Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding.
Results: The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25–40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding.
The authors applied both a DNN and a HONN in order to compare the two. See image below.
As it relates to PPI:
In this article, we propose novel machine learning methods to study a specific type of peptide-protein interaction, i.e. the interaction between peptides and major histocompatibility complex class I (MHC I) proteins, although our methods can be readily applicable to other types of peptide-protein interactions.
https://doi.org/10.1093/bioinformatics/btv371
The authors applied both a DNN and a HONN in order to compare the two. See image below.
As it relates to PPI: