Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy

Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality

The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.

Bacteriophages in nature: recent advances in research tools and diverse environmental and biotechnological applications

Bacteriophages infect and replicate within bacteria and play a key role in the environment, particularly in microbial ecosystems and bacterial population dynamics. The increasing recognition of their significance stems from their wide array of environmental and biotechnological uses, which encompass the mounting issue of antimicrobial resistance (AMR). Beyond their therapeutic potential in combating antibiotic-resistant infections, bacteriophages also find vast applications such as water quality monitoring, bioremediation, and nutrient cycling within environmental sciences. Researchers are actively involved in isolating and characterizing bacteriophages from different natural sources to explore their applications. Gaining insights into key aspects such as the life cycle of bacteriophages, their host range, immune interactions, and physical stability is vital to enhance their application potential. The establishment of diverse phage libraries has become indispensable to facilitate their wide-ranging uses. Consequently, numerous protocols, ranging from traditional to cutting-edge techniques, have been developed for the isolation, detection, purification, and characterization of bacteriophages from diverse environmental sources. This review offers an exploration of tools, delves into the methods of isolation, characterization, and the extensive environmental applications of bacteriophages, particularly in areas like water quality assessment, the food sector, therapeutic interventions, and the phage therapy in various infections and diseases.

Artificial intelligence and the analysis of cryo-EM data provide structural insight into the molecular mechanisms underlying LN-lamininopathies

Laminins (Lm) are major components of basement membranes (BM), which polymerize to form a planar lattice on cell surface. Genetic alternations of Lm affect their oligomerization patterns and lead to failures in BM assembly manifesting in a group of human disorders collectively defined as Lm N-terminal domain lamininopathies (LN-lamininopathies). We have employed a recently determined cryo-EM structure of the Lm polymer node, the basic repeating unit of the Lm lattice, along with structure prediction and modeling to systematically analyze structures of twenty-three pathogenic Lm polymer nodes implicated in human disease. Our analysis provides the detailed mechanistic explanation how Lm mutations lead to failures in Lm polymerization underlining LN-lamininopathies. We propose the new categorization scheme of LN-lamininopathies based on the insight gained from the structural analysis. Our results can help to facilitate rational drug design aiming in the treatment of Lm deficiencies.

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.

Cryo-EM reveals the molecular basis oflaminin polymerization and LN-lamininopathies

Laminin polymerization is the major step in basement membranes assembly. Its failures cause laminin N-terminal domain lamininopathies including Pierson syndrome. We have employed cryo-electron microscopy to determine a 3.7 Å structure of the trimeric laminin polymer node containing α1, β1 and γ1 subunits. The structure reveals the molecular basis of calcium-dependent formation of laminin lattice, and provides insights into polymerization defects manifesting in human disease.