Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

The ONIOM method and many extensions to it provide capabilities to treat challenging multiscale problems in catalysis and material science. Our open-source program Spicy is a flexible toolkit for ONIOM and fragment methods. Spicy includes a generalization of multicenter-ONIOM, a higher-order multipole embedding scheme, and fragment methods as useful extensions of our own n-layered integrated molecular orbital and molecular mechanics (ONIOM), which allow applying ONIOM and high accuracy calculations to a wider range of systems. A calculation on the metallo-protein hemoglobin demonstrates the versatility of the implementation.

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The power of computer simulations, including machine-learning, has become an inseparable part of scientific analysis of biological data. This has significantly impacted the field of cryogenic electron microscopy (cryo-EM), which has grown dramatically since the “resolution-revolution.” Many maps are now solved at 3–4 Å or better resolution, although a significant proportion of maps deposited in the Electron Microscopy Data Bank are still at lower resolution, where the positions of atoms cannot be determined unambiguously. Additionally, cryo-EM maps are often characterized by a varying local resolution, partly due to conformational heterogeneity of the imaged molecule. To address such problems, many computational methods have been developed for cryo-EM map reconstruction and atomistic model building. Here, we review the development in algorithms and tools for building models in cryo-EM maps at different resolutions. We describe methods for model building, including rigid and flexible fitting of known models, model validation, small-molecule fitting, and model visualization. We provide examples of how these methods have been used to elucidate the structure and function of dynamic macromolecular machines.

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The cover image is based on the Focus Article New phase space formulations and quantum dynamics approaches by Xin He et al., https://doi.org/10.1002/wcms.1619.

Machine learning techniques have been widely applied in many fields of chemistry, physics, biology, and materials science. One of the most fruitful applications is machine learning of the complicated multidimensional function of potential energy or related electronic properties from discrete quantum chemical data. In particular, substantial efforts have been dedicated to developing various atomistic neural network (AtNN) representations, which refer to a family of methods expressing the targeted physical quantity as a sum of atomic components represented by atomic NNs. This class of approaches not only fully preserves the physical symmetry of the system but also scales linearly with respect to the size of a system, enabling accurate and efficient chemical dynamics and spectroscopic simulations in complicated systems and even a number of variably sized systems across the phases. In this review, we discuss different strategies in developing highly efficient and representable AtNN potentials, and in generalizing these scalar AtNN models to learn vectorial and tensorial quantities with the correct rotational equivariance. We also review active learning algorithms to generate practical AtNN models and present selected examples of AtNN applications in gas-surface systems to demonstrate their capabilities of accurately representing both molecular systems and condensed phase systems. We conclude this review by pointing out remaining challenges for the further development of more reliable, transferable, and scalable AtNN representations in more application scenarios.

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A fundamental and challenging task of computational protein studies is to design proteins of desired structures and functions on demand. Data-driven approaches to protein design have been gaining tremendous momentum, with recent developments concentrated on protein sequence representation and generation by using deep learning language models, structure-based sequence design or inverse protein folding, and the de novo generation of new protein backbones. Currently, design methods have been assessed mainly by several useful computational metrics. However, these metrics are still highly insufficient for predicting the performance of design methods in wet experiments. Nevertheless, some methods have been verified experimentally, which showed that proteins of novel sequences and structures can be designed with data-driven models learned from natural proteins. Despite the progress, an important current limitation is the lack of accurate data-driven approaches to model or design protein dynamics.

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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.

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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.

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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.

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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.

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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.

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