Machine learning assisted design of highly active peptides for drug discovery

Abstract

The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning approaches can greatly assist in the process and even partly replace expensive laboratory experiments by learning a predictor with existing data or with a smaller amount of data generation. Unfortunately, once the model is learned, selecting peptides having the greatest predicted bioactivity often requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate solutions. We focused on recent advances in kernel methods and machine learning to learn a predictive model with proven success. For this type of model, we propose an efficient algorithm based on graph theory, that is guaranteed to find the peptides for which the model predicts maximal bioactivity. We also present a second algorithm capable of sorting the peptides of maximal bioactivity. Extensive analyses demonstrate how these algorithms can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic antimicrobial peptides. Source code freely available at http://graal.ift.ulaval.ca/peptide-design/.

Figure 1. Illustration of the 3-partite graph G ^h_y with k = 3 and a two letters alphabet .

In this graph, every source-sink path represent a peptide of size 5 (l = n + k − 1) based on the alphabet {A, B}.

Figure 2. Iterative process for the design of peptide ligands.

Figure 3. The 100,000 peptides with highest antimicrobial activity found by the K-longest path algorithm.

Figure 4. Correlation coefficient of h _random predictions on the CAMPs data while varying R, the number of random peptides used as training set.

Figure 5. CAMP bioactivity motifs.

Top motif: the best 1,000 peptides obtained from the oracle. Middle motif: the best 1,000 peptides obtained from h _random. Bottom motif: the best 1,000 out of 1,000,000 random peptides.