The human touch: Utilizing AlphaFold 3 to analyze structures of endogenous metabolons

The biomechanical phenomena observed in the cell invasion pathway of porcine epidemic diarrhea virus: a review

Porcine epidemic diarrhea virus (PEDV) is the primary pathogen responsible for highly contagious intestinal infections in pigs, which results in significant economic losses to the global animal husbandry industry. PEDV is an enveloped virus that enters cells via endocytosis, a process that is dependent on the binding of the viral surface S protein to a receptor on the host cell membrane. This results in a series of biomechanical alterations that drive the fusion of the viral and host cell membranes. These alterations stabilise the binding of the virus to the receptor and also affect the tension and the curvature of the plasma membrane and the formation of endocytic vesicles. A comprehensive understanding of the mechanism by which PEDV enters cells and the biomechanical changes that accompany this process is of paramount importance for the development of PEDV inhibitors, vaccines, and disease prevention and control strategies. Here, we review the general mechanism of PEDV entry, the biomechanical phenomena that occur during endocytosis, and the potential applications of biomechanics in antiviral therapy. It is anticipated that by gaining insight into these mechanisms, novel approaches to regulating viral entry pathways through mechanical interference, vaccine development, and antiviral drug design can be explored.

AlphaFold3: An Overview of Applications and Performance Insights

AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability to model not only single protein structures but also complex biomolecular interactions, including protein-protein interactions, protein-ligand docking, and protein-nucleic acid complexes. Herein, we provide a detailed examination of AlphaFold3's capabilities, emphasizing its applications across diverse biological fields and its effectiveness in complex biological systems. The strengths of the new AI model are also highlighted, including its ability to predict protein structures in dynamic systems, multi-chain assemblies, and complicated biomolecular complexes that were previously challenging to depict. We explore its role in advancing drug discovery, epitope prediction, and the study of disease-related mutations. Despite its significant improvements, the present review also addresses ongoing obstacles, particularly in modeling disordered regions, alternative protein folds, and multi-state conformations. The limitations and future directions of AlphaFold3 are discussed as well, with an emphasis on its potential integration with experimental techniques to further refine predictions. Lastly, the work underscores the transformative contribution of the new model to computational biology, providing new insights into molecular interactions and revolutionizing the fields of accelerated drug design and genomic research.

Emerging approaches to investigating functional protein dynamics in modular redox enzymes: Nitric oxide synthase as a model system

Approximately 80% of eukaryotic and 65% of prokaryotic proteins are composed of multiple folding units (i.e., domains) connected by flexible linkers. These dynamic protein architectures enable diverse, essential functions such as electron transfer, respiration, and biosynthesis. This review critically assesses recent advancements in methods for studying protein dynamics, with a particular focus on modular, multidomain nitric oxide synthase (NOS) enzymes. Moving beyond traditional static "snapshots" of protein structures, current research emphasizes the dynamic nature of proteins, viewing them as flexible architectures modulated by conformational changes and interactions. In this context, the review discusses key developments in the integration of quantitative crosslinking mass spectrometry (qXL MS) with AlphaFold 2 predictions, which provides a powerful approach to disentangling NOS structural dynamics and understanding their modulation by external regulatory cues. Additionally, advances in site-specific infrared (IR) spectroscopy offer exciting potential in providing rich details about the conformational dynamics of NOSs in docked states. Moreover, optimization of genetic code expansion machinery enables the generation of genuine phosphorylated NOS enzymes, paving the way for detailed biophysical and functional analyses of phosphorylation's role in shaping NOS activity and structural flexibility; notably, this approach also empowers site-specific IR probe labeling with cyano groups. By embracing and leveraging AI-driven tools like AlphaFold 2 for structural and conformational modeling, alongside solution-based biophysical methods such as qXL MS and site-specific IR spectroscopy, researchers will gain integrative insights into functional protein dynamics. Collectively, these breakthroughs highlight the transformative potential of modern approaches in driving fundamental biological chemistry research.

Supplying LSD1 with FAD in pancreatic cancer: A matter of protein-protein interaction?

Lysine-specific demethylase 1 (LSD1) is a key regulator in cancer epigenetic, and its activity is reliant on flavin adenine dinucleotide (FAD) as a cofactor. In this study, we investigated the correlation between LSD1 and FAD synthase isoform 2 (FADS2) protein levels in pancreatic ductal adenocarcinoma (PDAC) cell lines. We first assessed LSD1 protein and mRNA levels in mutant p53-expressing PANC-1 and MiaPaCa2 cells and p53-null AsPc-1 cells, compared to human pancreatic ductal epithelial (HPDE) controls. Our results confirmed elevated LSD1 protein levels in PANC-1 and MiaPaCa2, but not in AsPc-1, despite mRNA overexpression across all cell lines. Similarly, FADS2 levels were significantly upregulated in PANC-1 and MiaPaCa2, but not in AsPc-1, highlighting a possible link between FADS2 expression and p53 gain-of-function mutations. These results prompted us to better investigate the functional relationship between FADS2 and LSD1 by performing in cellulo protein-protein interaction analyses. Our results indicate a direct interaction between LSD1 and FADS2, while no significant interaction was observed between LSD1 and FADS1. These findings reinforce the role of FAD synthesis and its delivery to LSD1 as critical events in cancer progression and shed light on potential implications of FADS2-LSD1 dynamics as targeted therapies in cancer.