Protein–Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques. These methods not only improve predictive accuracy but also provide insights into functional sites, such as binding and catalytic residues. However, challenges such as limited high-resolution structural data and the need for effective negative sampling persist. Through the integration of experimental and computational tools, structure-based prediction paves the way for comprehensive proteomic network analysis, holding promise for advancements in drug discovery, biomarker identification, and personalized medicine. Future directions include enhancing scalability and dataset reliability to expand these approaches across diverse proteomes.

Cardiometabolic diseases (CVDs) are the leading cause of premature mortality and disability worldwide, arising from of cardiovascular and metabolic dysregulation. This review focuses on six critical therapeutic targets established in cardiometabolic regulation: GLP-1R, GIPR, FGFR1/β-Klotho, PCSK9, NF-κB, and the NLRP3 inflammasome. Drawing on curated structural datasets, we analyze the mechanisms of action and map key binding domain features that govern ligand efficacy and specificity. Dual GLP-1R/GIPR agonists, such as tirzepatide, demonstrate superior outcomes in glycemic control and weight reduction. Concurrently, inhibiting PCSK9, NF-κB, and NLRP3 helps to lower cholesterol and reduce harmful inflammation, offering cardioprotection. Structural analysis across these targets reveals complementary motifs (aromatic, hydrophobic, and polar residues). These insights guide the rational design of next-generation multi-target ligands (molecules capable of modulating two or more biological targets involved in related disease pathways, producing integrated therapeutic effects). Such integrated agents are promising for providing combined cardiovascular and metabolic benefits, thus reducing the risks associated with complex therapeutic drug combinations.

Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein–protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome.

The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome’s influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein–protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial–human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study’s findings contribute to the understanding of the intricate gut microbiome–host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay.

The potential of targeting the Renin-Angiotensin-Aldosterone System (RAAS) as a treatment for the coronavirus disease 2019 (COVID-19) is currently under investigation. One way to combat this disease involves the repurposing of angiotensin receptor blockers (ARBs), which are antihypertensive drugs, because they bind to angiotensin-converting enzyme 2 (ACE2), which in turn interacts with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. However, there has been no in silico analysis of the potential toxicity risks associated with the use of these drugs for the treatment of COVID-19. To address this, a network-based bioinformatics methodology was used to investigate the potential side effects of known Food and Drug Administration (FDA)-approved antihypertensive drugs, Sartans. This involved identifying the human proteins targeted by these drugs, their first neighbors, and any drugs that bind to them using publicly available experimentally supported data, and subsequently constructing proteomes and protein–drug interactomes. This methodology was also applied to Pfizer’s Paxlovid, an antiviral drug approved by the FDA for emergency use in mild-to-moderate COVID-19 treatment. The study compares the results for both drug categories and examines the potential for off-target effects, undesirable involvement in various biological processes and diseases, possible drug interactions, and the potential reduction in drug efficiency resulting from proteoform identification.

Colorectal cancer (CRC) pathogenesis is driven by complex protein–protein interactions (PPIs) between the host and the gut microbiome, yet these molecular dialogs remain largely unmapped. This study utilizes a Deep Learning framework, enhanced by protein structure embeddings, to predict approximately 8.9 billion interspecies PPIs from clinical metagenomic data. The model achieved high accuracy with an AUROC of 0.9960, identifying a high-confidence interactome representing roughly 16% of evaluated protein pairs. Phenotype-specific analysis revealed that while microbial hubs shift—transitioning from metabolic enzymes in healthy states to transport and regulatory proteins in CRC—the primary human targets remain remarkably consistent across both cohorts. These core human interactors are predominantly metalloproteins and regulators of ubiquitination, apoptosis, and zinc transport, suggesting these pathways are primary focal points for microbial manipulation regardless of disease state. Furthermore, co-occurring bacterial genera exhibit over 99% overlap in host target profiles, indicating significant functional redundancy in microbial engagement with the host. These findings suggest that CRC probably arises from network-level perturbations of stable host signaling hubs, offering a blueprint for identifying novel therapeutic targets and biomarkers.