Protein–Protein Interaction Prediction for Targeted Protein Degradation

Computational methods and key considerations for in silico design of proteolysis targeting chimera (PROTACs)

Proteolysis-targeting chimeras (PROTACs), as heterobifunctional molecules, have garnered significant attention for their ability to target previously undruggable proteins. Due to the challenges in obtaining crystal structures of PROTAC molecules in the ternary complex, a plethora of computational tools have been developed to aid in PROTAC design. These computational tools can be broadly classified into artificial intelligence (AI)-based or non-AI-based methods. This review aims to provide a comprehensive overview of the latest computational methods for the PROTAC design process, covering both AI and non-AI approaches, from protein selection to ternary complex modeling and prediction. Key considerations for in silico PROTAC design are discussed, along with additional considerations for deploying AI-based models. These considerations are intended to guide subsequent model development in the PROTAC design process. Finally, future directions and recommendations are provided.

Exercise ameliorates chronic inflammatory response induced by high-fat diet via Sestrin2 in an Nrf2-dependent manner

Chronic inflammation is a major contributor to the development of metabolic disorders and is commonly seen in studies of diet-induced obesity in humans and rodents. Exercise has been shown to have anti-inflammatory properties, though the exact mechanisms are still not fully understood. Sestrins and Nrf2 are of interest to researchers as they are known to protect against inflammation and oxidative stress. In this study, we aim to explore the interconnection between Sestrin2 (SESN2) and Nrf2 and their roles in exercise benefits on chronic inflammation. Our data showed that SESN2 knockout aggravated the abnormalities of body weight, fat mass, and serum lipid that were induced by a high-fat diet (HFD), and a concomitant increase of TNF-α, IL-1β and IL-6 in both serum and skeletal muscle. Notably, exercise was found to reverse these changes, and SESN2 was found to be necessary for exercise to reduce the inflammatory response in skeletal muscles, though not in serum. Immunoprecipitation and bioinformatics prediction experiments further revealed that SESN2 directly binds to Nrf2, indicating a protein-protein interaction between the two. Furthermore, our data demonstrated that SESN2 protein is necessary for exercise-induced effects on Nrf2 pathway in HFD-fed mice, and Nrf2 protein is necessary to enable SESN2 to reduce the inflammation caused by palmitic acid (PA)+ oleic acid (OA) treatment in vitro. Our findings indicate that exercise mitigates chronic inflammation induced by HFD through SESN2 in an Nrf2-dependent manner. Our study reveals a novel molecular mechanism whereby the SESN2/Nrf2 pathway mediates the positive impact of exercise on chronic inflammation.

Protein-protein interfaces in molecular glue-induced ternary complexes: classification, characterization, and prediction

Molecular glues are a class of small molecules that stabilize the interactions between proteins. Naturally occurring molecular glues are present in many areas of biology where they serve as central regulators of signaling pathways. Importantly, several clinical compounds act as molecular glue degraders that stabilize interactions between E3 ubiquitin ligases and target proteins, leading to their degradation. Molecular glues hold promise as a new generation of therapeutic agents, including those molecular glue degraders that can redirect the protein degradation machinery in a precise way. However, rational discovery of molecular glues is difficult in part due to the lack of understanding of the protein-protein interactions they stabilize. In this review, we summarize the structures of known molecular glue-induced ternary complexes and the interface properties. Detailed analysis shows different mechanisms of ternary structure formation. Additionally, we also review computational approaches for predicting protein-protein interfaces and highlight the promises and challenges. This information will ultimately help inform future approaches for rational molecular glue discovery.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.