Background: Visualization of protein-ligand complex plays an important role in elaborating protein-ligand interactions and aiding novel drug design. Most existing web visualizers either rely on slow software rendering, or lack virtual reality support. The vital feature of macromolecular surface construction is also unavailable.
Results: We have developed iview, an easy-to-use interactive WebGL visualizer of protein-ligand complex. It exploits hardware acceleration rather than software rendering. It features three special effects in virtual reality settings, namely anaglyph, parallax barrier and oculus rift, resulting in visually appealing identification of intermolecular interactions. It supports four surface representations including Van der Waals surface, solvent excluded surface, solvent accessible surface and molecular surface. Moreover, based on the feature-rich version of iview, we have also developed a neat and tailor-made version specifically for our istar web platform for protein-ligand docking purpose. This demonstrates the excellent portability of iview.
Conclusions: Using innovative 3D techniques, we provide a user friendly visualizer that is not intended to compete with professional visualizers, but to enable easy accessibility and platform independence.
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http://dx.doi.org/10.1186/1471-2105-15-56 | DOI Listing |
Nat Chem
January 2025
Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Protein catalysis and allostery require the atomic-level orchestration and motion of residues and ligand, solvent and protein effector molecules. However, the ability to design protein activity through precise protein-solvent cooperative interactions has not yet been demonstrated. Here we report the design of 14 membrane receptors that catalyse G protein nucleotide exchange through diverse engineered allosteric pathways mediated by cooperative networks of intraprotein, protein-ligand and -solvent molecule interactions.
View Article and Find Full Text PDFCurr Opin Struct Biol
January 2025
Department of Biological Sciences, Seoul National University, Seoul 08826, Republic of Korea. Electronic address:
Proteome-scale interaction prediction is essential for understanding protein functions and disease mechanisms. Traditional experimental methods are often limited by scale and complexity, driving the need for computational approaches. Deep learning has emerged as a powerful tool, enabling high-throughput, accurate predictions of protein interactions.
View Article and Find Full Text PDFMinerva Dent Oral Sci
January 2025
Department of Biomedical Sciences, Dentistry and Morphological and Functional Imaging, University of Messina, Messina, Italy.
Background: Cadaverine and hydrocinnamic acid are frequent metabolites in inflamed periodontal areas. Their role as a metabolite for plant growth inhibition has been established, but their relevance in humans has yet to be determined. Moreover, Vascular endothelial growth factor (VGEF) is a consistent growth factor in neo-angiogenesis in periodontal regeneration.
View Article and Find Full Text PDFJ Cheminform
January 2025
Department of Mathematics, University of Tennessee, Knoxville, TN, 37996, USA.
Accurate prediction of ligand-receptor binding affinity is crucial in structure-based drug design, significantly impacting the development of effective drugs. Recent advances in machine learning (ML)-based scoring functions have improved these predictions, yet challenges remain in modeling complex molecular interactions. This study introduces the AGL-EAT-Score, a scoring function that integrates extended atom-type multiscale weighted colored subgraphs with algebraic graph theory.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande do Norte, Natal, 59064-741, RN, Brazil.
The COVID-19 pandemic caused by SARS-CoV-2 continues to pose a major challenge to global health. Targeting the main protease of the virus (Mpro), which is essential for viral replication and transcription, offers a promising approach for therapeutic intervention. In this study, advanced computational techniques such as molecular docking and molecular dynamics simulations were used to screen a series of antiviral compounds for their potential inhibitory effect on the SARS-CoV-2 Mpro.
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