Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献_第10页

Cover Image, Volume 12, Issue 4 封面图片，第12卷，第4期

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-07-07 DOI: 10.1002/wcms.1633

Duan Ni, Zongtao Chai, Ying Wang, Mingyu Li, Zhengtian Yu, Yaqin Liu, Shaoyong Lu, Jian Zhang

引用次数: 0

Delocalization error: The greatest outstanding challenge in density-functional theory 离域误差:密度泛函理论中最大的突出挑战

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-07-01 DOI: 10.1002/wcms.1631

Kyle R. Bryenton, Adebayo A. Adeleke, Stephen G. Dale, Erin R. Johnson

引用次数: 36

Two decades of Martini: Better beads, broader scope 二十年的马提尼:更好的珠子，更广阔的范围

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-06-24 DOI: 10.1002/wcms.1620

Siewert J. Marrink, Luca Monticelli, Manuel N. Melo, Riccardo Alessandri, D. Peter Tieleman, Paulo C. T. Souza

{"title":"Two decades of Martini: Better beads, broader scope","authors":"Siewert J. Marrink, Luca Monticelli, Manuel N. Melo, Riccardo Alessandri, D. Peter Tieleman, Paulo C. T. Souza","doi":"10.1002/wcms.1620","DOIUrl":"https://doi.org/10.1002/wcms.1620","url":null,"abstract":"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.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1620","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5804521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 45

Network search algorithms and scoring functions for advanced-level computerized synthesis planning 高级计算机综合规划的网络搜索算法和评分功能

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-06-16 DOI: 10.1002/wcms.1630

Bartosz A. Grzybowski, Tomasz Badowski, Karol Molga, Sara Szymku?

{"title":"Network search algorithms and scoring functions for advanced-level computerized synthesis planning","authors":"Bartosz A. Grzybowski, Tomasz Badowski, Karol Molga, Sara Szymku?","doi":"10.1002/wcms.1630","DOIUrl":"https://doi.org/10.1002/wcms.1630","url":null,"abstract":"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.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6167308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Multiscale molecular simulations to investigate adenylyl cyclase-based signaling in the brain 多尺度分子模拟研究脑内腺苷基环化酶信号

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-06-14 DOI: 10.1002/wcms.1623

Siri C. van Keulen, Juliette Martin, Francesco Colizzi, Elisa Frezza, Daniel Trpevski, Nuria Cirauqui Diaz, Pietro Vidossich, Ursula Rothlisberger, Jeanette Hellgren Kotaleski, Rebecca C. Wade, Paolo Carloni

{"title":"Multiscale molecular simulations to investigate adenylyl cyclase-based signaling in the brain","authors":"Siri C. van Keulen, Juliette Martin, Francesco Colizzi, Elisa Frezza, Daniel Trpevski, Nuria Cirauqui Diaz, Pietro Vidossich, Ursula Rothlisberger, Jeanette Hellgren Kotaleski, Rebecca C. Wade, Paolo Carloni","doi":"10.1002/wcms.1623","DOIUrl":"https://doi.org/10.1002/wcms.1623","url":null,"abstract":"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.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5688020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case 用于分子动力学模拟的前百亿亿次高性能计算方法。Covid-19研究:一个用例

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-05-30 DOI: 10.1002/wcms.1622

Mi?osz Wieczór, Vito Genna, Juan Aranda, Rosa M. Badia, Josep Lluís Gelpí, Vytautas Gapsys, Bert L. de Groot, Erik Lindahl, Martí Municoy, Adam Hospital, Modesto Orozco

引用次数: 1

Review on the lithium transport mechanism in solid-state battery materials 锂在固态电池材料中的输运机制研究进展

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-05-18 DOI: 10.1002/wcms.1621

Zhong-Heng Fu, Xiang Chen, Qiang Zhang

{"title":"Review on the lithium transport mechanism in solid-state battery materials","authors":"Zhong-Heng Fu, Xiang Chen, Qiang Zhang","doi":"10.1002/wcms.1621","DOIUrl":"https://doi.org/10.1002/wcms.1621","url":null,"abstract":"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.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"13 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5692923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

New phase space formulations and quantum dynamics approaches 新的相空间公式和量子动力学方法

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-05-13 DOI: 10.1002/wcms.1619

Xin He, Baihua Wu, Youhao Shang, Bingqi Li, Xiangsong Cheng, Jian Liu

引用次数: 6

n2v: A density-to-potential inversion suite. A sandbox for creating, testing, and benchmarking density functional theory inversion methods n2v:密度对电位反演套件。一个沙盒创建，测试，和基准密度泛函理论反演方法

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-04-03 DOI: 10.1002/wcms.1617

Yuming Shi, Victor H. Chávez, Adam Wasserman

{"title":"n2v: A density-to-potential inversion suite. A sandbox for creating, testing, and benchmarking density functional theory inversion methods","authors":"Yuming Shi, Victor H. Chávez, Adam Wasserman","doi":"10.1002/wcms.1617","DOIUrl":"https://doi.org/10.1002/wcms.1617","url":null,"abstract":"From the most fundamental to the most practical side of density functional theory (DFT), Kohn–Sham inversions (iKS) can contribute to the development of functional approximations and shed light on their performance and limitations. On the one hand, iKS allows for the direct exploration of the Hohenberg–Kohn and Runge–Gross density-to-potential mappings that provide the foundations for DFT and time-dependent DFT. On the other hand, iKS can guide the analysis and development of approximate exchange–correlation and noninteracting kinetic energy functionals, and diagnose their errors. iKS can also play a similar role in the development of nonadditive functionals for modern density-based embedding methods. Various strategies to perform iKS calculations have been explored since the inception of DFT. We introduce n2v, a density-to-potential inversion Python module that is capable of performing the most useful and state-of-the-art inversion calculations. Currently based on NumPy, n2v was developed to be easy to learn by newcomers to the field. Its structure allows for other inversion methods to be easily added. The code offers a general interface that gives the freedom to use different software packages in the computational molecular sciences (CMS) community, and the current release supports the Psi4 and PySCF packages. Six inversion methods have been implemented into n2v and are reviewed here along with detailed numerical illustrations on molecules with numbers of electrons ranging from ~10 to ~100.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"12 6","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"6065380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Machine learning solutions for predicting protein–protein interactions 预测蛋白质相互作用的机器学习解决方案

IF 11.4 2区化学

Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2022-03-29 DOI: 10.1002/wcms.1618

Rita Casadio, Pier Luigi Martelli, Castrense Savojardo

{"title":"Machine learning solutions for predicting protein–protein interactions","authors":"Rita Casadio, Pier Luigi Martelli, Castrense Savojardo","doi":"10.1002/wcms.1618","DOIUrl":"https://doi.org/10.1002/wcms.1618","url":null,"abstract":"Proteins are “social molecules.” Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting that biological processes can occur locally, depending on the cell needs. The question then arises as to which extent we can monitor protein-aggregate formation, both experimentally and theoretically and then predict/simulate functional aggregate formation. Available data are relative to mesoscopic interacting networks at a proteome level, to protein-binding affinity data, and to interacting protein complexes, solved with atomic resolution. Powerful algorithms based on machine learning (ML) can extract information from data sets and infer properties of never-seen-before examples. ML tools address the problem of protein–protein interactions (PPIs) adopting different data sets, input features, and architectures. According to recent publications, deep learning is the most successful method. However, in ML-computational biology, convincing evidence of a success story comes out by performing general benchmarks on blind data sets. Results indicate that the state-of-the-art ML approaches, based on traditional and/or deep learning, can still be ameliorated, irrespectively of the power of the method and richness in input features. This being the case, it is quite evident that powerful methods still are not trained on the whole possible spectrum of PPIs and that more investigations are necessary to complete our knowledge of PPI-functional interactions.This article is categorized under:\u0000 ","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"12 6","pages":""},"PeriodicalIF":11.4,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"5856013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19