Machine Learning Advances in Predicting Peptide/Protein‐Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Abstract

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high‐throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide–protein interactions (PepPIs) and protein–protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning

“…For this reason, researchers have increasingly turned to machine learning approaches to predict interactions, with the goal of accelerating the screening process [13][14][15][16][17]. In a recent review by Ye et al [18] these methods have been categorized into five main groups: Linear-based, including linear regression and logistic regression; Tree-based, including decision tree, random forest, and gradient boosting machines; Kernel-based, including radial basis function, linear discriminate analysis, and support vector machine; Neuralnetwork-based, including convolutional neural-networks, recurrent neural networks and generative adversarial networks; and Attention-mechanism-based, which includes transformers and BERT.…”

Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline

“…As a cheminformatics model, ML combines chemistry, computer science, and information technology to aid in drug discovery through tasks like virtual screening, library design, and high-throughput screening analysis [10][11][12]. Machine learning algorithms leverage large chemical datasets for predictive modeling and pattern recognition, including the prediction of the properties and activities of peptides based on their sidechains [13][14][15][16]. This integration has accelerated the discovery and design of novel peptides with desired biological activities, opening new avenues for peptide-based drug development.…”