Computational peptide discovery with a genetic programming approach

Abstract

The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POET ${}_{\mathrm{Regex}}$ , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

Keywords: CEST MRI; Contrast agent; Evolutionary algorithm; Genetic programming; Peptide discovery; Regular expressions.

Fig. 1

Classical evolutionary cycle of a GP algorithm

Fig. 2

a Representation of an individual (a protein-function model) as a list of rules with 3 columns (ID, regular expression pattern and weight). An example (RE3) is represented as a built-in list structure in Python, where a parent node i has 2 children: (i*2)+1 and (i*2)+2. b Representation of RE3 as a binary tree. The yellow node is the root, grey nodes are the internal nodes and green nodes are the leaves. The small dotted nodes with red numbers are unexpressed nodes represented by “None”

Fig. 3

Representation of the one-point crossover: a part of parent 1 is merged with a part of parent 2 to produce offspring

Fig. 4

Representation of each type of mutation. a Addition of a new rule in the list of rules. b Replacement of a rule by a new rule. c Deletion of an existing rule in the list of rules. d Replacement of a branch of the tree. e Exchange of a node. f Deletion of a subtree. g Add one or more AAs to a leaf

Fig. 5

Average pairwise sequence identity in the dataset in percent, with [i–j] indicating values from i (included) to j (excluded)

Fig. 6

a Frequency of occurrence of each AA in both training (blue) and test (orange) sets. Molecules are illustrated for the four most prevalent AAs in the training set, and hydroxyl or amine groups are highlighted. b Comparison of the frequency of each AA in our dataset (yellow) and in the UniProtKB/Swiss-Prot database (green). The different values represent the percentage of occurrence. c Potential CEST value associated with each AA by occurrence method. The green box represents positively charged AAs, and the red box represents negatively charged AAs. d) Frequency of the 20 most observed motifs (size 2 to 6) in the training set with the associated CEST value

Fig. 7

a Comparison of POET_Regex (blue) and POET_Rdm (purple) models on the test set. b Performance of the best POET_Rdm model on the training set (orange) and the test set (green). The translucent bands around the regression line represent the confidence interval for the regression estimate

Fig. 8

a Performance of the best POET_Regex model on the training set (orange) and on the test set (green). The strong correlation indicates that the algorithm has converged to a good solution. The translucent bands around the regression line represent the confidence interval for the regression estimate. b Evolution of the fitness value during the evolutionary process. The green curve represents the fitness value of the best individual, and the orange curve represents the fitness value of the entire population

Fig. 9

The 9 best POET models. Each dot represents a datapoint with a true CEST value associated with a predicted CEST value. The green line represents the regression line and the translucent bands around the regression line represent the confidence interval for the regression estimate

Fig. 10

a Number of AAs present in the predicted peptides in the 3 types of DE experiments: 1000 (blue), 100 (orange) and 10 (green) cycles. b Sequence logos highlighting the probability of each AA at a given position, for the 3 experiments. As the number of cycles increases, the predicted peptides are more similar with high rates of lysine and leucine. The polar AAs are in green, the neutral in purple, the positively charged in blue, the negatively charged in red and the hydrophobic in black

Fig. 11

MTR_asym plot of nine peptides and the gold standard peptide (K12) measured by NMR