bims-lances 2024-01-28 papers

Issue of 2024‒01‒28
three papers selected by
James M. Krieger, National Centre for Biotechnology

Biophys J. 2024 Jan 23. pii: S0006-3495(24)00037-7. [Epub ahead of print]

Predictive Modeling and CryoEM: A Synergistic Approach to Modeling Macromolecular Structure.

Michael R Corum, Harikanth Venkanagari, Corey F Hryc, Matthew L Baker.

Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryoEM) and predictive modeling. Once relegated to low resolutions, single particle cryoEM is now capable of achieving near atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. While offering new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment and limitations when integrating predictive modeling with cryoEM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.

DOI: https://doi.org/10.1016/j.bpj.2024.01.021

Acta Crystallogr D Struct Biol. 2024 Feb 01.

A web-based dashboard for RELION metadata visualization.

Nayim González-Rodríguez, Emma Areán-Ulloa, Rafael Fernández-Leiro.

Cryo-electron microscopy (cryo-EM) has witnessed radical progress in the past decade, driven by developments in hardware and software. While current software packages include processing pipelines that simplify the image-processing workflow, they do not prioritize the in-depth analysis of crucial metadata, limiting troubleshooting for challenging data sets. The widely used RELION software package lacks a graphical native representation of the underlying metadata. Here, two web-based tools are introduced: relion_live.py, which offers real-time feedback on data collection, aiding swift decision-making during data acquisition, and relion_analyse.py, a graphical interface to represent RELION projects by plotting essential metadata including interactive data filtration and analysis. A useful script for estimating ice thickness and data quality during movie pre-processing is also presented. These tools empower researchers to analyse data efficiently and allow informed decisions during data collection and processing.

Keywords: RELION; cryo-electron microscopy; graphical user interface; ice thickness estimation; web-based cryo-EM tools

DOI: https://doi.org/10.1107/S2059798323010902

Mar Drugs. 2024 Jan 03. pii: 34. [Epub ahead of print]22(1):

Structural and Dynamical Basis of VP35-RBD Inhibition by Marine Fungi Compounds to Combat Marburg Virus Infection.

Abdullah S Alawam, Hadil Sultan Alawam, Mohammed Merae Alshahrani, Maher S Alwethaynani, Lina M Alneghery, Mubarak A Alamri.

The Marburg virus (MBV), a deadly pathogen, poses a serious threat to world health due to the lack of effective treatments, calling for an immediate search for targeted and efficient treatments. In this study, we focused on compounds originating from marine fungi in order to identify possible inhibitory compounds against the Marburg virus (MBV) VP35-RNA binding domain (VP35-RBD) using a computational approach. We started with a virtual screening procedure using the Lipinski filter as a guide. Based on their docking scores, 42 potential candidates were found. Four of these compounds-CMNPD17596, CMNPD22144, CMNPD25994, and CMNPD17598-as well as myricetin, the control compound, were chosen for re-docking analysis. Re-docking revealed that these particular compounds had a higher affinity for MBV VP35-RBD in comparison to the control. Analyzing the chemical interactions revealed unique binding properties for every compound, identified by a range of Pi-cation interactions and hydrogen bond types. We were able to learn more about the dynamic behaviors and stability of the protein-ligand complexes through a 200-nanosecond molecular dynamics simulation, as demonstrated by the compounds' consistent RMSD and RMSF values. The multidimensional nature of the data was clarified by the application of principal component analysis, which suggested stable conformations in the complexes with little modification. Further insight into the energy profiles and stability states of these complexes was also obtained by an examination of the free energy landscape. Our findings underscore the effectiveness of computational strategies in identifying and analyzing potential inhibitors for MBV VP35-RBD, offering promising paths for further experimental investigations and possible therapeutic development against the MBV.

Keywords: Marburg virus; RNA binding domain; VP35; marine fungi

DOI: https://doi.org/10.3390/md22010034