Systematic Modeling, Prediction, and Comparison of Domain–Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?

The protein–protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain–peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The Rprd2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

Development of Predictive Model for Helper T Lymphocyte Epitope Binding to HLADRB1* 01:01

Epitopes are essential peptides for immune system stimulation, such as governing helper T lymphocyte (HTL) activation via antigen presentation and recognition. Current predictive models for epitope selection mainly rely on the antigen presentation, although HTLs only recognize 50% of the presented peptides. Thus, we developed a HTL epitope predictor which involves the antigen recognition step. The predictor is specific for epitopes presented by Human Leukocyte Allele (HLA)-DRB1*01:01, which is protective against developing multiple sclerosis and association with autoimmune diseases. As the data set, we used binding register of immunogenic and non-immunogenic HTL peptides related to HLA-DRB1*01:01. The binding registers were obtained from consensus results of two current HLA-binder predictors. Amino acid descriptors were extracted from the binding registers and subjected to random forest algorithm. A threshold optimization were applied to overcome data set imbalance class. In addition, descriptors were screened by using a recursive feature elimination to enhance the model performance. The obtained model shows that the hydrophobicity, steric, and electrostatic properties of epitopes, mainly at center of binding registers, are important for the TCR recognition as well as the HTL epitopes predictive model. The model complements current HLA-DRB1*01:01-binder prediction methods to screen immunogenic HTL epitopes.

Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q2) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.

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.

The Study of Specificities of Interaction between Peptides and MHC Molecules

Determining which peptides bind to a specific major histocompatibility complex (MHC) class I molecule is not only helpful to understand the mechanism of immunity, but also to develop effective anti-tumor epitope vaccines. In order to further study the specificity of MHC class I molecule binding antigen peptide, the support vector regression (SVR) and four amino acid descriptors were used to build four models of predicting binding affinities between peptides and MHC class I molecules. Comparison among performances of the four models indicated that the model based on physicochemical properties of amino acids is more satisfying (AC=75.0%, CC=0.499). Furthermore, the specificities of MHC class I molecule binding antigen peptide were obtained through analysis based on the contribution of the amino acids to peptide-MHC class I molecule binding affinities in the predictive model.