability to bind with gold electrodes.Significance of the in silico eccrine sweat modelThe choice of the solvent model, namely the eccrine sweat model30 developed earlier by the same team, has a significant impact on determining the stability and strength of the protein-ligand complexes. The hydrophobic regions41 of the baseline peptide minimizes its exposure to water molecules by binding to ligands which can lead to increased binding affinity. Similarly, the hydrogen bonding capabilities influence the formation and strength of hydrogen bonds between proteins and ligands. Figure S4 shows the hydrophobic and hydrophilic parts of the peptide. Finally, as discussed in the next section, a full atomistic molecular dynamics simulation of the peptide is performed in eccrine sweat solution as a validation exercise.Validation of the baseline peptide via steered molecular dynamics (SMD) simulationWe performed SMD simulation of the baseline peptide–ligand complex in an eccrine sweat environment as described in the methods section. Figure 6A shows the peptide anchored to gold in a sweat environment. The C-alpha atoms RMSD (< 0.6 nm) of the baseline peptide and ligand confirms the stability of the complex and the equilibration of the system as shown in Fig. 6B. Despite the considerable shorter sequence length, the baseline peptide is stable and retains bound cortisol for the entire simulation run of 30 ns. The binding energy (approximately – 9.8 kcal/mol) computed is comparable with the native protein 2V95 (approximately – 10.2 kcal/mol) and inspires confidence in the utility of the proposed baseline peptide.Fig. 6(A) Model of the proposed baseline peptide bioreceptor immobilized on gold substrate and bound with cortisol docked in the binding cavity. (B) Binding pocket of baseline peptide and bound cortisol.Fig. 7PMF plot with standard deviation of candidate baseline peptide bioreceptor with cortisol demonstrating binding affinity over pull distance of approx. 4 nm.Fig. 8C-alpha atoms RMSD of candidate baseline peptide bound with cortisol demonstrating stability over 100 ns.As seen in Fig. 7, the PMFs of the native protein 2V95 bound to cortisol and of the baseline peptide demonstrates binding affinity of approximately − 9.8 kcal/mol and in Figure S9 in supporting information demonstrates number of hydrogen bonds providing insights into the binding affinity over a 100 ns simulation.Advantages of peptide bioreceptors over conventional antibody based bioreceptorsTraditional methods of developing biosensors involve using antibodies as the bioreceptor. Antibody based electrochemical sensors have been used for the measurement of cortisol and other biomolecules due to their high specificity and sensitivity. However, they suffer from limitations such as storage requirement, temperature instability, high cost, cross-reactivity, and batch-to-batch variability. Antibodies are large molecules that are not readily synthesized and can be chemically unstable17,19. To address the issues, we propose the development of an inexpensive, synthetic peptide which can be considered as an alternative to these antibodies. Our approach is biomimetically inspired i.e., selecting a continuous sequence of amino acids from the native protein, aimed at retaining structure, scaffold and stability of the finalized baseline peptide as opposed to combinatorial peptide design46.DiscussionOur goal was to systematically arrive at a candidate baseline peptide following a biomimetic approach. In addition to considering the specific binding affinity towards cortisol and a shorter sequence length, other factors such as ease of immobilization on gold electrodes, solubility, ease of synthesis and cost of synthesis need to be considered. Furthermore, the CBGs presented in Table S2 demonstrate a strong binding affinity towards progesterone, which needs to be corrected in the proposed peptide design to avoid non-specific interactions.Additional parameters such as size and sequence length of the peptide also play an important role in the design. In the current work, we have computed these values and have only optimized the design for the minimum continuous sequence length for the desired binding affinity. This is illustrated in the Table S2 (with peptides #1, #2, and #3 as the representative candidates) where various lengths of amino acid sequences are assessed before finalizing the 38 amino acids long sequence. The other native proteins that have similar or improved binding affinity with cortisol (e.g. 6NWL as presented in Table 1 and illustrated in Fig. 2) do not offer a contiguous sequence of amino acids < 50 AA, to create a peptide with a similar structure. Hence, they are not considered further. A final consideration is the ability of the designed peptide to bind to the surface of gold electrodes. This is achieved by a cysteine termination at the N terminal of the baseline peptide. The cysteine residue contains thiol groups that can readily form strong covalent bonds with gold atoms on the electrode surface, creating a stable gold-thiol bond.The proposed peptide sequence CQLIQMDYVGNGTAFFILPDQGQMDTVIAALSRDTIDR, as depicted in Figure S4 in supporting information, is > 50% hydrophobic in nature. As it is an acidic peptide, it will require an acidic solvent to dissolve it initially. Later the solution pH will need to be adjusted to 6.3 to mimic the mean pH value of eccrine sweat. Preliminary simulations of the proposed peptide, as presented in Figure S7 and Figure S8 in supporting information, have shown that the immobilization of the peptide on gold does not significantly alter its structure. The RMSD of the peptide and cortisol is within reasonable limits as presented in Fig. 8. Finally, the RMSD plot of the peptide in Fig. 8 demonstrates no significant increase in RMSD as compared to the RMSD of the native protein 2V95, despite the shorter sequence of the peptide. The proposed baseline peptide is one tenth the size of the native protein. Hence estimated cost of synthesis for 5 mg of the peptide at > 80% purity (HPLC Purification) is approximately USD 400. In contrast, the same amount of a conventional monoclonal antibody for cortisol (e.g. CORT-1) costs USD 4000. Furthermore, the central part of the sequence demonstrates hydrophobicity to improve its affinity and stability. The cysteine amino acid at N-Terminal with a reactive – SH group provides ease of binding with gold electrodes. In summary, the proposed baseline peptide can be considered as an efficient, cost effective, and a viable alternative to antibody-based cortisol bioreceptors.
