The results of this study are presented below in three sections. The first section presents the enzymes chosen owing to their association with DA and the interaction energy resulting from the enzyme–DA complex. The second section presents the variables that were controlled during the designing of the bioreceptor and the affinity results for DA after each structural modification. Finally, the third section includes the description of the parameters that allowed three bioreceptor candidates to be selected for future synthesis as well as the molecular interaction distances between DA and the bioreceptor.
2.1. Selection and analysis of enzymes with DA interaction
2.1.1. Enzyme Selection
According to the methodology proposed and followed, enzymes belonging to the enzyme class 1.14.18 were retrieved from the Protein Data Bank (PDB). However, the results only yielded the following three crystallized enzymes: tyrosinase (1.14.18.1) as 5M8L, methane monooxygenase (particulate) (1.14.18.9) as 1YEW, and methylsterol monooxygenase (1.14.18.9) as 4IIT.
Because only three crystallized enzymes were found out of the nine enzymes proposed, the authors decided to seek other enzymes that inter
act with DA or its analogs in the physiological processes as well as those crystallized using DA as the ligand. Accordingly, the following 15 enzymes were found (Table 1).
Table 1. Enzymes related to dopamine that have been crystallized and registered in the PDB.
Common name
|
PDB Nomenclature
|
Crystal structure of human tyrosinase related protein 1
|
5M8L
|
The Phenylacetyl-CoA monooxygenase PaaABC subcomplex with phenylacetyl-CoA
|
4IIT
|
Structure of Drosophila dopamine transporter bound to neurotransmitter dopamine
|
4XP1
|
Structure of AED7-norepineprhine complex
|
3DYE
|
ABC-transporter choline binding protein in complex with acetylcholine
|
2RIN
|
Structure of biogenic amine binding protein from Rhodnius prolixus
|
4GET
|
Structure of human sulfotransferase SULT1A3 in complex with dopamine and 3-phosphoadenosine 5-phosphate
|
2A3R
|
Quinone reductase 2 in complex with dopamine
|
2QMZ
|
Structure of the human dopamine D3 receptor in complex with eticlopride
|
3PBL
|
Structure of the human D4 dopamine receptor in complex with Nemonapride
|
5WIU
|
Crystal structure of the N-terminal domain of DrrA/SidM from Legionella pneumophila
|
3NKU
|
Structure of human I113T SOD1 mutant complexed with dopamine in the p21 space group
|
4A7V
|
Structure of Norcoclaurine synthase from Thalictrum flavum in complex with dopamine and hydroxybenzaldehyde
|
2VQ5
|
Structure of Drosophila melanogaster E47D dopamine N-acetyltransferase in ternary complex with CoA and Acetyl-dopamine
|
5GIG
|
Structure of human D-amino acid oxidase complexed with imino-DOPA
|
2E82
|
2.1.1. Interaction analysis of the enzyme–DA complex.
Table 2 presents the results obtained for each enzyme, calculated as the average of the three tests performed. Based on these results, the three enzymes selected with the highest affinity for DA were 5M8L, 2A3R, and 4IIT, which are in bold text the Table 2. These enzymes will be used as a starting point for bioreceptor design.
In addition, the docking performed to calculate these results, was specific to each enzyme because of the grid box had to be different, so to choose it, first, the sequence of each enzyme was analyzed in the PDB to identify amino acids corresponded to the binding site for residue LDP (L-Dopamine), after this, in AutoDock Tools those amino acids were selected and the dimensions and coordinates of the grid box were adjusted to ensure that every amino acid was inside the grid. With these dimensions and coordinates the docking in AutoDock Vina were performed to calculate the interaction energy with DA.
Table 2. Interaction energy (average) result of each enzyme for the calculation of affinity for dopamine.
Enzyme
|
Interaction energy (average) (kcal/mol)
|
Standard deviation
|
5M8L
|
−6.2
|
0.1
|
4IIT
|
−6.1
|
0.1
|
4XP1
|
−5.2
|
0.0
|
3DYE
|
−5.6
|
0.1
|
2RIN
|
−5.0
|
0.0
|
4GET
|
−5.4
|
0.0
|
2A3R
|
−6.3
|
0.0
|
2QMZ
|
−5.6
|
0.2
|
3PBL
|
−5.6
|
0.1
|
5WIU
|
−4.5
|
0.1
|
3NKU
|
−5.5
|
0.0
|
4A7V
|
−5.2
|
0.1
|
2VQ5
|
−5.0
|
0.1
|
5GIG
|
−4.9
|
0.1
|
2E82
|
−5.9
|
0.1
|
2.2. Design and analysis of bioreceptors that interact with DA
The bioreceptors were designed to perform a miniaturization allowing competition in affinity and molecular distances with DAT, a protein capable of modulating the DA availability in the synaptic space, because it recaptures extracellular DA and enters the presynaptic neurons [22]. Considering that this protein is activated or deactivated according to the both short- and long-term physiological demands of the neurons [22], it must be mimicked to recognize the DA not captured by postsynaptic neurons within the system when it is coupled to a biosensor system.
It is important to mention that miniaturization is a concept that has been defined as “the process of doing something on a very small scale using modern technology” [23] or as “a version of something on a small scale or small size” [24]. In addition, this miniaturization trend was first applied to electronic devices in the 1960’s and later evolved to their replacement by biological molecules [25] and to drug releasing processes [26].
It is important to keep in mind that human DAT (hDAT) has not been crystallized yet; therefore, there is no report on its crystalline structure determined through experimental methods. Nevertheless, the structure of DATs of other species, such as Drosophila melanogaster, has already been determined [27] (the PDB code for this protein is 4XP1). Therefore, this registered 4XP1 protein was deemed as analogous to hDAT. However, there are computational models that have simulated the structure and interactions of hDAT [28], and this information will also be considered in the following design of bioreceptors.
Further, the initial bioreceptors were designed from the amino acids of the active site of the 5M8L, 4IIT, and 2A3R enzymes to which structural changes were sequentially made as per the provisions included in the methodology.
The first group of bioreceptors designed comprised peptides constructed from the amino acids of the 5M8L, 4IIT, and 2A3R enzymes. Table 3 denotes their identifying code, their composing amino acid sequence, the affinity result calculated as the average of the triplicate calculation results along with their standard deviation. In addition, Figure 1 displays the images related to the design of these three bioreceptors.
Table 3. Results of bioreceptors designed exclusively based on the previously selected amino acids.
Bioreceptor code
|
Amino acid sequence
|
Average affinity (kcal/mol)
|
Standard deviation (kcal/mol)
|
1.0
|
NWR
|
−2.6
|
0.0
|
2.0
|
RQKYSSMMGPSPNKNFI
|
−3.6
|
0.0
|
3.0
|
FPFDKHEAH
|
−3.5
|
0.1
|
As expected, the results indicated the affinity of the three bioreceptors’ affinity DA was low in terms of the interaction energy of the DAT, which is −5.2 kcal/mol. These results were expected because the steric hindrance exhibited by a peptide within this design length range is not comparable with that of the enzyme [18]. As previously reported [18], it is important to identify amino acids capable of generating interaction with the substrate and crucial to mimic the steric volume of the target protein with which competition is desired in terms of interaction energy for the substrate of interest because this is a criterion that directly influences affinity.
Thus, two options were considered. The first was to consider the number of residues between the selected amino acids and the second was to increase the steric volume of the bioreceptors to increase their affinity [18].
Accordingly, Table 4 presents the code for each bioreceptor and its sequence when adding methylene bridges among the amino acids to preserve the distances established in the protein sequence. Finally, this Table also presents the affinity results, which are calculated as the average of the triplicate calculation along with their standard deviation. In addition, Figure 2 presents the images corresponding to each of the bioreceptors designed for this structural modification.
Table 4. Results of the bioreceptors designed maintaining the distances between the selected amino acids.
Bioreceptor code
|
Amino acid sequence with methylene bridges
|
Average affinity (kcal/mol)
|
Standard deviation
|
1.1
|
N(CH2)4W(CH2)2R
|
−3.0
|
0.0
|
2.1
|
R(CH2)Q(CH2)7KYSS(CH2)10MM(CH2)GP(CH2)SPN(CH2)K(CH2)N(CH2)5F(CH2)9I
|
−3.1
|
0.1
|
3.1
|
F(CH2)P(CH2)2F(CH2)D(CH2)5K(CH2)H(CH2)2EAH
|
−3.6
|
0.1
|
In this case, if the interaction energies of the bioreceptors composed solely of peptides are compared against the interaction energies from the bioreceptors to which the methylene bridges were added, no conclusive pattern may be observed. In some cases, the energy increased (bioreceptors 1.1 and 3.1, Table 3, second and fourth row), whereas in others, it decreased (bioreceptor 2.1). However, it was considered that because DA is a small molecule and does not have many atoms that may allow it to interact with other molecules, the amino acids are distant. Therefore, for the bioreceptor 2.1, the possible interactions that existed before adding the methylene bridges are lost. This result contradicts that in a previous report [18], but if it is considered that the substrate size in the previous study was much larger in terms of DA, it could be construed that the molecule can only generate a small number of interactions.
Because the previous results did not reflect a specific pattern, wherein one group of bioreceptors denotes better results than the other, it was decided that the following structural modifications were required in all previous designs to determine which bioreceptor and under which variables better interaction with DA occurs.
Therefore, as mentioned previously, the steric volume of the six bioreceptors already designed had to be increased to boost their affinity for DA (Tables 3 and 4). Therefore, each bioreceptor designed so far was polymerized by C and N terminal. The general structure of this process is shown in Figure 3, and the images of the bioreceptors designed are presented in Figures 5 and 6, both for the bioreceptors comprising only the peptide chain as for those with methylene bridges among the amino acids.
The following two different polymers were used for this purpose: polyethylene and polystyrene. Once designed, the interaction energy for DA was calculated for each one. The results of these dockings with polyethylene and polystyrene are presented in Tables 5 and 6 respectively, with their corresponding code and sequence. Addition of the third level.1 corresponds to the polymerization with polyethylene and of the level 2 corresponds to the polymerization with polystyrene.
Table 5. Results of designed receptors maintaining the distances between the selected amino acids.
Bioreceptor code
|
Amino acid sequence
|
Average affinity (kcal/mol)
|
Standard deviation (kcal/mol)
|
1.0.1
|
poly (ethylene)-NWR-poly (ethylene)
|
−4.0
|
0.0
|
2.0.1
|
poly (ethylene)-RQKYSSMMGPSPNKNFI-poly (ethylene)
|
−3.7
|
0.0
|
3.0.1
|
poly (ethylene)-FPFDKHEAH-poly (ethylene)
|
−3.9
|
0.0
|
1.1.1
|
poly (ethylene)-N(CH2)4W(CH2)2R-poly (ethylene)
|
−4.0
|
0.0
|
2.1.1
|
poly (etileno)-R(CH2)Q(CH2)7KYSS(CH2)10MM(CH2)GP(CH2)SPN(CH2)K(CH2)N(CH2)5F(CH2)9I-poly (ethylene)
|
−4.1
|
0.1
|
3.1.1
|
poly (ethylene)- F(CH2)P(CH2)2F(CH2)D(CH2)5K(CH2)H(CH2)2EAH- poly (ethylene)
|
−4.4
|
0.0
|
Table 6. Results of the designed receptors maintaining the distances between the chosen amino acids.
Bioreceptor code
|
Amino acid sequence
|
Average affinity (kcal/mol)
|
Standard deviation
|
1.0.2
|
poly (styrene)-NWR-poly (styrene)
|
−5.1
|
0.1
|
2.0.2
|
poly (styrene)-RQKYSSMMGPSPNKNFI-poly (styrene)
|
−4.8
|
0.0
|
3.0.2
|
poly (styrene)-FPFDKHEAH-poly (styrene)
|
−4.8
|
0.1
|
1.1.2
|
poly (styrene)- N(CH2)4W(CH2)2R-poly (styrene)
|
−4.4
|
0.1
|
2.1.2
|
poly (styrene)-R(CH2)Q(CH2)7KYSS(CH2)10MM(CH2)GP(CH2)SPN(CH2)K(CH2)N(CH2)5F(CH2)9I-poly (styrene)
|
−5.2
|
0.0
|
3.1.2
|
poly (styrene)-F(CH2)P(CH2)2F(CH2)D(CH2)5K(CH2)H(CH2)2EAH- poly (styrene)
|
−5.1
|
0.1
|
According to the results presented in Tables 5 and 6 in comparison with those in Tables 3 and 4, it is evident that affinity increases in all cases when the steric volume increases by means of polymerization. For this case particularly, there is a recognizable pattern. In addition, for polymerization with polystyrene, affinity for DA increases with respect to the results of all bioreceptors when polymerization is performed with polyethylene. On the other hand, these results follow the pattern set out previously [18], which it has been stated that recognition interaction improves when polymerization is done with polystyrene.
Considering that the affinity results are not equal or better than those of the DAT up to this point, variables other than the amino acids identified from the PDB enzymes were evaluated while considering that the bioreceptor design is targeted toward a miniaturized DAT.
Therefore, variables other than the amino acids identified from the PDB enzymes were evaluated to lower the number of amino acids that make up the bioreceptor even further.
Based on the above, we decided to study which amino acid variables could influence affinity for DA. Thus, the first criterion was stereochemistry. With the exception of glycine, amino acids have a chiral carbon, which exhibits four bonds with different functional groups [29], thus generating an enantiomer pair of spatial isomers defined as non-superimposable mirror images [30]. It is important to keep in mind that its natural stereochemistry in the human body is the L configuration and not the D configuration [29]. However, this section uses the R and S nomenclature, which applies to natural compounds and determines the stereochemistry based on the importance defined by the atomic number of the chiral carbon substituent. Using this information, amino acid stereochemistry was selected as the first variable to be analyzed for identifying its influence on the interaction with bioreceptors.
The next parameter considered was not directly focused on the nature or structure of the amino acids but on the ability of DA to form bonds or to interact with other molecules and how many of these could be formed. For this section, the authors used information taken from publications, such as the computational modeling of hDAT [28] and the models of DA receptor interaction dynamics [10]. Therefore, the second variable analyzed was the relationship between the interaction energy between bioreceptors and DA according to the amount of amino acids in the bioreceptor.
The third criterion was the nature of the amino acids, considering that they are divided into four groups. The first group includes non-polar amino acids, the second includes polar amino acids, the third includes acidic amino acids, and the fourth includes basic amino acids [31]. Thus, the next variable studied was the chemical characteristics of amino acids.
To address the first variation, the NWR bioreceptor polymerized with polystyrene (code: 1.0.2) was selected because this was the polymerization that provided the best results only with these three amino acids. Furthermore, the interaction could be only with a portion of the bioreceptors with more amino acids, which cannot be identified. This is also supported by the interactions identified between the computational model of hDAT and DA, wherein it is generally determined whether they occur with groups of two to four amino acids [28].
Based on this, all possible combinations of variations between the R and S stereochemistry were performed for the NWR tripeptide by polymerizing with polystyrene. Figure 6 presents the flat structure of this bioreceptor, wherein each amino acid whose stereochemistry will be modified is identified with a different color.
Table 7 denotes the codes for these bioreceptors, the stereochemistry of each amino acid in the respective order, and the average triplicate calculation of the interaction energy in kcal/mol; Figure 7 presents the images related to each one.
Table 7. Results of bioreceptor 1.0.2 stereochemistry variation.
Bioreceptor code
|
Stereochemistry
|
Average affinity (kcal/mol)
|
Standard deviation (kcal/mol)
|
1.0.2
|
SSS
|
−5.1
|
0.1
|
1.0.2.2
|
RRS
|
−4.5
|
0.0
|
1.0.2.3
|
RSR
|
−4.9
|
0.0
|
1.0.2.4
|
RSS
|
−5.0
|
0.1
|
1.0.2.5
|
SRS
|
−4.8
|
0.0
|
1.0.2.6
|
SSR
|
−4.9
|
0.1
|
1.0.2.7
|
RRR
|
−5.0
|
0.0
|
As seen in Table 7, there are variations in the affinity results for each bioreceptor according to variations in the stereochemistry of the amino acids that compose them. Hence, it was determined that the bioreceptor that best interacts with DA is the one with SSS stereochemistry, which corresponds to code 1.0.2, is in bold text in Table 7. Although the other results were not considerably distant, this result was obtained because this is the natural stereochemistry of amino acids, and therefore, the other results exhibited decreased affinity. It is worth mentioning that the bioreceptor with SSS stereochemistry is the same one that was designed by polymerization with polystyrene, which is why the code did not change. The standard deviation of the data is generally reduced to one decimal or even becomes null in some cases, which means that the data dispersion is not very variable.
However, once we defined that we wanted to maintain the natural amino acid stereochemistry in the bioreceptor design, we proceeded to determine how many amino acids they should have.
Based on the hDAT model described [28] and the analysis of the number of interactions that DA can form, the influence of the number of amino acids was evaluated only with three styrene-polymerized bioreceptors according to the previous results. The design of these bioreceptors was based on glycines so that only the interaction of DA with the amount of peptide bonds could be assessed with no influence of the functional groups that compose the substituents of the other amino acids. The amount of amino acids varied from two to four glycine molecules, as shown in Figure 8.
Considering this, Table 8 indicates the affinity results for the three glycine bioreceptors, specifying the amount of glycine they contain through their code and the interaction energy in kcal/mol. Figure 9 also presents the images related to each bioreceptor corresponding to this variable.
Table 8. Results of the receptors varying the amount of glycine that composes them.
Bioreceptor code
|
Peptide
|
Average affinity (kcal/mol)
|
Standard deviation
|
4.0
|
GG
|
−4.9
|
0.0
|
4.1
|
GGG
|
−5.1
|
0.0
|
4.2
|
GGGG
|
−4.8
|
0.0
|
As shown in Table 8, the best results were obtained with the bioreceptor comprising three glycine molecules (4.1, in bold text in Table 8) with a value of −5.1 kcal/mol, which was the parameter used to build the following bioreceptors to study the relationship of bioreceptor affinity for DA according to the amino acids that compose it.
Based on this, the bioreceptors configured by tripeptides were designed according to their nature. A total of seven bioreceptors were modeled while studying this characteristic, comprising glycine, phenylalanine, alanine, asparagine, serine, cysteine, and histidine. The general structure for this group of bioreceptors is shown in Figure 10.
Regarding the previous results, as mentioned, the glycine was tested to determine the importance of the presence or absence of the substituent in the amino acids that made up the bioreceptor or if the peptide bonds alone could generate enough affinity for DA. This result is presented in the first row of Table 12 and is the same one presented in Table 8 under code 4.1.
Phenylalanine and alanine were used as standards for the group of non-polar amino acids to simultaneously compare the influence of the amino acid with an aromatic substituent, which made it possible to analyze the π-π interaction that can occur between the amino acids themselves or with DA. We identified that this interaction can occur with phenylalanine [32]. Therefore, the bioreceptor results with phenylalanine can be compared with those that are formed only by alanine, which are also non-polar, but with an aliphatic and single-carbon substituent. This allows the result to be related to the type of interaction that can be formed and to the steric volume of the amino acid.
The results of the average affinity of the triplicate calculation of the bioreceptor comprising the phenylalanine tripeptide is reported in Table 9 on line two under code 5. The alanine bioreceptor corresponds to code 6 and is reported in the third row of the same Table; these bioreceptors correspond to images B and C in Figure 11.
Regarding the bioreceptors designed from asparagine (code 7), serine (code 8), and cysteine (code 9), the group of polar amino acids has been addressed. However, there are differences in the substituents of these three amino acids, which were considered during their selection.
Asparagine is an amino acid that, in addition to being polar, has the amide functional group (RCONH2) in its substituent and has the capacity to accept three and donate two hydrogen bonds [33]. On the other hand, serine has a hydroxyl group in its substituent and can donate three and accept four hydrogen bonds [34]. Cysteine is a thiol [35]. The results of these three bioreceptors are reported in rows four, five, and six, respectively, of Table 9.
Histidine is a basic amino acid because of the chain in its substituent. It was selected owing to its high reactivity and because it is an amino acid that plays an important role in the catalytic activity of proteins [36]. The bioreceptor designed based on histidine was assigned code 10 and the result of interaction energy with the DA is reported in row seven of Table 9.
Figure 11 presents the images corresponding to each of these seven bioreceptors designed to assess the importance of the nature of their composing amino acid. Each one is identified by its code, as specified in Table 9.
Table 9. Results of the receptors composed of amino acids of different chemical nature.
Bioreceptor code
|
Tripeptide
|
Average affinity (kcal/mol)
|
Standard deviation (kcal/mol)
|
4.1
|
GGG
|
−5.1
|
0.0
|
5
|
FFF
|
−5.0
|
0.0
|
6
|
AAA
|
−4.3
|
0.0
|
7
|
NNN
|
−4.5
|
0.0
|
8
|
CCC
|
−4.8
|
0.0
|
9
|
SSS
|
−4.9
|
0.0
|
10
|
HHH
|
−4.8
|
0.1
|
As denoted in Table 9, the results for this series of amino acids range from −4.3 to −5.1 kcal/mol, wherein only one of the bioreceptors has a standard deviation other than zero, which means that there was no variability between them and that in the case of bioreceptor 10, data dispersion decreased. Bioreceptor 6, which comprises a tripeptide of alanine, exhibited a more distant result than the others, as shown in row four of Table 9. This may mean that the alanine substituent (CH3) did not generate a significant affinity with DA, and this result is comparable with that of phenylalanine (row 3 of Table 9), which is also non-polar and provides better results. Therefore, these π–π interactions are stronger than those formed by alanine, as mentioned in the amino acid selection criteria.
The next lowest result found was −4.5 kcal/mol, corresponding to bioreceptor 7, recorded in row five of Table 9. Here, the substituent was an amide did not exhibit any result despite being able to donate two and receive three hydrogen bonds. This may be because these protons possess a very weak acidic character; therefore, in contrast, hydrogen bond interactions may be unlikely, for example, with protons of aspartic acid [37]. These results are comparable with those of serine and cysteine bioreceptors which are also weak despite having acidic protons.
Because with the structural modifications made and the assessment of the three variables above failed to achieve a bioreceptor with better affinity results, it was considered that mixtures between the different amino acids will potentiate the results, specially because it had been observed that the expected results were not obtained for bioreceptors comprising only one amino acid. Then, to combine these amino acids, we decided to use the groups of amino acids reported as exhibiting interaction in the models of hDAT and the group of DA receptors. These groups were taken from the previous studies [10,28].
Before analyzing the results obtained for this group of bioreceptors, it was necessary to highlight that it had already been determined that the amount of amino acids should not exceed four to ensure that interactions with DA were specifically occurring with the amino acids of interest. The existence of aromatic amino acids showed an increase in the interaction, and to add steric volume, the polymerization had to be performed with polystyrene.
Consequently, after testing the variables above, 13 additional bioreceptors were designed which correspond to the amino acid groups identified in the computational models both of the hDAT [28] and the interaction mechanism of DA receptors [10].
Overall, it has been reported that both intra and extracellular DA interactions with DATs and DA receptors involve groups of amino acids, with the number ranging from two to four. In fact, a study argues that aspartic acid is a very important amino acid and essential for the DA reuptake process. In addition, several aromatic interactions were also identified as playing a prominent role in the activity of the protein with DA [28].
In total, 14 groups of amino acids were identified. They were polymerized by the C and N terminal with polystyrene to give them steric volume, a characteristic that had already been proven by increasing bioreceptor interaction. Table 10 displays the code assigned to each bioreceptor, the peptide for which it was composed, and the average affinity result of the triplicate calculation.
Table 10. Results of the bioreceptors polymerized with polystyrene and designed from the molecular interactions of hDAT and dopamine receptors with DA, determined from computational models.
Bioreceptor code
|
Peptide
|
Average affinity (kcal/mol)
|
Standard deviation (kcal/mol)
|
11
|
LS
|
−4.5
|
0.1
|
12
|
RD
|
−4.7
|
0.0
|
13
|
RDYF
|
−5.1
|
0.0
|
14
|
SD
|
−5.1
|
0.0
|
15
|
SDW
|
−5.3
|
0.1
|
16
|
WFF
|
−4.6
|
0.0
|
17
|
WFFH
|
−4.6
|
0.1
|
18
|
WFFN
|
−5.1
|
0.0
|
19
|
WFT
|
−5.4
|
0.0
|
20
|
WH
|
−4.5
|
0.1
|
21
|
WHF
|
−4.5
|
0.1
|
22
|
YDN
|
−5.1
|
0.0
|
23
|
YF
|
−5.0
|
0.0
|
When observing the results obtained for the 14 bioreceptors presented in Figure 12 the affinity range obtained was determined to be ranging from −4.5 to −5.4 kcal/mol, with null or 0.1 standard deviations, indicating that there was no significant data dispersion. We were able to obtain bioreceptors, the energy of which exceeded the interaction energy of the DAT at −5.2 kcal/mol. The two bioreceptors that improved interaction with DA were 15 and 19, which are in bold text in rows five and ten of Table 10.
First, bioreceptor 15, denoted in row five of the previous table and image D in Figure 12, will be discussed. This bioreceptor comprises serine, aspartic acid, and tryptophan. As per the above-mentioned findings, serine is a polar amino acid because of its hydroxyl group, which has been described as playing a prominent role in the catalytic activity of enzymes [34]. Conversely, aspartic acid was not addressed or considered in the tests; therefore, it is essential to emphasize its characteristics. This amino acid is acidic and can donate three and accept five hydrogen bonds; therefore, it could be said that it is the amino acid with the highest number of interactions so far [31,38]. The final one is tryptophan, which is part of the group of non-polar and aromatic amino acids. Its substituent has the indole functional group and can donate and accept three hydrogen bonds [39].
This bioreceptor reported an interaction energy of –5.3 kcal/mol. Its corresponding image shows how the amino acids were exposed and the polymer provided steric volume leaving a free pocket for interaction with DA.
Bioreceptor 19 corresponds to image I in Figure 12. With this design, we obtained an affinity of –5.4 kcal/mol, and its composing amino acids are tryptophan, phenylalanine, and tyrosine. Of these amino acids, tyrosine, which is an aromatic and polar amino acid, capable of donating three and accepting four hydrogen bonds, was not analyzed [40]. It is one of the amino acids found at the highest percentage of protein composition and has the phenol functional group in its substituent [41].
As described previously, this bioreceptor contains a tripeptide of aromatic amino acids, which reaffirms the finding that the π–π interaction is essential for DA recognition. However, it is evident that the aromatic group is not strong enough to interact with DA alone, but when supplemented by the hydroxyl group in the tyrosine ring and the benzofused substituent of tryptophan, they interact together to release more energy. The image depicting this bioreceptor also shows that the peptide bonds form a curve that exposes the amino acid substituents so that they can interact with DA.
This applies to the bioreceptors that exceeded the affinity of the DAT. However, it is evident that there are four more bioreceptors that approach this affinity, presenting a value of –5.1 kcal/mol. Therefore, the affinity value while assessing these cases was considered and so were the difference between the first interaction identified by the AutoDock Vina software as well as the difference between the upper and lower quadratic distances of the different configurations tested by the software to yield the results.
This was performed because the interactions are more likely to form when the difference between the first and the second values is not greater than 2 Å [42]. Thus, the first two values obtained on performing molecular docking with bioreceptors 13, 14, 18, and 22 are denoted in Figure 13.
This is the result for the four bioreceptors being analyzed. As mentioned above, bioreceptor 14 is the only one in which one of the two values, between the upper or lower limit, of the quadratic root of the average atomic distances deviations in the interaction is <2 Å. This bioreceptor corresponds to image (b) in the figure. Therefore, this bioreceptor may exhibit a good interaction with DA beyond the energy value recorded.
Thus, there are several bioreceptors that may offer better recognition characteristics for DA than DAT.
2.3. Analysis of candidate bioreceptors
Based on the results, the study continued with two candidates that exceeded the affinity parameter and an additional one with promising characteristics owing to the differences in the upper and lower root-mean-square deviation (RMSD) values. These three bioreceptors were further assessed to determine the distances used for the interaction in the docking process and to compare them against the distance of the DAT.
Another criterion used to assess the docking result was through a graphical interface that displays the calculated interaction model [43]. This visualization was conducted using the PyMOL software for both the DAT and the three selected bioreceptors.
Figure 14 presents the possible interactions through hydrogen bonds (yellow dotted lines) that were simulated to calculate the affinity of the DAT for DA. The distances of these hydrogen bonds are shown in the image. Only four possible interactions were evaluated because the hydroxyl groups in the ring were closer to the protein. The results yielded two of 2.8 Å, one of 2.7 Å, and one of 2.4 Å.
With these criteria, we determined whether the distances of the selected bioreceptors were similar to those of the DAT. The images corresponding to the results of the docking for bioreceptors 14, 15, and 19 are denoted in Figure 15.
As presented in Figure 14, the measured length values of the interactions created between DA and each of the bioreceptors can be observed. These results are further summarized in Table 11, wherein bioreceptors 19 and 15 exhibit distances greater than the distances reported for the hydrogen bonds formed between the DAT and DA, although they have better interaction energy in terms of affinity. Conversely, for bioreceptor 19, the distance of one of the hydrogen bonds between the bioreceptor and DA is 2.3 Å. Therefore, the distance is decreased by one tenth when compared against the shortest bond that can be formed with the DAT according to the simulations. However, this bioreceptor has no affinities higher than the DA reuptake.
Table 11. Results of the distances of the hydrogen bonds for the bioreceptors analyzed.
Bioreceptor code
|
Hydrogen bond length (Å)
|
19
|
3.3
|
15
|
3.7
|
14
|
2.3
|
Based on these results, three bioreceptors designed were candidates to be synthesized for in vitro tests for selectivity, stability to be a functional part of a biosensor, and affinity for DA.
However, it was decided to evaluate in silico the affinity of the bioreceptors for the catecholamines epinephrine and norepinephrine, because the structural similarity of the catecholamines could interfere with the recognition and subsequent quantification of dopamine (table 12).
Table 12. Evaluation of the affinity of bioreceptors against catecholamines.
|
Dopamine
|
Norepinephrine
|
Epinephrine
|
|
Catecholamines
Bioreceptor
|
|
|
|
|
SD
|
-5,1
|
-4,6
|
-4,7
|
Binding affinity (Kcal/mol)
|
SDW
|
-5,3
|
-4,9
|
-5,2
|
WFT
|
-5,4
|
-4,6
|
-4,8
|
Based on the binding affinity obtained, it is clearly evident that bioreceptors have a greater affinity for dopamine than for the other catecholamines. Additionally, the WFT bioreceptor has a slightly larger difference with respect to norepinephrine and epinephrine, which suggests that it may have greater selectivity for dopamine
In brief, two of the bioreceptors report better DAT interaction energies, and both comprise three amino acids and are polymerized by the C and N terminal with polystyrene. The third bioreceptor has shorter distances for interaction with DA although the energy interaction is –0.1 kcal/mol weaker than the energy reported for the DAT.