The domain combination pair approach is employed to derive putative protein domain-domain interactions (DDI) from the protein-protein interactions (PPI) database DIP. The results of putative DDI are computed for seven species. To determine the prediction performance, putative DDI results are compared with that of the database InterDom, where an average matching ratio of about 76% can be achieved. Several real PPI pathways are reconstructed based on the predicted DDI results. It is found that the pathways could be reconstructed with reasonable accuracy. Furthermore, a novel quantity, so called AP-order index, is introduced to predict the regulatory order for six PPI pathways. It is found that the AP-order index is a very reliable parameter to determine the regulatory order of PPI.
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http://dx.doi.org/10.1016/j.compbiolchem.2007.10.002 | DOI Listing |
An unusual family of bifunctional terpene synthases has been discovered in which both catalytic domains - a prenyltransferase and a cyclase - are connected by a long, flexible linker. These enzymes are unique to fungi and catalyze the first committed steps in the biosynthesis of complex terpenoid natural products: the prenyltransferase assembles 5-carbon precursors to form C geranylgeranyl diphosphate (GGPP), and the cyclase converts GGPP into a polycyclic hydrocarbon product. Weak domain-domain interactions as well as linker flexibility render these enzymes refractory to crystallization and challenge their visualization by cryo-EM.
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December 2024
Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7FZ, UK. Electronic address:
Nuclear receptors (NRs) regulate gene expression in response to hormonal signals, influencing diverse physiological processes and diseases. Structural and dynamics investigations based on X-ray crystallography, cryo-electron microscopy (cryo-EM), hydrogen-deuterium exchange mass spectrometry, and molecular dynamics simulations, have significantly deepened our understanding of the conformational states, dynamics, and interdomain interactions of multi-domain NRs. Structural studies have examined heterodimeric complexes such as peroxisome proliferator-activated receptor gamma (PPAR-γ) with retinoid X receptor alpha (RXRα), liver X receptor beta (LXRβ) with RXRα, and retinoic acid receptor beta (RARβ) with RXRα, as well as homodimers like hepatic nuclear factor 4 alpha (HNF-4α), androgen receptor (AR), and glucocorticoid receptor (GR).
View Article and Find Full Text PDFChem Sci
December 2024
Department of Medicinal Chemistry, School of Pharmacy, Fudan University 826 Zhangheng Road Shanghai 201203 People's Republic of China
Protein-protein interactions are pivotal in numerous biological processes. The computational design of these interactions facilitates the creation of novel binding proteins, crucial for advancing biopharmaceutical products. With the evolution of artificial intelligence (AI), protein design tools have swiftly transitioned from scoring-function-based to AI-based models.
View Article and Find Full Text PDFNat Commun
November 2024
School of Computer Science, The University of Birmingham, Edgbaston, Birmingham, UK.
Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently.
View Article and Find Full Text PDFJ Leukoc Biol
October 2024
Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
The impact of aging on T cell subsets, specifically CD4+ and CD8+ T cells, leading to immune system dysfunction, has been the focus of scientific investigation due to its potential to reverse age-associated deterioration. Transcriptomic and epigenomic studies have identified the primary regulators in T cell aging. However, comprehending the underlying dynamic mechanisms requires studying these proteins with their interactors.
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