On Structural Parameterization and Molecular Modeling of Peptide Analogues by Molecular Electronegativity Edge Vector (VMEE): Estimation and Prediction for Biological Activity of Dipeptides

QSBR study of bitter taste of peptides: application of GA-PLS in combination with MLR, SVM, and ANN approaches

Detailed information about the relationships between structures and properties/activities of peptides as drugs and nutrients is useful in the development of drugs and functional foods containing peptides as active compounds. The bitterness of the peptides is an undesirable property which should be reduced during drug/nutrient production, and quantitative structure bitter taste relationship (QSBR) studies can help researchers to design less bitter peptides with higher target efficiency. Calculated structural parameters were used to develop three different QSBR models (i.e., multiple linear regression, support vector machine, and artificial neural network) to predict the bitterness of 229 peptides (containing 2–12 amino acids, obtained from the literature). The developed models were validated using internal and external validation methods, and the prediction errors were checked using mean percentage deviation and absolute average error values. All developed models predicted the activities successfully (with prediction errors less than experimental error values), whereas the prediction errors for nonlinear methods were less than those for linear methods. The selected structural descriptors successfully differentiated between bitter and nonbitter peptides.

Quantitative Structure-Activity Relationship Study of Radical Scavenging Peptides Based on Orac Method by Using Different Sets of Amino Acids Descriptor

Some radical scavenging peptides by ORAC method from different hydrolysates were used for the quantitative structure-activity relationships (QSAR) research. Partial least-squares regression analysis (PLSR) was treated as the method to build the model with 17 kinds of amino acid descriptors. In order to translate the sequence to the same length, two-terminal position numbering (TTPN) was applied. Two of amino acid descriptors VSHE and VSW were selected for their excellent performance (R2, Q2, and RMSEcwith VHSE and VSW descriptor are 0.995, 0.630, 0.318 and 0.966, 0.543, 0.181 respectively). VHSE has the definite physicochemical meanings and easy to understand while VSW has good predictive ability (Rand RMSEpwith VHSE and VSW are 0.404, 2.633 and 0.635, 2.298 respectively). It is believed that the position No.2 amino acid from N-terminal (N2) have more importance than others in sequence, and most of electronic properties are negative to activity while all the steric properties are positive to activity as well as the hydrophobic properties. The suitable amino acids in sequence are as follow: G, R, K, W, Y, N, E, H, and Q are suitable for N2position which illustrated the importance of acidic amino acids in peptide sequence for radical scavenging activity.

Study of Peptides QSAR Based on Multidimensional Attributes (E) Using Multiple Linear Regression

A new amino acids descriptor E, which (E1~E5) has been introduced in bioactive peptides Quantitative Structure-Activity Relationship (QSAR) Study. It has been proved that correlate good with hydrophobicity, size, preference for amino acids to occur in -helices, composition and the net charge, respectively. They were then applied to construct characterization and QSAR analysis on 48 bitter tasting dipeptides and 30 bradykinin potentiating (BP) pentapeptides using multiple linear regression (MLR). The leave-one-out cross validation values (Q2(CV)) were 0.888 and 0.797, the multiple correlation coefficients (R2) were 0.940 and 0.891, respectively for bitter tasting dipeptides and BP pentapeptides. The results showed that, in comparison with the conventional descriptors, the descriptor (E) is a useful structure characterization method for peptide QSAR analysis. The importance of each property at each position in peptides is estimated by the regression coefficient value of the MLR model. The establishment of such methods will be a very meaningful work to peptide bioactive investigation in peptide drug design.

Descriptors for antimicrobial peptides

INTRODUCTION

A frightening increase in the number of isolated multidrug resistant bacterial strains linked to the decline in novel antimicrobial drugs entering the market is a great cause for concern. Cationic antimicrobial peptides (AMPs) have lately been introduced as a potential new class of antimicrobial drugs, and computational methods utilizing molecular descriptors can significantly accelerate the development of new peptide drug candidates.

AREAS COVERED

This paper gives a broad overview of peptide and amino-acid scale descriptors available for AMP modeling and highlights which of these are currently being used in quantitative structure-activity relationship (QSAR) studies for AMP optimization. Additionally, some key commercial computational tools are discussed, and both successful and less successful studies are referenced, illustrating some of the challenges facing AMP scientists. Through examples of different peptide QSAR studies, this review highlights some of the missing links and illuminates some of the questions that would be interesting to challenge in a more systematic fashion.

EXPERT OPINION

Computer-aided peptide QSAR using molecular descriptors may provide the necessary edge to peptide drug discovery, enabling successful design of a new generation anti-infective drug molecules. However, if this wonderful scenario is to play out, computational chemists and peptide microbiologists would need to start playing together and not just side by side.

ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues

In this study, structural topology scale (ST-scale) was recruited as a novel structural topological descriptor derived from principal component analysis on 827 structural variables of 167 amino acids. By using partial least squares (PLS), we applied ST-scale for the study of quantitative sequence-activity models (QSAMs) on three peptide datasets (58 angiotensin-converting enzyme (ACE) inhibitors, 34 antimicrobial peptides (AMPs) and 89 elastase substrates (ES)). The results of QSAMs were superior to that of the earlier studies, with determination coefficient (r(2)) and cross-validated (q(2)) equal to 0.855, 0.774; 0.79, 0.371 (OSC-PLS: 0.995, 0.848) and 0.846, 0.747, respectively. Therefore, ST-scale descriptors were considered to be competent to extract information from 827 structural variables and relate with their bioactivities.

Using multidimensional patterns of amino acid attributes for QSAR analysis of peptides

On the basis of exploratory factor analysis, six multidimensional patterns of 516 amino acid attributes, namely, factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, are proposed to represent structures of 48 bitter-tasting dipeptides and 58 angiotensin-converting enzyme inhibitors. Characteristic parameters related to bioactivities of the peptides studied are selected by genetic algorithm, and quantitative structure-activity relationship (QSAR) models are constructed by partial least square (PLS). Our results by a leave-one-out cross validation are compared with the previously known structure representation method and are shown to give slightly superior or comparative performance. Further, two data sets are divided into training sets and test sets to validate the characterization repertoire of FASGAI. Performance of the PLS models developed by training samples by a leave-one-out cross validation and external validation for test samples are satisfying. These results demonstrate that FASGAI is an effective representation technique of peptide structures, and that FASGAI vectors have many preponderant characteristics such as straightforward physicochemical information, high characterization competence and easy manipulation. They can be further applied to investigate the relationship between structures and functions of various peptides, even proteins.

Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): a novel rotation-translation invariant 3D structure descriptor and its applications to peptides

Quantitative structure-activity relationship (QSAR) study, important in drug design, mainly involves two aspects, molecular structural characterization (MSC) and construction of a statistical model. MSC focuses on transforming molecular structural and property characteristics into a group of numerical codes, dedicated to minimizing information loss during this process. In this context, common atoms in organic compounds are classified according to their families in the periodic table, and hybridization states, and on the basis of these, three nonbonding interactions (i.e. electrostatic, van der Waals and hydrophobic) are calculated, ultimately resulting in a new rotation-translation invariant, 3D-MSC, as a three-dimensional holograph vector of atomic interaction field (3D-HoVAIF). By applying 3D-HoVAIF to QSAR studies on two classical peptides including 58 angiotensin-converting enzyme (ACE) inhibitors and 48 bitter-tasting dipeptides, we get two excellent genetic algorithm-partial least squares (GA-PLS) models, with statistics r(2), q(2), root mean square error (RMSEE), and root mean square error of cross-validation (RMSCV) of 0.857, 0.811, 0.376, and 0.432 for ACE inhibitors and 0.940, 0.892, 0.153 and 0.205 for bitter-tasting dipeptides, respectively. By equally dividing the two datasets into training and test sets by D-optimal, the 3D-HoVAIF approach undergoes rigorous statistical validation. Furthermore, the superior performance of 3D-HoVAIF is confirmed in comparison with two other peptide MSC approaches referring to z-scale and ISA-ECI. For 58 ACE inhibitors, the GA-PLS model yields two principal components, with the following statistics: r(2) = 0.893, q(2) = 0.824, RMSEE = 0.349, RMSCV = 0.425, q2(ext) = 0.739, r2(ext)= 0.784, r2(0.ext) = 0.781, rf2(0.ext) = 0.77, k = 0.962, k' = 1.019, and RMSEP = 0.460; for 48 bitter-tasting dipeptides, three principal components resulted, with the statistics as: r(2) = 0.950, q(2) = 0.893, RMSEE = 0.152, RMSCV = 0.222, q2(ext)= 0.875, r2(ext) = 0.919, r2(0.ext)= 0.919, rf2(0.ext)= 0.919, k = 1.018, k' = 0.974, and RMSEP = 0.198. In addition, the relationship of ACE-inhibiting activities with bitter-tasting thresholds has been investigated by applying the above-constructed models to predictions on 400 theoretically possible dipeptides. Through analysis, the ACE-inhibiting activities are found to be prominently related to bitter-tasting intensities. Thus, it is deemed to be difficult to find such dipeptides that simultaneously satisfy pharmacodynamic action (high ACE-inhibiting activities) and comfortable tastes, suggesting that active components of dipeptides that are served as functional food to lower blood pressure are not very ideal.

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED)

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.

A new set of amino acid descriptors and its application in peptide QSARs

In this work, a new set of amino acid descriptors, i.e., VHSE (principal components score Vectors of Hydrophobic, Steric, and Electronic properties), is derived from principal components analysis (PCA) on independent families of 18 hydrophobic properties, 17 steric properties, and 15 electronic properties, respectively, which are included in total 50 physicochemical variables of 20 coded amino acids. Using the stepwise multiple regression (SMR) method combined with partial least squares (PLS), the VHSE scales are then applied to QSAR studies of bitter-tasting dipeptides (BTD), angiotensin-converting enzyme (ACE) inhibitors, and bradykinin-potentiating pentapeptides (BPP). To validate the predictive power of resulting models, external validation are also performed. A comparison of the results to those obtained with z scores and other two-dimensional (2D) or three-dimensional(3D) descriptors shows that the VHSE scales are comparable for parameterizing the structural variability of the peptide series.