QSAR For Structural Optimization And Design Of Functional Peptides

Peptides as a significant kind of substance which are able to maintain the normaloperation of organisms attract more and more attentions of modern people, andespecially for their phamaceutical value. What’s more, in recent years, peptides haveincreasing application value in material field. However, some defects of peptides bothembody in their structures and functions. Hence optimizing and modifying thestructures of peptides are conducive to designing more rational peptide compounds.In this paper, we cooperated2D-QSAR with3D-QSAR to research in depth onopioid peptides and amyloid peptides. We mainly adopted multiple linearregression(MLR), support vector machine(SVM), Topomer CoMFA and TopomerSearch as well as molecule docking to comprehensively analyze the quantitativestructure-activity relationships and mechanism of actions of the two peptides. On thebasis of it, we further optimized the structures of peptides and designed novelcompounds which have more optimal functions and more rational structures. In addition,we integrated mocular dynamics(MD) with quantum chemistry to evaluate thosedesigned molecules. The achievements in this paper are given below：①We employed Topomer CoMFA to build a3D-QSAR model for30opioidhexapeptides based on the R-group technology. The optimal multiple correlationcoefficients by fitting modeling and cross validation were0.892and0.596, respectively.Based on the builded model，we designed215delta opioid peptidomimetics modified bynon-natural amino acids by combining Topomer Search. And the predicted activities ofthe214peptidomimetics outperformed the template molecule. The Surflex-Dockmethod was applied to probe the key binding sites between the ligands and the receptor,and the corresponding interaction energies were further determined by density functiontheory. The results indicated that the third amino acid of the delta opioid peptidessignificantly contributes to their bioactivities. The Lys108, Asp128, Tyr129, Lys214,Val217, Val218, Trp284, Leu300, Ile304and Tyr308of the delta opioid receptor wereimportant for its ligand selectivity and activity. The result of the intercation energiesshowed His278may closed relate to the activity of the delta opioid peptides.②We used descriptors of factor analysis scale of generalized amino acidinformation(FASGAI) to characterize the sequential structures of30delta opioidhexapeptides and combined with stepwise multiple regression(SMR) to screen variables. As a result, we constructed a MLR model and partial least squares(PLS) model.Through comparison, the MLR model with8variables had the optimal result and theoptimal multiple correlation coefficient by fitting modeling and cross validation were0.891and0.756, respectively. Based on established model, we designed30delta opioidpeptides with potential higher activities. We investigated the action mode of opioidpeptides with the receptor of opioid peptides through Surflex-Dock. In this part of paper,we made a progress to demonstrate the more significant effects that the third amino acidof N terminal on activities, and the fifth and the sixth amino acid had a relative lowerinfluence on activities. The result showed that the desinged molecules by using2Dmethod and3D method respectively had the same selective acting sites on the receptor.③We employed descriptors of FASGAI to characterize the sequential structures of30delta opioid hexapeptides and combined with RBF-SVM to build the predictedmodel of the amyloid peptides. The accuracy was74.37%through a fivefold crossvalidation and the area was0.759under the receiver operating characteristic curve. Thestructure-activity relationship of amyloid peptides indicated that the hydrophobicity andelectronic property of amino acids played key roles in the occurrence process of peptideaggregation. We used a6-residue slide window to scan sequences of β-amyloid peptidewith42amino acids (Aβ42) and human islet amyloid polypeptide with37amino acids(hIAPP37). By comparison, the hexapeptides which can form aggregation identified bythe model obtained were agreement with experimental observations. Based onestablished model, we designed558potential amyloid hexapeptides. Then we employedmolecule dynamics, quantum chemistry and Circular Dichroism to validate theaggregation ability of the sequence GGVVIA of the Aβ42and the previous designed8hexapeptides. The results indicated that the peptide stacking which had the dominantenergy, moderate size of the side chain and evenly energy distribute are conducive tohexapeptide aggregation. The designed hexapeptide AEFRHD may be aggregation well.