Prediction of Cell Penetrating Peptides by Support Vector Machines

Abstract

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.

Viral and Other Cell-Penetrating Peptides as Vectors of Therapeutic Agents in Medicine

“…Although CPP-related data can be readily found in both literature and databases, information on their nonfunctional analogs is still scarce. What is worse, penetration assays are far from being standardized, and what appears as incapable of penetration in one cell line or experiment may turn out to be a CPP in the other (Sanders et al, 2011).…”

A novel entropy-based graph signature from the average mixing matrix

“…For the proposed method and the entropy-based graph complexity methods, we calculate the characterization values of graphs as features. We then evaluate the performance of the signatures in a graph classification task by performing 10-fold cross-validation using a Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) [20] and the Pearson VII Universal Kernel (PUK) [21]. For the BRWK and GCGK kernels, evaluate the classification performance by performing 10-fold cross-validation using a C-SVM associated with the kernel matrix.…”

Cell Penetrating Peptides

“…Since very few peptides have been experimentally validated as nonCPPs (negative examples), equal number of peptides (15-30 amino acids) were generated randomly from SwissProt proteins, and considered them as non-CPPs. This strategy for creating negative dataset has already been used in previous studies [22,25].…”

“…Sanders et al (2011) have developed a method for CPP prediction. In this study, they have used 111 experimentally validated CPPs and equal number of non-CPPs (generated randomly from the chicken proteome).…”

“…Most of these peptides are short (up to 35 amino acids), water soluble, partly hydrophobic, and/or polybasic in nature with a net positive charge at physiological pH [18]. In the past, few attempts have been made to develop computational methods for CPP prediction [19][20][21][22]. In 2008, Hansen et al developed a method, which involves a set of z-scales of 87 coded and noncoded amino acids published by Sandberg and his group [23].…”