Current opinion in structural biology最新文献

Biomolecular simulation can act as both a digital microscope and a crystal ball; offering the potential for a deeper understanding of experimental observations whilst also presenting a forward-looking avenue for the in silico design and evaluation of hitherto unsynthesized compounds. Indeed, as the intricacy of our scientific inquiries has grown, so too has the computational prowess we seek to deploy in our pursuit of answers. As we enter the Exascale era, this mini-review surveys the computational landscape from both the point of view of the development of new and ever more powerful systems, and the simulations that are run on them. Moreover, as we stand on the cusp of a transformative phase in computational biology, this article offers a contemplative glance into the future, speculating on the profound implications of artificial intelligence and quantum computing for large-scale biomolecular simulations.

The ionotropic glutamate receptors (iGluRs) are comprised of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA), N-methyl-d-aspartate receptor, kainate, and delta subtypes and are pivotal in neuronal plasticity. Recent structural studies on AMPA receptors reveal intricate conformational changes during activation and desensitization elucidating the steps from agonist binding to channel opening and desensitization. Additionally, interactions with auxiliary subunits, including transmembrane AMPA-receptor regulatory proteins, germ-cell-specific gene 1–like protein, and cornichon homologs, intricately modulate AMPA receptors. We discuss the recent high-resolution structures of these complexes that unveil stoichiometry, subunit positioning, and differences in specific side-chain interactions that influence these functional modulations.

Gram-negative bacteria and eukaryotic organelles of bacterial origin contain outer membrane proteins that possess a transmembrane “β-barrel” domain. The conserved β-barrel assembly machine (BAM) and the sorting and assembly machine (SAM) are required for the folding and membrane insertion of β-barrels in Gram-negative bacteria and mitochondria, respectively. Although the mechanisms by which β-barrels are folded are incompletely understood, advances in cryo-electron microscopy (cryo-EM) have recently yielded unprecedented insights into their folding process. Here we highlight recent studies that show that both bacterial and mitochondrial β-barrels fold via the formation of remarkable “hybrid-barrel” intermediate states during their interaction with the folding machinery. We discuss how these results align with a general model of β-barrel folding.

Recent advances in molecular dynamics (MD) simulations have led to rapid improvement in our understanding of the molecular details of the outer membranes (OMs) of Gram-negative bacteria. In this review, we highlight the latest discoveries from MD simulations of OMs, shedding light on the dynamic nature of these bacteria's first line of defense. With the focus on cutting-edge approaches, we explore the OM's sensitivity to structural features, including divalent cations and membrane composition, which have emerged as crucial determinants of antimicrobial passage. Additionally, studies have provided novel insights into outer-membrane proteins (OMPs), revealing their intricate roles in substrate translocation and their distinct interactions with lipopolysaccharides (LPS) in the OM. Finally, we explore the challenging process of β-barrel membrane protein insertion, showcasing recent findings that have enhanced our grasp of this fundamental biological phenomenon.

Determining how chromatin is structured in the nucleus is critical to studying its role in gene regulation. Recent advances in the analysis of single-cell chromatin architecture have considerably improved our understanding of cell-type-specific chromosome conformation and nuclear architecture. In this review, we discuss the methods used for analysis of 3D chromatin conformation, including sequencing-based methods, imaging-based techniques, and computational approaches. We further review the application of these methods in the study of the role of chromatin topology in neural development and disorders.

Knowledge of the structure and dynamics of biomolecules is key to understanding the mechanisms underlying their biological functions. Single-particle cryo-electron microscopy (cryo-EM) is a powerful structural biology technique to characterize complex biomolecular systems. Here, we review recent advances of how Molecular Dynamics (MD) simulations are being used to increase and enhance the information extracted from cryo-EM experiments. We will particularly focus on the physics underlying these experiments, how MD facilitates structure refinement, in particular for heterogeneous and non-isotropic resolution, and how thermodynamic and kinetic information can be extracted from cryo-EM data.

In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these articles under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the articles deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.

Understanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource. Network analysis built on Molecular Dynamics simulation data is particularly suited in this respect and is gradually becoming of routine use. Herein, we collect the recent literature in the field, discussing different aspects and available options for network construction and analysis. We further highlight interesting refinements and extensions, eventually providing our perspective on this topic.

Specimen preparation is a critical but challenging step in high-resolution cryogenic electron microscopy (cryo-EM) structural analysis of macromolecules. In the past decade, graphene has gained much recognition as the supporting substrate to optimize cryo-EM specimen preparation. It improves macromolecule embedding in ice, reduces beam-induced motion, while imposing negligible background noise. Various types of graphene-coated cryo-EM grids were implemented to improve the robustness and efficiency of specimen preparation. Graphene functionalization by different means has been proved specifically useful in addressing challenges related to the air-water interface (AWI), such as preferential orientation and sample denaturation. Graphene sandwich specimen preparation sets a new direction to explore in cryo-EM analysis of biological specimens. In this review, we discuss the current challenges and future prospects of graphene application in cryo-EM analysis of macromolecules.

The complexity of biological systems and processes, spanning molecular to macroscopic scales, necessitates the use of multiscale simulations to get a comprehensive understanding. Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations are crucial for capturing processes beyond the reach of classical MD simulations. The advent of exascale computing offers unprecedented opportunities for scientific exploration, not least within life sciences, where simulations are essential to unravel intricate molecular mechanisms underlying biological processes. However, leveraging the immense computational power of exascale computing requires innovative algorithms and software designs. In this context, we discuss the current status and future prospects of multiscale biomolecular simulations on exascale supercomputers with a focus on QM/MM MD. We highlight our own efforts in developing a versatile and high-performance multiscale simulation framework with the aim of efficient utilization of state-of-the-art supercomputers. We showcase its application in uncovering complex biological mechanisms and its potential for leveraging exascale computing.