Three-dimensional quantitative structure–activity relationship for several bioactive peptides searched by a convex hull–comparative molecular field analysis approach
Introduction
Three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods are valuable tools that one can use to improve the quality of some preliminary drug designing models. It is known that molecules of different structures but presenting similar biological activity are difficult to superimpose on each other using the conventional atom-by-atom fit (Dammkoehler et al., 1989; Folkers et al., 1993; Dunn et al., 1998; Klebe, 1998). In such cases, 3D QSAR methods can help to find conformations that can be fitted to a certain pattern known as important for affinity (Folkers et al., 1993; Klebe, 1998). The comparative molecular field analysis (CoMFA) method (Cramer et al., 1988a, Cramer et al., 1988b) is emerging as a popular 3D QSAR method. The interactions of molecules at lattice points outside the molecules are computed and analyzed in the method (Cramer et al., 1988a, Cramer et al., 1988b). The success of a CoMFA model is strongly dependent on how the molecules are aligned in the lattice on which both the steric and electrostatic fields are sampled. Structures are usually aligned against a common template using a fitting algorithm that minimizes the root-mean-square (RMS) distances between specified pairs of atoms. The CoMFA method can also be used to screen bioactive conformations for highly flexible molecules (Waller and Marshall, 1993). This approach requires deriving as many CoMFA models as there are plausible conformations that exist and picking the one that has the best predictive power. In deriving a CoMFA model, it is believed that obtaining the self-consistency of an alignment is much more important than knowledge on active site geometry (Cramer et al., 1988a, Cramer et al., 1988b; Martin et al., 1996).
In this report, we have used a 3D convex hull computation algorithm developed in-house (Lin et al., 1998) to detect the commonly exposed groups for structures of some bioactive tachykinin peptides generated from molecular dynamics (MD) simulations. We treated the commonly exposed groups determined as sets of correspondences for aligning the structures generated using the sybyl 6.6 program (Tripos, 1999). The number of peptide compounds studied is 18. The peptides are divided into a training set and a test set; the former contains 14 peptides and the latter contains four peptides. The peptide structures generated in each training set are also randomly permuted and analyzed by the CoMFA program (Cramer et al., 1988a, Cramer et al., 1988b) to show that the CoMFA statistics obtained for structures aligned by the convex hull alignment rules are not chance statistics. Several of these aligned training sets were also analyzed with the comparative molecular similarity indices analysis (CoMSIA) module (Klebe, 1998) within the sybyl 6.6 package (Tripos, 1999). It is found that while the CoMSIA statistics computed for indices of steric, electrostatic, and hydrophobic effects are correlated well with those of the corresponding CoMFA result, those computed for indices of donor and acceptor are not. The CoMSIA result provides the detailed contribution of each of the correlated fields to the total CoMFA statistics after the structural alignment. We also demonstrate that the CoMFA results can be improved using distance constraints derived from vertices of 3D convex hulls computed for structures of some of the aligned training sets. Since the most important task in CoMFA is to align all the structures in a structural set against a common template, we attempt to adjust the conformation of each structure in the set using some distance constraints so that it will be better correlated with the structure of the common template. Each of the adjusted conformations is generated through a constraint energy minimization and then a constraint MD simulation run using the distance constraints derived from the convex hull vertices determined. We find that the CoMFA result is improved when a second CoMFA is performed on a set of structures with adjusted conformations generated and aligned using the same set of commonly exposed groups on which the distance constraints are derived as the set of correspondence.
Section snippets
Materials and methods
Tachykinins are a family of deca-, undeca-, dodeca- and octadeca-peptides that share a common C-terminal sequence, Phe–x–Gly–Leu–Met–NH2 (x=Phe, Tyr, Ile or Val) (Dutta, 1993). The three mammalian tachykinins, substance P (SP), neurokinin A (NKA), and neurokinin B (NKB), were isolated and all of them could lower blood pressure, stimulate isolated smooth muscle preparations and cause salivary secretion (Dutta, 1993). The C-terminal amide group of all of these peptides is important for biological
Results and discussion
Since the N-terminal residues of peptides SP, NKA, and NKB are not required for biological activity (Dutta, 1993), most of the modifications or cyclic analogues are prepared for residues that are close to the C-terminus of these peptides (Table 1 and Fig. 1). Most of these peptides are very flexible since the difference in binding activity between some of them is large, and yet the sequence diversity between them is small. One way of handling such a highly flexible system is to derive many
Conclusion
Several rational approaches have been proposed to choose a representative conformation from a vast conformation space for matching flexible molecules (Folkers et al., 1993; Klebe, 1998). We have shown here that the convex hull alignment rule can be used to screen conformations of structures of several peptides that are better correlated with the corresponding bioactivity measured. The best cross-validated r2 obtained using the convex hull alignment rule can be improved if more structural sets
Acknowledgements
This work is supported in part by a grant from the National Science Council, ROC (NSC 88-2213-E-007-023).
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2019, Food Research InternationalCitation Excerpt :To illustrate the correlation between biological activity and chemical structure, three-dimensional quantitative structure-activity relationship (3D-QSAR) was analyzed by comparative molecular field analysis (CoMFA) using SYBYL-X 2.1.1 software (Tripos, Inc., St. Louis, MO, USA). CoMFA 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 (Iwaniak, Minkiewicz, Darewicz, & Hrynkiewicz, 2016; Lin & Lin, 2001; Malik et al., 2017). 3D-QSAR models constructed by CoMFA have successfully described the structure-activity relationships of antimicrobial, ACE-inhibitory, bitter-tasting and other bioactivity peptides (Vukic et al., 2017; Wu et al., 2014).
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