The MCPep server (http://bental.tau.ac.il/MCPep/) is designed for non-experts wishing to perform Monte Carlo (MC) simulations of helical peptides in association with lipid membranes. MCPep is a web implementation of a previously developed MC simulation model. The model has been tested on a variety of peptides and protein fragments. The simulations successfully reproduced available empirical data and provided new molecular insights, such as the preferred locations of peptides in the membrane and the contribution of individual amino acids to membrane association. MCPep simulates the peptide in the aqueous phase and membrane environments, both described implicitly. In the former, the peptide is subjected solely to internal conformational changes, and in the latter, each MC cycle includes additional external rigid body rotational and translational motions to allow the peptide to change its location in the membrane. The server can explore the interaction of helical peptides of any amino-acid composition with membranes of various lipid compositions. Given the peptide's sequence or structure and the natural width and surface charge of the membrane, MCPep reports the main determinants of peptide-membrane interactions, e.g. average location and orientation in the membrane, free energy of membrane association and the peptide's helical content. Snapshots of example simulations are also provided.
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http://dx.doi.org/10.1093/nar/gks577 | DOI Listing |
Sci Rep
December 2024
Clermont Auvergne University, CNRS, IRD, OPGC, Magmas and Volcanoes Laboratory, 63000, Clermont-Ferrand, France.
The new submarine volcano Fani Maoré offshore Mayotte (Comoros archipelago) discovered in 2019 has raised the awareness of a possible future eruption in Petite-Terre island, located on the same 60 km-long volcanic chain. In this context of a renewal of the volcanic activity, we present here the first volcanic hazard assessment in Mayotte, focusing on the potential reactivation of the Petite-Terre eruptive centers. Using the 2-D tephra dispersal model HAZMAP and the 1979 - 2021 meteorological ERA-5 database, we first identify single eruptive scenarios of various impacts for the population of Mayotte.
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December 2024
Bioinformatics Laboratory, College of Computing, University Mohammed VI Polytechnic, Ben Guerir, Morocco.
Hepatitis C virus (HCV) presents a significant global health issue due to its widespread prevalence and the absence of a reliable vaccine for prevention. While significant progress has been achieved in therapeutic interventions since the disease was first identified, its resurgence underscores the need for innovative strategies to combat it. The nonstructural protein NS5A is crucial in the life cycle of the HCV, serving as a significant factor in both viral replication and assembly processes.
View Article and Find Full Text PDFAdv Mater
December 2024
Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
Magnetoplumbites are one of the most broadly studied families of hexagonal ferrites, typically with high magnetic ordering temperatures, making them excellent candidates for permanent magnets. However, magnetic frustration is rarely observed in magnetoplumbites. Herein, the discovery, synthesis, and characterization of the first Mn-based magnetoplumbite, as well as the first magnetoplumbite involving pnictogens (Sb), ASbMnO (A = K or Rb) are reported.
View Article and Find Full Text PDFAIP Adv
December 2024
Center for Natural Sciences, University of Pannonia, Egyetem u. 10, Veszprém 8200, Hungary.
We present simulation results for the Donnan equilibrium between a homogeneous bulk reservoir and inhomogeneous confining geometries with varying number of restricted dimensions, . Planar slits ( = 1), cylindrical pores ( = 2), and spherical cavities ( = 3) are considered. The walls have a negative surface charge density.
View Article and Find Full Text PDFACS Cent Sci
December 2024
Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172 Mestre, Italy.
Computational generation of cyclic peptide inhibitors using machine learning models requires large size training data sets often difficult to generate experimentally. Here we demonstrated that sequential combination of Random Forest Regression with the pseudolikelihood maximization Direct Coupling Analysis method and Monte Carlo simulation can effectively enhance the design pipeline of cyclic peptide inhibitors of a tumor-associated protease even for small experimental data sets. Further studies showed that such -evolved cyclic peptides are more potent than the best peptide inhibitors previously developed to this target.
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