European Journal of Medicinal Chemistry 84 (2014) 100e106
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European Journal of Medicinal Chemistry journal homepage: http://www.elsevier.com/locate/ejmech
Original article
CoMFA and CoMSIA analysis of ACE-inhibitory, antimicrobial and bitter-tasting peptides Shufen Wu a, Wei Qi a, b, c, d, *, Rongxin Su a, b, c, d, Tonghe Li a, Dan Lu a, Zhimin He a, b, d a
Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, PR China State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, PR China c Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin University, Tianjin 300072, PR China d The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, PR China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 November 2013 Received in revised form 21 June 2014 Accepted 5 July 2014 Available online 7 July 2014
Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to the ACE-inhibitory, antimicrobial, and bitter-tasting peptides. Predictive 3DQSAR models were established using SYBYL multifit molecular alignment rule over a training set and a test set. The optimum models were all statistically significant with cross-validated coefficients (Q2) >0.5 and conventional coefficients (R2) >0.9, indicating that they were reliable enough for activity prediction. The obtained results may aid in the design of novel bioactive peptides. © 2014 Elsevier Masson SAS. All rights reserved.
Keywords: ACE inhibition Antimicrobial Bitterness CoMFA CoMSIA
1. Introduction Considerable attention has been paid to bioactive peptides derived from food proteins and much work regarding those peptides is currently underway, mainly focusing on their release via selectively enzymatic hydrolysis. Bioactive peptides have been described to promote different health effects [1e3], mainly including antimicrobial properties, blood pressure-lowering (ACE inhibitory) effects, antithrombotic and antioxidant activities. Despite the fact that an increasing number of new peptides are continuously reported in literatures, the rationale by which these peptides play the relevant physiological roles has not been well elucidated. Moreover, the conventional activity-based purification steps often take months to generate a new sequence, which largely limit their throughput. At the same time, some peptides with strong bitter taste associated with most food protein hydrolyzates have been identified [4], which is a negative aspect. Thus, how to decrease or eliminate unwanted taste is very important, and numerous options have been investigated for hydrolyzate debittering [5]. In order to identify novel peptides, as well as prepare and
debitterize more efficiently, there is a need to explore an alternative and more theoretical approach. Three-dimensional quantitative structureeactivity relationship (3D-QSAR) method is helpful to extract a correlation between biological activity and chemical structure [6]; especially, the CoMFA and CoMSIA are particularly effective methods based on statistical techniques [7e9]. These two models sample the potential fields surrounding a set of ligands and construct 3D-QSAR models by correlating these 3D fields with the corresponding experimental activities of ligands interacting with a common target receptor. Although several peptide QSAR models have been reported to describe the structureeactivity relationships of antimicrobial, ACEinhibitory, and bitter-tasting peptides [10], 3D-QSAReCoMFA/CoMSIA models for these compounds, to our knowledge, have not been derived simultaneously in the literature. Therefore, the present study aims to develop predictive models and evaluate what structural features (hydrogen bond donor/acceptor, hydrophobic, steric and electrostatic) are responsible for these three bioactivity peptides. 2. Materials and methods 2.1. Preparation of data set
* Corresponding author. The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, PR China. E-mail address: [email protected] (W. Qi). http://dx.doi.org/10.1016/j.ejmech.2014.07.015 0223-5234/© 2014 Elsevier Masson SAS. All rights reserved.
A total of 113 ACE-inhibitory peptides, which were classified into three data sets according to peptide length, i.e. di-peptides, tri-
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Table 1 ACE inhibitory peptides used for 3D-QSAR analysis. C#a
Sequence
Dipeptides 1 YP 2 FP 3 RP 4 GP 5 TP 6 VP 7 KP 8 IP 9 DY 10 RY 11 AY 12 HY 13 SY 14 NY 15 VY 16 KY 17 MF 18 RF 19 SF 20 GF 21 NF 22 DF 25 AW 26 LW 27 GW 28 VW 29 MW 30 RW 31 IW c 32 AP c 33 IY c 34 LY 35c GY 36c FY 37c TF 38c AF c 39 VF c 40 KF a b c
Log IC50b
C#
Observed
Predicted (error)
2.86 2.50 1.32 2.56 2.46 2.62 1.34 2.11 2.00 1.71 1.28 1.42 1.82 1.51 1.20 0.89 1.65 1.97 2.11 2.44 1.67 2.56 1.08 1.24 1.40 0.20 1.00 1.20 0.67 2.43 0.79 1.51 2.32 1.40 1.95 1.88 0.96 1.45
2.69(0.17) 2.62(0.12) 1.54(0.22) 2.65(0.09) 2.52(0.06) 2.39(0.23) 1.32(0.02) 2.29(0.18) 2.01(0.01) 1.54(0.17) 1.56(0.28) 1.39(0.03) 1.78(0.04) 1.39(0.12) 1.20(0.00) 0.87(0.02) 1.79(0.14) 2.06(0.09) 2.10(0.01) 2.30(0.14) 1.83(0.16) 2.47(0.09) 1.10(0.02) 0.94(0.30) 1.37(0.03) 0.57(0.37) 1.06(0.06) 1.18(0.02) 0.56(0.11) 2.49(0.06) 1.05(0.26) 1.43(0.08) 1.91(0.41) 1.30(0.10) 1.99(0.04) 1.97(0.09) 1.42(0.46) 1.21(0.24)
Sequence
Tripeptides 41 FEP 42 LKP 43 ALP 44 LRP 45 IEP 46 LAP 47 GRP 48 LSP 49 IAP 50 MNP 51 LEP 52 TNP 53 VSP 54 VLP 55 ILP 56 LNP 57 VGP 58 GKP 59 VYP 60 IKP 61 FAP 62 AVP 65 VRP 66c LYP c 67 DLP 68c IRP 69c FQP 70c LQP 71c GEP c 72 VMP Tetra-, 73 74 75 76 77 78
Log IC50
C#
Observed
Predicted (error)
1.08 0.60 2.38 0.57 0.20 0.54 1.30 0.23 0.43 1.82 0.28 2.32 1.00 1.91 1.51 1.76 1.42 2.55 2.46 0.84 0.58 2.53 0.34 0.82 0.68 0.13 1.08 0.28 2.51 1.46
1.05(0.03) 0.61(0.01) 2.35(0.03) 0.56(0.01) 0.35(0.15) 0.61(0.07) 1.27(0.03) 0.22(0.01) 0.43(0.00) 1.85(0.03) 0.30(0.02) 2.26(0.06) 1.07(0.07) 1.98(0.07) 1.51(0.00) 1.86(0.10) 1.38(0.04) 2.59(0.04) 2.38(0.08) 0.83(0.01) 0.62(0.04) 2.49(0.04) 0.32(0.02) 0.21(0.61) 1.18(0.50) 0.13(0.00) 0.84(0.24) 0.80(0.52) 2.16(0.35) 1.98(0.52)
penta- and hexa-peptides GWAP 0.59 FVAP 1.00 AVGP 0.49 SPYP 2.93 VSSP 1.00 VVPP 2.45
0.33(0.26) 0.94(0.06) 0.70(0.21) 2.78(0.15) 1.10(0.10) 2.24(0.21)
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95c 96c 97c 98c 99c 100 101 102 103 104 105 106 107 108 109 110 111 112c 113c
Sequence
LVPP ILPP PKHP SKVPP APVPP PVPQP GRVMP PQPIP TKVIP VHLPP VHLAP IAYKP LALPP VLPYP HQIYP VLIVP HLPLP FFVAP AVPYP VLPIP DKIHP DYGLYP VRGPFP VIEKYP KVLPVP PVVVPP RPQIPP NIFYCP LHLPLP LHLPAP LHLPYP LHLPGP LHLYLP LHLWLP LAYFYP
Log IC50 Observed
Predicted (error)
2.10 2.24 2.85 1.87 2.80 2.04 2.40 2.53 2.60 1.26 0.65 0.34 2.90 1.56 2.04 2.23 1.61 0.78 1.90 1.49 2.05 1.79 2.77 2.24 0.70 2.07 1.66 1.18 0.57 0.78 0.36 0.28 1.28 0.95 1.81
2.12(0.02) 2.40(0.16) 2.74(0.11) 1.82(0.05) 2.82(0.02) 1.97(0.07) 2.45(0.05) 2.46(0.07) 2.67(0.07) 1.23(0.03) 0.81(0.16) 0.31(0.03) 2.92(0.02) 1.51(0.05) 2.05(0.01) 2.14(0.09) 1.40(0.21) 1.20(0.42) 1.90(0.00) 1.76(0.27) 1.85(0.20) 1.76(0.03) 2.77(0.00) 2.39(0.15) 0.74(0.04) 2.26(0.19) 1.66(0.00) 0.94(0.24) 0.70(0.13) 0.45(0.33) 0.64(0.28) 0.36(0.08) 1.26(0.02) 1.05(0.10) 1.47(0.34)
C# ¼ Compounds. IC50 expressed as mM. Test set peptides.
peptides as well as tetra-, penta- and hexa-peptides, were selected from published literatures [11e20]. Each peptide was listed in Table 1 with a reported in vitro IC50 (the peptide concentration required to produce 50% inhibition of ACE) value that was less than 1 mM. Prior to model training, the IC50 values of all ACE-inhibitory peptides in units of micromolarity (mM) were transformed into log IC50. Twenty-four antimicrobial peptides with their relative IC50 (Rel IC50, the ratio of the IC50 for the experimental peptide to the IC50 of Bac2A with sequence RLARIVVIRVAR) values selected for the present analysis were taken from the published literature [21]. In order to facilitate the modeling, the Rel IC50 values were converted into log (106/Rel IC50). Moreover, twenty-one bitter peptides were collected as a database from the previous report [13]. During the following 3DQSAR analysis, the bitterness values determined by sensory evaluations were expressed as log 1/T, where T was the bitter threshold concentration (M). The fifty-four peptides as well as their biological activities were shown in Table 2. The selection of training set was based on the distribution of biological data and structural diversity. For example, the log IC50 values of dipeptides span a rang of 0.2e2.86 in Table 1, which providing a broad data set for the 3D-QSAR analysis; moreover, the data set of bitter peptides in Table 2 consisted of eight groups by
length, i.e. tri-, tetra-, penta-, hexa-, hepta-, octa-, deca- and dodeca-peptides, to ensure the 3D-QSAR model extendable. Testing set peptides marked with c and e in Tables 1 and 2, were selected manually to evaluate the predictive power of the resulting models. 2.2. Molecular modeling and alignment The 3D-QSAR study was performed on SYBYL 6.92 (Tripos, Inc, St. Louis, USA). The 3D structures of all molecules were constructed using the Build Protein function. Partial atomic charges of all compounds were calculated by the GasteigereMarsili method, and then were optimized for their geometry using Tripos field [22] with a distance-dependent dielectric function and energy convergence criterion of 0.005 kcal/mol Å using the maximum iterations set to 1000 [23]. For the molecule alignment, the most active peptide of each data set was chosen as the template. All peptides were aligned on the basis of the common structure, and the final superimpositions were displayed in Fig. 1. 2.3. CoMFA and CoMSIA analysis To derive the CoMFA and CoMSIA descriptor fields, the aligned training set molecules were placed in a 3D cubic lattice with grid spacing of 2Å in x, y, and z directions such that the entire set was
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included in it. The CoMFA steric and electrostatic field energies were calculated using a sp3 carbon probe atom with a van der Waals radius of 1.52 Å and a charge of þ1.0. Cut-off values for both steric and electrostatic fields were set to 30.0 kcal/mol. For CoMSIA analysis, the standard settings [probe with charge þ1, radius 1Å, hydrophobicity þ1.0, hydrogen-bond donating þ1.0, hydrogen bond accepting þ1.0 [9]] were used to calculate five different fields: steric, electrostatic, hydrophobic, acceptor and donor. Gaussiantype distance dependence was used to measure the relative attenuation of the field position of each atom in the lattice, and led to much smoother sampling of the fields around the molecules when compared to CoMFA. The default value of 0.3 was set for attenuation factor a. For CoMFA and CoMSIA analysis, two or five descriptor fields are not totally independent of each other [24,25]. Therefore, we systematically altered the combination of fields and chose the best model. 2.4. Regression analysis by PLS method The partial least squares (PLS) methodology analysis with the leave-one-out (LOO) cross-validation procedure was carried out to determine the optimal number of components using the SAMPLS [26]. The cross-validated coefficient Q2, as an internal statistical index of predictive power, was subsequently obtained. In order to test the real predictive ability of the best model derived by the training set, biological activities of the test set were predicted. The quality of the external prediction was documented using the standard deviation of error prediction (R2). Q2 and R2 are calculated according to the formula [27]:
P 2
Q ¼1 P P R2 ¼ 1 P
Yobs YCVpred
2
ðYobs Ymean Þ2 Yobs Ypred
2
ðYobs Ymean Þ2
where Ymean means average activity value of the entire data set, while Yobs, Ypred and YCVpred represent observed, predicted and cross-validated activity values, respectively. Often, a high Q2 and R2 value (Q2 > 0.5, R2 > 0.6) is considered as a proof of high predictive ability of the model [28]. The CoMFA/CoMSIA results were graphically interpreted by field contribution maps using field type “stdev*coeff”, and the contour levels were set to default values. 3. Results The most intricate and subjective part of CoMFA/CoMSIA studies is molecular alignment. In general, geometric similarity should exist between the modeled structure and the bioactive conformation for 3D-QSAR. Thus, in this work, peptides with the highest activity in Tables [peptide 28 with log IC50 ¼ 0.20 (Table 1), peptide 44 with log IC50 ¼ 0.57 (Table 1); peptide 6 with pIC50 ¼ 6.89 (Table 2); peptide 39 with log 1/T ¼ 5.70 (Table 2)] of each data set were used as the templates; additionally, it was necessary to point out that for tetra/penta/hexapeptides, peptide 105 with log IC50 ¼ 1.66 (Table 1) was chosen as the template, because tetra-, penta- and hexa-peptides could be all aligned on the common structure and peptide 105 was with the sequence IPP (the famous ACE-inhibitory tripeptide) at the C-terminal. As stated before, since the five different descriptor fields may not be totally independent, all 31 possible
combinations of the descriptors for each group (dipeptides, tripeptides, tetra/penta/hexa-peptides, antimicrobial peptides and bitter peptides) were attempted to build the optimum model, and the evaluation parameters were calculated for every model (see Supplementary data Table S1eS5). For analysis, we focused on the best model for each data set, and the statistical parameters were displayed in Table 3. 3.1. ACE-inhibitory peptides As shown in Table 3, for ACE-inhibitory peptides, the best predictions were obtained by the 3D-QSAR/CoMSIA, 3D-QSAR/ CoMFA and 3D-QSAR/CoMFA for dipeptides, tripeptides, tetraypentayhexa-peptides, respectively. Each model had a high R2 (0.952, 0.995 and 0.975, respectively) with a low standard deviation (S, 0.172, 0.084 and 0.145, respectively) and a high Fischer ratio (F, 78.8, 233.8 and 293.5, respectively); while a QSAR model is generally acceptable if R2 is approximately 0.9 or higher [29]; furthermore, the steric and electrostatic fields among the five ones (steric, electrostatic, hydrophobic, acceptor and donor fields) contributed prominently and thus detailed analyses were focused on these two fields. The plots of the predicted versus actual log IC50 for the best QSAR models were displayed in Fig. 2(aec). It could be noted that the data points were uniformly distributed along the regression line; additionally, as shown in Table 1, all the prediction errors were smaller than 0.7, suggesting the satisfactorily predictive capability, high reliability and accuracy of the models. To view the field effect on the target property, CoMFA and CoMSIA contour maps were generated and shown in Fig. 3(aec). For steric fields, the green (in the web version) (bulky group favorable) and yellow (in the web version) (bulky group unfavorable) contours represented 80% and 20% contributions, respectively. Similarly, the blue (in the web version) (electropositive charge favorable) and red (in the web version) (electronegative charge favorable) contours in the electrostatic field represented 80% and 20% contributions, respectively. 3.2. Antimicrobial peptides The 3D-QSAReCoMFA/CoMSIA models were used to predict the antimicrobial activity of 24 nonapeptides against Pseudomonas aeruginosa [21]. PLS analysis performed with three principal components led to good statistical parameters (Table 3), and the optimal CoMFA model was established. Based on this model, the correlation between experimental and predicted antimicrobial activity of all peptides was presented in Fig. 2(d). From Table 2, the lowest prediction errors were obtained for peptide 17 (0.00) and peptide 4 (0.01); while the worst predictions were obtained for peptide 19 (0.62) and peptide 21 (0.55). These results suggested that the latter two peptides were not suitable for being templates to design new antimicrobial peptides. In general, most date points were uniformly dispersed along a line with a slope coefficient of 1.0 (Fig. 2), which indicating that no systematic error existed in the method. 3.3. Bitter peptides The best CoMFA model for bitter peptides was constructed using a series of 21 peptides with a training set of 17 peptides and a test set of 4 peptides as shown in Table 2. In Fig. 2(e), a strong linear relation was displayed between the predicted and actual log 1/T values, and most samples were rather uniformly dispersed around the regression line except the compound 43 (0.60), indicating the satisfactorily predictive capability of the model.
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Table 2 Antimicrobial and Bitter peptides used for 3D-QSAR analysis. Antimicrobial peptides
Bitter peptides
C#a
Sequence
Rel IC50b
pIC50c Observed
Predicted (error)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18e 19e 20e 21e 22e 23e 24e
RWRRKWWWW RIKRWWWWR KKRWLWWWR RWWRKWWIR RRRWWWWWW KRWWKWWRR RRIWRWWWW IRRRKWWWW KRKIWWWIR WRWWKWWRR TRKVWWWRW RHWKTWWKR RKRLKWWIY RWILWWWRI IRRRWWWIV WHGVRWWKW RRKRWWWWW WKRWKWWKR KWWKIWWKR RIKVIWWWR KRRRIWWWK LRFILWWKR KRWWWWWKR RKWWRWWRW
0.39 0.31 0.30 0.24 1.82 0.13 0.68 0.21 0.28 0.23 0.76 0.95 0.18 25 0.23 2.5 0.43 0.25 0.20 0.51 0.40 0.88 0.22 0.31
6.41 6.51 6.52 6.62 5.74 6.89 6.17 6.68 6.55 6.64 6.12 6.02 6.74 4.60 6.64 5.60 6.37 6.60 6.70 6.29 6.40 6.06 6.66 6.51
6.36(0.05) 6.38(0.13) 6.48(0.04) 6.63(0.01) 5.90(0.16) 6.81(0.07) 6.07(0.10) 6.95(0.27) 6.60(0.05) 6.74(0.10) 6.06(0.06) 5.99(0.03) 6.69(0.06) 4.69(0.09) 6.53(0.11) 5.57(0.03) 6.37(0.00) 7.01(0.41) 6.08(0.62) 6.00(0.29) 6.95(0.55) 6.16(0.10) 6.18(0.48) 6.24(0.27)
a b c d e
C#
Sequence
Log 1/Td Observed
Predicted (error)
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42e 43e 44e 45e
GFF GGFF GPFF RGFF RPFF RPGFF RRPFF RPGGFF RGPPFF RRPPFF RGPPGFF RPPPFFF RGPPFFF RPFFRPFF RRPPPFFF FFRPFFRPFF RPFFRPFFRPFF FFF GGRPFF RGPPGGFF RGPPGGGFF
3.23 2.85 3.80 3.80 4.40 3.51 4.70 4.04 4.23 5.15 4.40 4.70 5.00 5.00 5.70 5.15 5.00 3.70 4.04 4.11 3.95
3.16(0.07) 3.06(0.21) 3.64(0.16) 3.60(0.20) 4.37(0.03) 3.53(0.02) 4.82(0.12) 4.14(0.10) 4.26(0.03) 5.14(0.01) 4.52(0.12) 4.72(0.02) 4.95(0.05) 5.04(0.04) 5.67(0.03) 5.02(0.13) 5.03(0.03) 3.43(0.27) 4.29(0.25) 4.04(0.07) 4.23(0.28)
C# ¼ Compounds. Rel IC50, relative IC50, defined as the ratio of the IC50 for the experimental peptide to the IC50 of the control peptide Bac2A with sequence RLARIVVIRVAR. pIC50 ¼ log(106/Rel IC50). T was the bitter threshold concentration in unit of molarity (M). Test set peptides.
4. Discussion 4.1. ACE-inhibitory peptides In the CoMSIA model for dipeptides based on peptide 28, the distribution of steric field (0.553) was little higher than that of electrostatic field (0.447). As seen in Fig. 3 (a-left), two green (in the web version) contours near N-terminal suggested steric bulky substituents were favored there; a small yellow (in the web version) contour around C-terminal represented the disfavor of bulky group at this region. It can be confirmed by the fact that the activities of dipeptides 31, 25 and 27 decreased accompanied by the less of their R-substituents volume at the N-terminal. The same situation occurred to two sets of dipeptides 8, 32 and 4 as well as 33, 11 and 35. In Fig. 3 (a-right), positively charged favorable blue (in the web version) regions were found around the N-terminal, while negatively charged favorable red (in the web version) regions were observed near the C-terminal, which could indicate nothing for its malposition. By comparing dipeptides 10,12,16 and 35, it could be found that the positive charge on the guanidino or ε-amino group of C-terminal Arg and the imidazole of His as well as Lys side-chains, resulted in high inhibitory potency [30]. Meanwhile, other researches have suggested that amino acids with positively charged functional groups at N-terminal are potential inhibitors of ACE [30e33]. Although this CoMFA model could not reflect the conformational behavior of ACE-inhibitory dipeptides sufficiently, it confirmed dipeptides with Trp, Tyr and Phe at Cterminal would exhibit high activity [32]. For tripeptides, the optimal CoMFA model validated internally yields Q2 ¼ 0.848. The steric contour map [Fig. 3 (b-left)] was characterized by a medium green (in the web version) contour upon the N-terminal and a small contour around the C-terminal, indicating that compounds taking Ile or Leu as the first amide group
were more active than those with Gly. Another yellow (in the web version) contour near the second amide group was hard to be confirmed because of its malposition. Fig. 3 (b-right) displayed a medium blue (in the web version) contour upon the second residue with no obvious red (in the web version) ones mapped. Thus it is suggested that amino acids with positive charge are preferred for the middle residue of the tripeptides, which is consistent with the previous study [11]. For tetra-, penta- and hexa-peptides, a CoMFA model was constructed with a highest F value of 293.5. In the CoMFA contour maps [Fig. 3(c)], the green (in the web version) and yellow (in the web version) contours appeared near the third and fifth residues from the C-terminal, respectively, which was consistent with the point that the tetrapeptide instead of tripeptide residues from C-terminal largely affected the inhibitory of ACE activity [12]. In addition, Ile, Leu, Val and Met were found preferred for the third amino acid residue. The fact that R-substituents of Ile, Leu, Val and Met containing long alkyl chains, confirmed our simulation result, i.e. bulky groups at the third amino acid residue are favorable. For the electrostatic field, blue (in the web version) contours around the Nterminal showed that greater values of bioactivity were correlated with more positive charge near this region and as such that peptides 107e110 with His as the second residue from C-terminal showed higher activities. Considering the models above, ACE appears to prefer substrates with positive charge at both N- and Cterminal [12]. 4.2. Antimicrobial peptides CoMFA steric and electrostatic contributions were shown in Fig. 3(d, e). In the steric field [Fig. 3(d)], a large green (in the web version) contour at P2 and a small one at P5 suggested bulkier groups were favored at these positions. As seen in Table 2, the most
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Fig. 1. The superimpositions of peptides in the training and test sets of each database with common substructures: the superimposition of (a) all ACE-inhibitory dipeptides on peptide 28 (Table 1); (b) all ACE-inhibitory tripeptides on peptide 44 (Table 1); (c) all ACE-inhibitory tetra-, penta- and hexa-peptides on peptide 105 (Table 1); (d) all antimicrobial peptides on peptide 6 (Table 2); (e) all bitter peptides on peptide 39 (Table 2).
active peptide 6 had the Arg-residue at P2 and Lys-residue at P5; in the case of peptide 23, a substitute for Trp-residue at P5 led to a low activity; additionally, the Arg-residue at P2 of peptide 15 instead of the Trp-residue of peptide 14 led to a higher activity; similarly, the Ile-residue instead of Trp-residue at P5 resulted in difference in biological activity between peptide 19 (pIC50 ¼ 6.70) and peptide 14 (pIC50 ¼ 4.60). However, the flanking yellow contour at P2 indicated that careful selection of substituent group was required in this region. For example, the peptide 21 with Arg-residue at P2 exhibited a lower activity than peptide 19 (Trp-residue at P2).
Table 3 The statistical parameters for the best 3D-QSAR CoMFA/CoMSIA models. Parameters
ACE inhibitory peptides Dipeptides Tripeptides Tetra-, pentaand hexapeptides CoMSIA
NC 6 Q2 0.862 2 R 0.952 SCV 0.29 S 0.172 F 78.8 Field contributions Steric 0.553 Electrostatic 0.447 H-bond acceptor
Antimicrobial Bitter peptides peptides
CoMFA
CoMFA
CoMFA
CoMSIA
11 0.848 0.995 0.462 0.084 233.8
4 0.656 0.975 0.539 0.145 293.5
3 0.601 0.965 0.391 0.117 117.752
3 0.530 0.870 0.587 0.309 28.932
0.690 0.310
0.645 0.355
0.527 0.473
0.582 0.418
NC ¼ Optimal number of principal components; Q2 ¼ The leave-one-out (LOO) cross-validation coefficient; Scv ¼ Standard error of estimate. R2 ¼ The predictive correlation coefficient; S ¼ Standard error of prediction; F ¼ Ftest value.
Therefore, it can be concluded that at these positions (P2 and P5), a straight chain of four carbon length is beneficial to the activity, and this is supported by peptide 16 and peptide 5, which own aromatic groups at P2 or P5 with low activity. Some small yellow (in the web version) contours were mapped at P1 and P6, showing the substituent with the bulky steric was detrimental to the inhibitory potency. By comparing peptide 8 and peptides 3, 4 or 7, respectively, it was found that the presence of a larger group (Arg- or Lysresidue for peptides 3, 4 or 7; while Ile-residue for peptide 8) at P1 was harmful to the activity. For the electrostatic field [Fig. 3(e)], two large red (in the web version) contours surrounded the P1 and P6, while several small blue (in the web version) ones were found upon P2, P5 and P7, indicating that electronegative groups and electropositive substituents were favorable in these regions, respectively. Within all samples, those with positive Lys or Arg at the N-terminal owned high activities, and this could be explained by the previous report [27]. Moreover, it is further proved that introducing Lysresidue or Arg-residue at P2 and P5 is beneficial to improve the antimicrobial inhibitory. 4.3. Bitter peptides To visualize the results of the CoMSIA model, the graphical interpretation of the steric and H-bond acceptor fields were shown in Fig. 3(f, g). For H-bond acceptor field, contours in magenta and red (in the web version) (80% and 20% contributions) depicted the favorable and unfavorable hydrogen bond acceptor groups, respectively. As shown in Fig. 3(f), two medium size green (in the web version) contours upon the third and fifth residues from Cterminal indicated the location of bulky groups had an enhanced effect on the bitter taste. For instance, by comparing peptides 35 and 37, peptides 30 and 31, as well as peptides 33 and 34, it was
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Fig. 2. Scatter plot of observed against predicted log IC50 values of ACE-inhibitory peptides for the training and test set compounds based on the best models. (a) CoMSIA model for dipeptides; (b) CoMFA model for tripeptides; (c) CoMFA model for tetra-, penta- and hexa-peptides; (d) scatter plot of observed against predicted pIC50 values of antimicrobial peptides for the training and test set peptides based on CoMFA model; (e) scatter plot of observed against predicted log 1/T values of bitter peptides for the training and test set peptides based on CoMSIA model. (B) training set, (C) test set.
found that the presence of Arg or Phe instead of Gly was favorable at the third or fifth residue. A large yellow (in the web version) contour was mapped around the second and fourth residues with several small ones that located on the eighth or first residue from C-
terminal, suggesting that bulkier amino acids at the second or fourth residue from C-terminal was important for the prediction of the bitterness. This has been reported by Kim et al. [13]; additionally, by comparing peptide 39 and 38, it was noted that Pro
Fig. 3. Steric (left) and electrostatic (right) contour maps of the best CoMFA/CoMSIA models seen around ACE-inhibitory peptides, (a) dipeptides based on peptide 28 (Table 1); (b) tripeptides based on peptide 44 (Table 1); (c) tetra-, penta- and hexa-peptides based on peptide 105 (Table 1). The contour maps of CoMFA model: (d) steric and (e) electrostatic for antimicrobial peptides based on peptide 6 (Table 2). The contour maps of the CoMSIA model: (f) steric and (g) H-bond acceptor for bitter peptides based on peptide 39 (Table 2).
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instead of Arg at the fourth residue led to a high activity. For Hbond acceptor [Fig. 3(g)], some large red (in the web version) contours around the C-terminal indicated that a strong non-Hbond acceptor group might be a necessity for strong bitter taste. However, according to the previous report [34], the hydrophobic Cterminal sequence enhanced the bitterness; other researchers also found the important effect of hydrophobic amino acid residues at the C-terminal on the bitter tast [35e38]. Here, it must be noted that, considering the complexity of bitterness of oligopeptides, a more suitable QSAR model should be constructed [39]. 5. Conclusion
[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
In conclusion, QSAReCoMFA/CoMSIA analysis was applied to the ACE-inhibitory, antimicrobial, and bitter-tasting peptides. Meanwhile, predictive 3D-QSAR models were established using SYBYL. The optimum models were all statistically significant with cross-validated coefficients (Q2) >0.5 and conventional coefficients (R2) >0.9, indicating they were reliable enough for activity prediction and providing some insights into the critical structural factors affecting the bioactivity of ACE-inhibitory, antimicrobial and bitter-tasting peptides. Furthermore, the contour maps suggested sufficient information for understanding the structureeactivity relationship and thus aided in the design of novel bioactive peptides. Acknowledgment This work was supported by the Natural Science Foundation of China (Nos. 51173128, 31071509), the 863 Program of China (Nos. 2008AA10Z318, 2012AA06A303, 2013AA102204); the Ministry of Science and Technology of China (No. 2012YQ090194), the Beiyang Young Scholar of Tianjin University (2012) and the Program of Introducing Talents of Discipline to Universities of China (No. B06006). Appendix A. Supplementary data
[16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35]
Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.ejmech.2014.07.015.
[36] [37]
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Contents lists available at ScienceDirect
European Journal of Medicinal Chemistry journal homepage: http://www.elsevier.com/locate/ejmech
Original article
CoMFA and CoMSIA analysis of ACE-inhibitory, antimicrobial and bitter-tasting peptides Shufen Wu a, Wei Qi a, b, c, d, *, Rongxin Su a, b, c, d, Tonghe Li a, Dan Lu a, Zhimin He a, b, d a
Chemical Engineering Research Center, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, PR China State Key Laboratory of Chemical Engineering, Tianjin University, Tianjin 300072, PR China c Tianjin Key Laboratory of Membrane Science and Desalination Technology, Tianjin University, Tianjin 300072, PR China d The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, PR China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 November 2013 Received in revised form 21 June 2014 Accepted 5 July 2014 Available online 7 July 2014
Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to the ACE-inhibitory, antimicrobial, and bitter-tasting peptides. Predictive 3DQSAR models were established using SYBYL multifit molecular alignment rule over a training set and a test set. The optimum models were all statistically significant with cross-validated coefficients (Q2) >0.5 and conventional coefficients (R2) >0.9, indicating that they were reliable enough for activity prediction. The obtained results may aid in the design of novel bioactive peptides. © 2014 Elsevier Masson SAS. All rights reserved.
Keywords: ACE inhibition Antimicrobial Bitterness CoMFA CoMSIA
1. Introduction Considerable attention has been paid to bioactive peptides derived from food proteins and much work regarding those peptides is currently underway, mainly focusing on their release via selectively enzymatic hydrolysis. Bioactive peptides have been described to promote different health effects [1e3], mainly including antimicrobial properties, blood pressure-lowering (ACE inhibitory) effects, antithrombotic and antioxidant activities. Despite the fact that an increasing number of new peptides are continuously reported in literatures, the rationale by which these peptides play the relevant physiological roles has not been well elucidated. Moreover, the conventional activity-based purification steps often take months to generate a new sequence, which largely limit their throughput. At the same time, some peptides with strong bitter taste associated with most food protein hydrolyzates have been identified [4], which is a negative aspect. Thus, how to decrease or eliminate unwanted taste is very important, and numerous options have been investigated for hydrolyzate debittering [5]. In order to identify novel peptides, as well as prepare and
debitterize more efficiently, there is a need to explore an alternative and more theoretical approach. Three-dimensional quantitative structureeactivity relationship (3D-QSAR) method is helpful to extract a correlation between biological activity and chemical structure [6]; especially, the CoMFA and CoMSIA are particularly effective methods based on statistical techniques [7e9]. These two models sample the potential fields surrounding a set of ligands and construct 3D-QSAR models by correlating these 3D fields with the corresponding experimental activities of ligands interacting with a common target receptor. Although several peptide QSAR models have been reported to describe the structureeactivity relationships of antimicrobial, ACEinhibitory, and bitter-tasting peptides [10], 3D-QSAReCoMFA/CoMSIA models for these compounds, to our knowledge, have not been derived simultaneously in the literature. Therefore, the present study aims to develop predictive models and evaluate what structural features (hydrogen bond donor/acceptor, hydrophobic, steric and electrostatic) are responsible for these three bioactivity peptides. 2. Materials and methods 2.1. Preparation of data set
* Corresponding author. The Co-Innovation Center of Chemistry and Chemical Engineering of Tianjin, Tianjin University, Tianjin 300072, PR China. E-mail address: [email protected] (W. Qi). http://dx.doi.org/10.1016/j.ejmech.2014.07.015 0223-5234/© 2014 Elsevier Masson SAS. All rights reserved.
A total of 113 ACE-inhibitory peptides, which were classified into three data sets according to peptide length, i.e. di-peptides, tri-
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101
Table 1 ACE inhibitory peptides used for 3D-QSAR analysis. C#a
Sequence
Dipeptides 1 YP 2 FP 3 RP 4 GP 5 TP 6 VP 7 KP 8 IP 9 DY 10 RY 11 AY 12 HY 13 SY 14 NY 15 VY 16 KY 17 MF 18 RF 19 SF 20 GF 21 NF 22 DF 25 AW 26 LW 27 GW 28 VW 29 MW 30 RW 31 IW c 32 AP c 33 IY c 34 LY 35c GY 36c FY 37c TF 38c AF c 39 VF c 40 KF a b c
Log IC50b
C#
Observed
Predicted (error)
2.86 2.50 1.32 2.56 2.46 2.62 1.34 2.11 2.00 1.71 1.28 1.42 1.82 1.51 1.20 0.89 1.65 1.97 2.11 2.44 1.67 2.56 1.08 1.24 1.40 0.20 1.00 1.20 0.67 2.43 0.79 1.51 2.32 1.40 1.95 1.88 0.96 1.45
2.69(0.17) 2.62(0.12) 1.54(0.22) 2.65(0.09) 2.52(0.06) 2.39(0.23) 1.32(0.02) 2.29(0.18) 2.01(0.01) 1.54(0.17) 1.56(0.28) 1.39(0.03) 1.78(0.04) 1.39(0.12) 1.20(0.00) 0.87(0.02) 1.79(0.14) 2.06(0.09) 2.10(0.01) 2.30(0.14) 1.83(0.16) 2.47(0.09) 1.10(0.02) 0.94(0.30) 1.37(0.03) 0.57(0.37) 1.06(0.06) 1.18(0.02) 0.56(0.11) 2.49(0.06) 1.05(0.26) 1.43(0.08) 1.91(0.41) 1.30(0.10) 1.99(0.04) 1.97(0.09) 1.42(0.46) 1.21(0.24)
Sequence
Tripeptides 41 FEP 42 LKP 43 ALP 44 LRP 45 IEP 46 LAP 47 GRP 48 LSP 49 IAP 50 MNP 51 LEP 52 TNP 53 VSP 54 VLP 55 ILP 56 LNP 57 VGP 58 GKP 59 VYP 60 IKP 61 FAP 62 AVP 65 VRP 66c LYP c 67 DLP 68c IRP 69c FQP 70c LQP 71c GEP c 72 VMP Tetra-, 73 74 75 76 77 78
Log IC50
C#
Observed
Predicted (error)
1.08 0.60 2.38 0.57 0.20 0.54 1.30 0.23 0.43 1.82 0.28 2.32 1.00 1.91 1.51 1.76 1.42 2.55 2.46 0.84 0.58 2.53 0.34 0.82 0.68 0.13 1.08 0.28 2.51 1.46
1.05(0.03) 0.61(0.01) 2.35(0.03) 0.56(0.01) 0.35(0.15) 0.61(0.07) 1.27(0.03) 0.22(0.01) 0.43(0.00) 1.85(0.03) 0.30(0.02) 2.26(0.06) 1.07(0.07) 1.98(0.07) 1.51(0.00) 1.86(0.10) 1.38(0.04) 2.59(0.04) 2.38(0.08) 0.83(0.01) 0.62(0.04) 2.49(0.04) 0.32(0.02) 0.21(0.61) 1.18(0.50) 0.13(0.00) 0.84(0.24) 0.80(0.52) 2.16(0.35) 1.98(0.52)
penta- and hexa-peptides GWAP 0.59 FVAP 1.00 AVGP 0.49 SPYP 2.93 VSSP 1.00 VVPP 2.45
0.33(0.26) 0.94(0.06) 0.70(0.21) 2.78(0.15) 1.10(0.10) 2.24(0.21)
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95c 96c 97c 98c 99c 100 101 102 103 104 105 106 107 108 109 110 111 112c 113c
Sequence
LVPP ILPP PKHP SKVPP APVPP PVPQP GRVMP PQPIP TKVIP VHLPP VHLAP IAYKP LALPP VLPYP HQIYP VLIVP HLPLP FFVAP AVPYP VLPIP DKIHP DYGLYP VRGPFP VIEKYP KVLPVP PVVVPP RPQIPP NIFYCP LHLPLP LHLPAP LHLPYP LHLPGP LHLYLP LHLWLP LAYFYP
Log IC50 Observed
Predicted (error)
2.10 2.24 2.85 1.87 2.80 2.04 2.40 2.53 2.60 1.26 0.65 0.34 2.90 1.56 2.04 2.23 1.61 0.78 1.90 1.49 2.05 1.79 2.77 2.24 0.70 2.07 1.66 1.18 0.57 0.78 0.36 0.28 1.28 0.95 1.81
2.12(0.02) 2.40(0.16) 2.74(0.11) 1.82(0.05) 2.82(0.02) 1.97(0.07) 2.45(0.05) 2.46(0.07) 2.67(0.07) 1.23(0.03) 0.81(0.16) 0.31(0.03) 2.92(0.02) 1.51(0.05) 2.05(0.01) 2.14(0.09) 1.40(0.21) 1.20(0.42) 1.90(0.00) 1.76(0.27) 1.85(0.20) 1.76(0.03) 2.77(0.00) 2.39(0.15) 0.74(0.04) 2.26(0.19) 1.66(0.00) 0.94(0.24) 0.70(0.13) 0.45(0.33) 0.64(0.28) 0.36(0.08) 1.26(0.02) 1.05(0.10) 1.47(0.34)
C# ¼ Compounds. IC50 expressed as mM. Test set peptides.
peptides as well as tetra-, penta- and hexa-peptides, were selected from published literatures [11e20]. Each peptide was listed in Table 1 with a reported in vitro IC50 (the peptide concentration required to produce 50% inhibition of ACE) value that was less than 1 mM. Prior to model training, the IC50 values of all ACE-inhibitory peptides in units of micromolarity (mM) were transformed into log IC50. Twenty-four antimicrobial peptides with their relative IC50 (Rel IC50, the ratio of the IC50 for the experimental peptide to the IC50 of Bac2A with sequence RLARIVVIRVAR) values selected for the present analysis were taken from the published literature [21]. In order to facilitate the modeling, the Rel IC50 values were converted into log (106/Rel IC50). Moreover, twenty-one bitter peptides were collected as a database from the previous report [13]. During the following 3DQSAR analysis, the bitterness values determined by sensory evaluations were expressed as log 1/T, where T was the bitter threshold concentration (M). The fifty-four peptides as well as their biological activities were shown in Table 2. The selection of training set was based on the distribution of biological data and structural diversity. For example, the log IC50 values of dipeptides span a rang of 0.2e2.86 in Table 1, which providing a broad data set for the 3D-QSAR analysis; moreover, the data set of bitter peptides in Table 2 consisted of eight groups by
length, i.e. tri-, tetra-, penta-, hexa-, hepta-, octa-, deca- and dodeca-peptides, to ensure the 3D-QSAR model extendable. Testing set peptides marked with c and e in Tables 1 and 2, were selected manually to evaluate the predictive power of the resulting models. 2.2. Molecular modeling and alignment The 3D-QSAR study was performed on SYBYL 6.92 (Tripos, Inc, St. Louis, USA). The 3D structures of all molecules were constructed using the Build Protein function. Partial atomic charges of all compounds were calculated by the GasteigereMarsili method, and then were optimized for their geometry using Tripos field [22] with a distance-dependent dielectric function and energy convergence criterion of 0.005 kcal/mol Å using the maximum iterations set to 1000 [23]. For the molecule alignment, the most active peptide of each data set was chosen as the template. All peptides were aligned on the basis of the common structure, and the final superimpositions were displayed in Fig. 1. 2.3. CoMFA and CoMSIA analysis To derive the CoMFA and CoMSIA descriptor fields, the aligned training set molecules were placed in a 3D cubic lattice with grid spacing of 2Å in x, y, and z directions such that the entire set was
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included in it. The CoMFA steric and electrostatic field energies were calculated using a sp3 carbon probe atom with a van der Waals radius of 1.52 Å and a charge of þ1.0. Cut-off values for both steric and electrostatic fields were set to 30.0 kcal/mol. For CoMSIA analysis, the standard settings [probe with charge þ1, radius 1Å, hydrophobicity þ1.0, hydrogen-bond donating þ1.0, hydrogen bond accepting þ1.0 [9]] were used to calculate five different fields: steric, electrostatic, hydrophobic, acceptor and donor. Gaussiantype distance dependence was used to measure the relative attenuation of the field position of each atom in the lattice, and led to much smoother sampling of the fields around the molecules when compared to CoMFA. The default value of 0.3 was set for attenuation factor a. For CoMFA and CoMSIA analysis, two or five descriptor fields are not totally independent of each other [24,25]. Therefore, we systematically altered the combination of fields and chose the best model. 2.4. Regression analysis by PLS method The partial least squares (PLS) methodology analysis with the leave-one-out (LOO) cross-validation procedure was carried out to determine the optimal number of components using the SAMPLS [26]. The cross-validated coefficient Q2, as an internal statistical index of predictive power, was subsequently obtained. In order to test the real predictive ability of the best model derived by the training set, biological activities of the test set were predicted. The quality of the external prediction was documented using the standard deviation of error prediction (R2). Q2 and R2 are calculated according to the formula [27]:
P 2
Q ¼1 P P R2 ¼ 1 P
Yobs YCVpred
2
ðYobs Ymean Þ2 Yobs Ypred
2
ðYobs Ymean Þ2
where Ymean means average activity value of the entire data set, while Yobs, Ypred and YCVpred represent observed, predicted and cross-validated activity values, respectively. Often, a high Q2 and R2 value (Q2 > 0.5, R2 > 0.6) is considered as a proof of high predictive ability of the model [28]. The CoMFA/CoMSIA results were graphically interpreted by field contribution maps using field type “stdev*coeff”, and the contour levels were set to default values. 3. Results The most intricate and subjective part of CoMFA/CoMSIA studies is molecular alignment. In general, geometric similarity should exist between the modeled structure and the bioactive conformation for 3D-QSAR. Thus, in this work, peptides with the highest activity in Tables [peptide 28 with log IC50 ¼ 0.20 (Table 1), peptide 44 with log IC50 ¼ 0.57 (Table 1); peptide 6 with pIC50 ¼ 6.89 (Table 2); peptide 39 with log 1/T ¼ 5.70 (Table 2)] of each data set were used as the templates; additionally, it was necessary to point out that for tetra/penta/hexapeptides, peptide 105 with log IC50 ¼ 1.66 (Table 1) was chosen as the template, because tetra-, penta- and hexa-peptides could be all aligned on the common structure and peptide 105 was with the sequence IPP (the famous ACE-inhibitory tripeptide) at the C-terminal. As stated before, since the five different descriptor fields may not be totally independent, all 31 possible
combinations of the descriptors for each group (dipeptides, tripeptides, tetra/penta/hexa-peptides, antimicrobial peptides and bitter peptides) were attempted to build the optimum model, and the evaluation parameters were calculated for every model (see Supplementary data Table S1eS5). For analysis, we focused on the best model for each data set, and the statistical parameters were displayed in Table 3. 3.1. ACE-inhibitory peptides As shown in Table 3, for ACE-inhibitory peptides, the best predictions were obtained by the 3D-QSAR/CoMSIA, 3D-QSAR/ CoMFA and 3D-QSAR/CoMFA for dipeptides, tripeptides, tetraypentayhexa-peptides, respectively. Each model had a high R2 (0.952, 0.995 and 0.975, respectively) with a low standard deviation (S, 0.172, 0.084 and 0.145, respectively) and a high Fischer ratio (F, 78.8, 233.8 and 293.5, respectively); while a QSAR model is generally acceptable if R2 is approximately 0.9 or higher [29]; furthermore, the steric and electrostatic fields among the five ones (steric, electrostatic, hydrophobic, acceptor and donor fields) contributed prominently and thus detailed analyses were focused on these two fields. The plots of the predicted versus actual log IC50 for the best QSAR models were displayed in Fig. 2(aec). It could be noted that the data points were uniformly distributed along the regression line; additionally, as shown in Table 1, all the prediction errors were smaller than 0.7, suggesting the satisfactorily predictive capability, high reliability and accuracy of the models. To view the field effect on the target property, CoMFA and CoMSIA contour maps were generated and shown in Fig. 3(aec). For steric fields, the green (in the web version) (bulky group favorable) and yellow (in the web version) (bulky group unfavorable) contours represented 80% and 20% contributions, respectively. Similarly, the blue (in the web version) (electropositive charge favorable) and red (in the web version) (electronegative charge favorable) contours in the electrostatic field represented 80% and 20% contributions, respectively. 3.2. Antimicrobial peptides The 3D-QSAReCoMFA/CoMSIA models were used to predict the antimicrobial activity of 24 nonapeptides against Pseudomonas aeruginosa [21]. PLS analysis performed with three principal components led to good statistical parameters (Table 3), and the optimal CoMFA model was established. Based on this model, the correlation between experimental and predicted antimicrobial activity of all peptides was presented in Fig. 2(d). From Table 2, the lowest prediction errors were obtained for peptide 17 (0.00) and peptide 4 (0.01); while the worst predictions were obtained for peptide 19 (0.62) and peptide 21 (0.55). These results suggested that the latter two peptides were not suitable for being templates to design new antimicrobial peptides. In general, most date points were uniformly dispersed along a line with a slope coefficient of 1.0 (Fig. 2), which indicating that no systematic error existed in the method. 3.3. Bitter peptides The best CoMFA model for bitter peptides was constructed using a series of 21 peptides with a training set of 17 peptides and a test set of 4 peptides as shown in Table 2. In Fig. 2(e), a strong linear relation was displayed between the predicted and actual log 1/T values, and most samples were rather uniformly dispersed around the regression line except the compound 43 (0.60), indicating the satisfactorily predictive capability of the model.
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103
Table 2 Antimicrobial and Bitter peptides used for 3D-QSAR analysis. Antimicrobial peptides
Bitter peptides
C#a
Sequence
Rel IC50b
pIC50c Observed
Predicted (error)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18e 19e 20e 21e 22e 23e 24e
RWRRKWWWW RIKRWWWWR KKRWLWWWR RWWRKWWIR RRRWWWWWW KRWWKWWRR RRIWRWWWW IRRRKWWWW KRKIWWWIR WRWWKWWRR TRKVWWWRW RHWKTWWKR RKRLKWWIY RWILWWWRI IRRRWWWIV WHGVRWWKW RRKRWWWWW WKRWKWWKR KWWKIWWKR RIKVIWWWR KRRRIWWWK LRFILWWKR KRWWWWWKR RKWWRWWRW
0.39 0.31 0.30 0.24 1.82 0.13 0.68 0.21 0.28 0.23 0.76 0.95 0.18 25 0.23 2.5 0.43 0.25 0.20 0.51 0.40 0.88 0.22 0.31
6.41 6.51 6.52 6.62 5.74 6.89 6.17 6.68 6.55 6.64 6.12 6.02 6.74 4.60 6.64 5.60 6.37 6.60 6.70 6.29 6.40 6.06 6.66 6.51
6.36(0.05) 6.38(0.13) 6.48(0.04) 6.63(0.01) 5.90(0.16) 6.81(0.07) 6.07(0.10) 6.95(0.27) 6.60(0.05) 6.74(0.10) 6.06(0.06) 5.99(0.03) 6.69(0.06) 4.69(0.09) 6.53(0.11) 5.57(0.03) 6.37(0.00) 7.01(0.41) 6.08(0.62) 6.00(0.29) 6.95(0.55) 6.16(0.10) 6.18(0.48) 6.24(0.27)
a b c d e
C#
Sequence
Log 1/Td Observed
Predicted (error)
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42e 43e 44e 45e
GFF GGFF GPFF RGFF RPFF RPGFF RRPFF RPGGFF RGPPFF RRPPFF RGPPGFF RPPPFFF RGPPFFF RPFFRPFF RRPPPFFF FFRPFFRPFF RPFFRPFFRPFF FFF GGRPFF RGPPGGFF RGPPGGGFF
3.23 2.85 3.80 3.80 4.40 3.51 4.70 4.04 4.23 5.15 4.40 4.70 5.00 5.00 5.70 5.15 5.00 3.70 4.04 4.11 3.95
3.16(0.07) 3.06(0.21) 3.64(0.16) 3.60(0.20) 4.37(0.03) 3.53(0.02) 4.82(0.12) 4.14(0.10) 4.26(0.03) 5.14(0.01) 4.52(0.12) 4.72(0.02) 4.95(0.05) 5.04(0.04) 5.67(0.03) 5.02(0.13) 5.03(0.03) 3.43(0.27) 4.29(0.25) 4.04(0.07) 4.23(0.28)
C# ¼ Compounds. Rel IC50, relative IC50, defined as the ratio of the IC50 for the experimental peptide to the IC50 of the control peptide Bac2A with sequence RLARIVVIRVAR. pIC50 ¼ log(106/Rel IC50). T was the bitter threshold concentration in unit of molarity (M). Test set peptides.
4. Discussion 4.1. ACE-inhibitory peptides In the CoMSIA model for dipeptides based on peptide 28, the distribution of steric field (0.553) was little higher than that of electrostatic field (0.447). As seen in Fig. 3 (a-left), two green (in the web version) contours near N-terminal suggested steric bulky substituents were favored there; a small yellow (in the web version) contour around C-terminal represented the disfavor of bulky group at this region. It can be confirmed by the fact that the activities of dipeptides 31, 25 and 27 decreased accompanied by the less of their R-substituents volume at the N-terminal. The same situation occurred to two sets of dipeptides 8, 32 and 4 as well as 33, 11 and 35. In Fig. 3 (a-right), positively charged favorable blue (in the web version) regions were found around the N-terminal, while negatively charged favorable red (in the web version) regions were observed near the C-terminal, which could indicate nothing for its malposition. By comparing dipeptides 10,12,16 and 35, it could be found that the positive charge on the guanidino or ε-amino group of C-terminal Arg and the imidazole of His as well as Lys side-chains, resulted in high inhibitory potency [30]. Meanwhile, other researches have suggested that amino acids with positively charged functional groups at N-terminal are potential inhibitors of ACE [30e33]. Although this CoMFA model could not reflect the conformational behavior of ACE-inhibitory dipeptides sufficiently, it confirmed dipeptides with Trp, Tyr and Phe at Cterminal would exhibit high activity [32]. For tripeptides, the optimal CoMFA model validated internally yields Q2 ¼ 0.848. The steric contour map [Fig. 3 (b-left)] was characterized by a medium green (in the web version) contour upon the N-terminal and a small contour around the C-terminal, indicating that compounds taking Ile or Leu as the first amide group
were more active than those with Gly. Another yellow (in the web version) contour near the second amide group was hard to be confirmed because of its malposition. Fig. 3 (b-right) displayed a medium blue (in the web version) contour upon the second residue with no obvious red (in the web version) ones mapped. Thus it is suggested that amino acids with positive charge are preferred for the middle residue of the tripeptides, which is consistent with the previous study [11]. For tetra-, penta- and hexa-peptides, a CoMFA model was constructed with a highest F value of 293.5. In the CoMFA contour maps [Fig. 3(c)], the green (in the web version) and yellow (in the web version) contours appeared near the third and fifth residues from the C-terminal, respectively, which was consistent with the point that the tetrapeptide instead of tripeptide residues from C-terminal largely affected the inhibitory of ACE activity [12]. In addition, Ile, Leu, Val and Met were found preferred for the third amino acid residue. The fact that R-substituents of Ile, Leu, Val and Met containing long alkyl chains, confirmed our simulation result, i.e. bulky groups at the third amino acid residue are favorable. For the electrostatic field, blue (in the web version) contours around the Nterminal showed that greater values of bioactivity were correlated with more positive charge near this region and as such that peptides 107e110 with His as the second residue from C-terminal showed higher activities. Considering the models above, ACE appears to prefer substrates with positive charge at both N- and Cterminal [12]. 4.2. Antimicrobial peptides CoMFA steric and electrostatic contributions were shown in Fig. 3(d, e). In the steric field [Fig. 3(d)], a large green (in the web version) contour at P2 and a small one at P5 suggested bulkier groups were favored at these positions. As seen in Table 2, the most
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Fig. 1. The superimpositions of peptides in the training and test sets of each database with common substructures: the superimposition of (a) all ACE-inhibitory dipeptides on peptide 28 (Table 1); (b) all ACE-inhibitory tripeptides on peptide 44 (Table 1); (c) all ACE-inhibitory tetra-, penta- and hexa-peptides on peptide 105 (Table 1); (d) all antimicrobial peptides on peptide 6 (Table 2); (e) all bitter peptides on peptide 39 (Table 2).
active peptide 6 had the Arg-residue at P2 and Lys-residue at P5; in the case of peptide 23, a substitute for Trp-residue at P5 led to a low activity; additionally, the Arg-residue at P2 of peptide 15 instead of the Trp-residue of peptide 14 led to a higher activity; similarly, the Ile-residue instead of Trp-residue at P5 resulted in difference in biological activity between peptide 19 (pIC50 ¼ 6.70) and peptide 14 (pIC50 ¼ 4.60). However, the flanking yellow contour at P2 indicated that careful selection of substituent group was required in this region. For example, the peptide 21 with Arg-residue at P2 exhibited a lower activity than peptide 19 (Trp-residue at P2).
Table 3 The statistical parameters for the best 3D-QSAR CoMFA/CoMSIA models. Parameters
ACE inhibitory peptides Dipeptides Tripeptides Tetra-, pentaand hexapeptides CoMSIA
NC 6 Q2 0.862 2 R 0.952 SCV 0.29 S 0.172 F 78.8 Field contributions Steric 0.553 Electrostatic 0.447 H-bond acceptor
Antimicrobial Bitter peptides peptides
CoMFA
CoMFA
CoMFA
CoMSIA
11 0.848 0.995 0.462 0.084 233.8
4 0.656 0.975 0.539 0.145 293.5
3 0.601 0.965 0.391 0.117 117.752
3 0.530 0.870 0.587 0.309 28.932
0.690 0.310
0.645 0.355
0.527 0.473
0.582 0.418
NC ¼ Optimal number of principal components; Q2 ¼ The leave-one-out (LOO) cross-validation coefficient; Scv ¼ Standard error of estimate. R2 ¼ The predictive correlation coefficient; S ¼ Standard error of prediction; F ¼ Ftest value.
Therefore, it can be concluded that at these positions (P2 and P5), a straight chain of four carbon length is beneficial to the activity, and this is supported by peptide 16 and peptide 5, which own aromatic groups at P2 or P5 with low activity. Some small yellow (in the web version) contours were mapped at P1 and P6, showing the substituent with the bulky steric was detrimental to the inhibitory potency. By comparing peptide 8 and peptides 3, 4 or 7, respectively, it was found that the presence of a larger group (Arg- or Lysresidue for peptides 3, 4 or 7; while Ile-residue for peptide 8) at P1 was harmful to the activity. For the electrostatic field [Fig. 3(e)], two large red (in the web version) contours surrounded the P1 and P6, while several small blue (in the web version) ones were found upon P2, P5 and P7, indicating that electronegative groups and electropositive substituents were favorable in these regions, respectively. Within all samples, those with positive Lys or Arg at the N-terminal owned high activities, and this could be explained by the previous report [27]. Moreover, it is further proved that introducing Lysresidue or Arg-residue at P2 and P5 is beneficial to improve the antimicrobial inhibitory. 4.3. Bitter peptides To visualize the results of the CoMSIA model, the graphical interpretation of the steric and H-bond acceptor fields were shown in Fig. 3(f, g). For H-bond acceptor field, contours in magenta and red (in the web version) (80% and 20% contributions) depicted the favorable and unfavorable hydrogen bond acceptor groups, respectively. As shown in Fig. 3(f), two medium size green (in the web version) contours upon the third and fifth residues from Cterminal indicated the location of bulky groups had an enhanced effect on the bitter taste. For instance, by comparing peptides 35 and 37, peptides 30 and 31, as well as peptides 33 and 34, it was
S. Wu et al. / European Journal of Medicinal Chemistry 84 (2014) 100e106
105
Fig. 2. Scatter plot of observed against predicted log IC50 values of ACE-inhibitory peptides for the training and test set compounds based on the best models. (a) CoMSIA model for dipeptides; (b) CoMFA model for tripeptides; (c) CoMFA model for tetra-, penta- and hexa-peptides; (d) scatter plot of observed against predicted pIC50 values of antimicrobial peptides for the training and test set peptides based on CoMFA model; (e) scatter plot of observed against predicted log 1/T values of bitter peptides for the training and test set peptides based on CoMSIA model. (B) training set, (C) test set.
found that the presence of Arg or Phe instead of Gly was favorable at the third or fifth residue. A large yellow (in the web version) contour was mapped around the second and fourth residues with several small ones that located on the eighth or first residue from C-
terminal, suggesting that bulkier amino acids at the second or fourth residue from C-terminal was important for the prediction of the bitterness. This has been reported by Kim et al. [13]; additionally, by comparing peptide 39 and 38, it was noted that Pro
Fig. 3. Steric (left) and electrostatic (right) contour maps of the best CoMFA/CoMSIA models seen around ACE-inhibitory peptides, (a) dipeptides based on peptide 28 (Table 1); (b) tripeptides based on peptide 44 (Table 1); (c) tetra-, penta- and hexa-peptides based on peptide 105 (Table 1). The contour maps of CoMFA model: (d) steric and (e) electrostatic for antimicrobial peptides based on peptide 6 (Table 2). The contour maps of the CoMSIA model: (f) steric and (g) H-bond acceptor for bitter peptides based on peptide 39 (Table 2).
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instead of Arg at the fourth residue led to a high activity. For Hbond acceptor [Fig. 3(g)], some large red (in the web version) contours around the C-terminal indicated that a strong non-Hbond acceptor group might be a necessity for strong bitter taste. However, according to the previous report [34], the hydrophobic Cterminal sequence enhanced the bitterness; other researchers also found the important effect of hydrophobic amino acid residues at the C-terminal on the bitter tast [35e38]. Here, it must be noted that, considering the complexity of bitterness of oligopeptides, a more suitable QSAR model should be constructed [39]. 5. Conclusion
[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]
In conclusion, QSAReCoMFA/CoMSIA analysis was applied to the ACE-inhibitory, antimicrobial, and bitter-tasting peptides. Meanwhile, predictive 3D-QSAR models were established using SYBYL. The optimum models were all statistically significant with cross-validated coefficients (Q2) >0.5 and conventional coefficients (R2) >0.9, indicating they were reliable enough for activity prediction and providing some insights into the critical structural factors affecting the bioactivity of ACE-inhibitory, antimicrobial and bitter-tasting peptides. Furthermore, the contour maps suggested sufficient information for understanding the structureeactivity relationship and thus aided in the design of novel bioactive peptides. Acknowledgment This work was supported by the Natural Science Foundation of China (Nos. 51173128, 31071509), the 863 Program of China (Nos. 2008AA10Z318, 2012AA06A303, 2013AA102204); the Ministry of Science and Technology of China (No. 2012YQ090194), the Beiyang Young Scholar of Tianjin University (2012) and the Program of Introducing Talents of Discipline to Universities of China (No. B06006). Appendix A. Supplementary data
[16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35]
Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.ejmech.2014.07.015.
[36] [37]
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