Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Ribonucleoprotein (RNP)-machineries are comprised of intricate networks of long noncoding RNAs and proteins that allow them to actively participate in transcription, RNA processing, and translation. RNP-machineries thus play vital roles in gene expression and regulation. Recent advances in cryo-EM techniques provided a wealth of near-atomic-level resolution structures setting the basis for understanding how these fascinating multiscale complexes exert their diverse roles. However, these structures represent only isolated snapshots of the plastic and highly dynamic RNP-machineries and are thus insufficient to comprehensively assess their multifaceted mechanisms. In this review, we discuss the role and merit of all-atom simulations in disentangling the mechanism of eukaryotic RNA-based machineries responsible for RNA processing. We showcase how all-atom simulations can capture their large-scale functional movements, trace the signaling pathways that are at the root of their massive conformational remodeling, explain recognition mechanisms of specific RNA sequences, and, lastly, unravel the chemical mechanisms underlying the formation of functional RNA strands. Finally, we review the methodological pitfalls and outline future challenges in modeling key functional aspects of these large molecular engines with all-atom simulations. In addition to providing insights into the most basic processes that govern all forms of life, in-depth mechanistic comprehension of RNP-machineries offers a foundation for developing innovative therapeutic strategies against the variety of human diseases linked to deregulated RNA metabolism.

This article is categorized under:

Voltage-gated sodium channels (VGSCs/Na_vs), which control the flow of Na⁺ and affect the generation of action potentials (APs), have been regarded as essential targets for many diseases. The biological and pharmacological functions of VGSCs have been extensively studied and many efforts have been made to discover and design ligands of VGSCs as potential therapies. Here, we summarize the recent and representative studies of VGSCs from the perspective of computer-aided drug design (CADD) and molecular modeling, including the structural biology of VGSCs, virtual screening and drug design toward VGSCs based on CADD, and functional studies using molecular modeling technologies. Furthermore, we conclude the achievements that have been made in the field of VGSCs and discuss the shortcomings found in previous studies. We hope that this review can provide some inspiration and reference for future investigations of VGSCs and drug design.

This article is categorized under:

Interfaces, the boundary that separates two or more chemical compositions and/or phases of matter, alters basic chemical and physical properties including the thermodynamics of selectivity, transition states, and pathways of chemical reactions, nucleation events and phase growth, and kinetic barriers and mechanisms for mass transport and heat transport. While progress has been made in advancing more interface-sensitive experimental approaches, their interpretation requires new theoretical methods and models that in turn can further elaborate on the microscopic physics that make interfacial chemistry so unique compared to the bulk phase. In this review, we describe some of the most recent theoretical efforts in modeling interfaces, and what has been learned about the transport and chemical transformations that occur at the air–liquid and solid–liquid interfaces.

This article is categorized under:

The superposition of small molecules is a standard technique in molecular modeling and for some more advanced in silico applications of drug discovery a critical prerequisite. The aims of superposing molecules are manifold. An assessment of the 3D similarity, an understanding of the SAR in a compound series, or ultimately an estimate of the likelihood of a compound to be active and selective against a target protein of interest. Considering so many objectives it is not surprising that new superpositioning methods are continuously developed and the overlay problem cannot be considered solved. We present 51 superposition methods with a focus on those published in the 21st century. For 36 methods that are currently available, we briefly describe and compare the respective pose generation and scoring processes. While the modeling community got a wealth of methods at hand, the scientific necessity of rigorous and comparable benchmarking becomes apparent.

This article is categorized under:

Computational studies have delivered valuable mechanistic insights into thiol Michael additions, which are important CS bond-forming reactions used in biological and materials chemistry. The field has delivered a wealth of understanding about the ways in which substituents, catalysts, and the local environment influence the addition pathway. Several mechanistic scenarios are now recognized, differing with respect to the energies and timing of the bond-forming processes. While technical challenges still exist, the field has advanced to such an extent that full-scale simulations of the additions of Michael acceptors to protein thiol groups are now possible.

This article is categorized under:

Drug design involves the process of identifying and designing novel molecules that have desirable properties and bind well to a given target receptor. Typically, such molecules are identified by screening large chemical libraries for desirable physicochemical properties and binding strength with the target protein. This traditional approach, however, has severe limitations as exhaustively screening every molecule in known chemical libraries is computationally infeasible. Furthermore, currently available molecular libraries are only a minuscule part of the entire set of possible drug-like molecular structures (drug-like chemical space). In this review, we discuss how the former limitation is addressed by modeling virtual screening as a search space problem and how these endeavors utilize machine learning to reduce the number of required computational experiments to identify top candidates. We follow that up by discussing generative methods that attempt to approximate the entire drug-like chemical space providing us a path to explore beyond the known drug-like chemical space. We place special emphasis on generative models that learn the marginal distributions conditioned on specific properties or receptor structures for efficient sampling of molecules. Through this review, we aim to highlight modern machine learning based methods that try to efficiently enhance our sampling capability beyond conventional screening methods which, in turn, would benefit drug design significantly. Therefore, we also encourage further methods of development that work on such important aspects of drug design.

This article is categorized under:

The scalable preparation of high-quality and low-cost two-dimensional (2D) materials is critical to achieving their potential applications in various fields. Chemical vapor deposition (CVD) method is considered the most promising method for producing ultrathin 2D materials and has continued to develop in recent years. First-principles calculations have provided important theoretical guidance for the CVD synthesis of 2D materials, and have played an increasingly important role in the field of material synthesis in recent years. In this review, we present recent advances in the growth mechanism of 2D materials, focusing on the theoretical research progress of four typical 2D materials: graphene, hexagonal boron nitride (hBN), transition metal dichalcogenide (TMDC), and phosphorene. Several aspects of the growth process are discussed in detail, including the decomposition of precursors, nucleation, growth kinetics, domain shape, and epitaxial and alignment of 2D crystals. Based on the understanding of these atomic-scale growth processes, strategies toward the wafer-scale growth of continuous and homogeneous 2D thin films are proposed and confirmed by experiments. In the final section, we summarize future challenges and opportunities in the computational studies of the growth mechanism of 2D materials.

This article is categorized under:

The cover image is based on the Advanced Review Machine intelligence for chemical reaction space by Philippe Schwaller et al., https://doi.org/10.1002/wcms.1604.

While in principle exact, Kohn–Sham density functional theory—the workhorse of computational chemistry—must rely on approximations for the exchange–correlation functional. Despite staggering successes, present-day approximations still struggle when the effects of electron–electron correlation play a prominent role. The limit in which the electronic Coulomb repulsion completely dominates the exchange–correlation functional offers a well-defined mathematical framework that provides insight for new approximations able to deal with strong correlation. In particular, the mathematical structure of this limit, which is now well-established thanks to its reformulation as an optimal transport problem, points to the use of very different ingredients (or features) with respect to the traditional ones used in present approximations. We focus on strategies to use these new ingredients to build approximations for computational chemistry and highlight future promising directions.

This article is categorized under:

The cover image is based on the Advanced Review The hierarchy of Davydov's Ansätze and its applications by Yang Zhao et al., https://doi.org/10.1002/wcms.1589.