Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

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.

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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.

The cover image is based on the Advanced Review Along the allostery stream: Recent advances in computational methods for allosteric drug discovery by Duan Ni et al., https://doi.org/10.1002/wcms.1585.

Every day, density-functional theory (DFT) is routinely applied to computational modeling of molecules and materials with the expectation of high accuracy. However, in certain situations, popular density-functional approximations (DFAs) have the potential to give substantial quantitative, and even qualitative, errors. The most common class of error is delocalization error, which is an overarching term that also encompasses the one-electron self-interaction error. In our opinion, its resolution remains the greatest outstanding challenge in DFT development. In this paper, we review the history of delocalization error and provide several complimentary conceptual pictures for its interpretation, along with illustrative examples of its various manifestations. Approaches to reduce delocalization error are discussed, as is its interplay with other shortcomings of popular DFAs, including treatment of non-bonded repulsion and neglect of London dispersion.

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The Martini model, a coarse-grained force field for molecular dynamics simulations, has been around for nearly two decades. Originally developed for lipid-based systems by the groups of Marrink and Tieleman, the Martini model has over the years been extended as a community effort to the current level of a general-purpose force field. Apart from the obvious benefit of a reduction in computational cost, the popularity of the model is largely due to the systematic yet intuitive building-block approach that underlies the model, as well as the open nature of the development and its continuous validation. The easy implementation in the widely used Gromacs software suite has also been instrumental. Since its conception in 2002, the Martini model underwent a gradual refinement of the bead interactions and a widening scope of applications. In this review, we look back at this development, culminating with the release of the Martini 3 version in 2021. The power of the model is illustrated with key examples of recent important findings in biological and material sciences enabled with Martini, as well as examples from areas where coarse-grained resolution is essential, namely high-throughput applications, systems with large complexity, and simulations approaching the scale of whole cells.

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In 2020, a “hybrid” expert-AI computer program called Chematica (a.k.a. Synthia) was shown to autonomously plan multistep syntheses of complex natural products, which remain outside the reach of purely data-driven AI programs. The ability to plan at this level of chemical sophistication has been attributed mainly to the superior quality of Chematica's reactions rules. However, rules alone are not sufficient for advanced synthetic planning which also requires appropriately crafted algorithms with which to intelligently navigate the enormous networks of synthetic possibilities, score the synthetic positions encountered, and rank the pathways identified. Chematica's algorithms are distinct from prêt-à-porter algorithmic solutions and are product of multiple rounds of improvements, against target structures of increasing complexity. Since descriptions of these improvements have been scattered among several of our prior publications, the aim of the current Review is to narrate the development process in a more comprehensive manner.

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Adenylyl cyclases (ACs) play a key role in many signaling cascades. ACs catalyze the production of cyclic AMP from ATP and this function is stimulated or inhibited by the binding of their cognate stimulatory or inhibitory Gα subunits, respectively. Here we used simulation tools to uncover the molecular and subcellular mechanisms of AC function, with a focus on the AC5 isoform, extensively studied experimentally. First, quantum mechanical/molecular mechanical free energy simulations were used to investigate the enzymatic reaction and its changes upon point mutations. Next, molecular dynamics simulations were employed to assess the catalytic state in the presence or absence of Gα subunits. This led to the identification of an inactive state of the enzyme that is present whenever an inhibitory Gα is associated, independent of the presence of a stimulatory Gα. In addition, the use of coevolution-guided multiscale simulations revealed that the binding of Gα subunits reshapes the free-energy landscape of the AC5 enzyme by following the classical population-shift paradigm. Finally, Brownian dynamics simulations provided forward rate constants for the binding of Gα subunits to AC5, consistent with the ability of the protein to perform coincidence detection effectively. Our calculations also pointed to strong similarities between AC5 and other AC isoforms, including AC1 and AC6. Findings from the molecular simulations were used along with experimental data as constraints for systems biology modeling of a specific AC5-triggered neuronal cascade to investigate how the dynamics of downstream signaling depend on initial receptor activation.

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Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics.

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The growing demands to mitigate climate change and environmental degradation stimulate the rapid developments of rechargeable lithium (Li) battery technologies. Fast Li transports in battery materials are of essential significance to ensure superior Li dynamical stability and rate performance of batteries. Herein, the Li transport mechanisms in solid-state battery materials (SSBMs) are comprehensively summarized. The collective diffusion mechanisms in solid electrolytes are elaborated, which are further understood from multiple perspectives including lattice dynamics, crystalline structure, and electronic structure. With the exponentially improving performance of computers, atomistic simulations have been playing an increasingly important role in revealing and understanding the Li transport in SSBMs, bridging the gap between experimental phenomena and theoretical models. Theoretical and experimental characterization methods for Li transports are discussed. The design strategies toward fast Li transports are classified. Finally, a perspective on the achievements and challenges of probing Li transports is provided.

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We report recent progress on the phase space formulation of quantum mechanics with coordinate-momentum variables, focusing more on new theory of (weighted) constraint coordinate-momentum phase space for discrete-variable quantum systems. This leads to a general coordinate-momentum phase space formulation of composite quantum systems, where conventional representations on infinite phase space are employed for continuous variables. It is convenient to utilize (weighted) constraint coordinate-momentum phase space for representing the quantum state and describing nonclassical features. Various numerical tests demonstrate that new trajectory-based quantum dynamics approaches derived from the (weighted) constraint phase space representation are useful and practical for describing dynamical processes of composite quantum systems in the gas phase as well as in the condensed phase.

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