Biophysical reviews最新文献

Biophysical Reviews' "Meet the Editors Series"-a profile of Anthony Watts. 生物物理评论》的 "编辑见面系列"--Anthony Watts 的简介。

IF 4.9

Biophysical reviews Pub Date : 2024-08-31 eCollection Date: 2024-08-01 DOI: 10.1007/s12551-024-01214-5

Anthony Watts

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Biophysical Reviews: the official journal of the International Union for Pure and Applied Biophysics. 生物物理评论》：国际纯粹与应用生物物理学联合会的官方期刊。

IF 4.9

Biophysical reviews Pub Date : 2024-08-27 eCollection Date: 2024-08-01 DOI: 10.1007/s12551-024-01216-3

Wilma K Olson

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The Albany Conversations Are Now the Next Generation Conversations: Albany @ Ruston. 奥尔巴尼对话现在是下一代对话：奥尔巴尼 @ 拉斯顿。

IF 4.9

Biophysical reviews Pub Date : 2024-08-12 eCollection Date: 2024-08-01 DOI: 10.1007/s12551-024-01215-4

Thomas C Bishop, Kelly M Thayer, Robert T Young

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Navigating the complexity of p53-DNA binding: implications for cancer therapy 驾驭 p53 与 DNA 结合的复杂性：对癌症治疗的影响

IF 4.9

Biophysical reviews Pub Date : 2024-07-11 DOI: 10.1007/s12551-024-01207-4

Kelly M. Thayer, Sean Stetson, Fernando Caballero, Christopher Chiu, I. Han

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LAFeBS, alive, kicking, and growing: the story continues… LAFeBS 生机勃勃，不断发展壮大：故事仍在继续......

IF 4.9

Biophysical reviews Pub Date : 2024-07-09 DOI: 10.1007/s12551-024-01208-3

R. D. Peluffo, Silvia del V. Alonso, R. Itri, F. L. G. Flecha, Leandro R S Barbosa

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Structural dynamics in chromatin unraveling by pioneer transcription factors 先锋转录因子解开染色质的结构动态变化

IF 4.9

Biophysical reviews Pub Date : 2024-07-04 DOI: 10.1007/s12551-024-01205-6

Andrea Orsetti, D. van Oosten, Roxana-Geanina Vasarhelyi, Theodor-Marian Dănescu, Jan Huertas, Hugo van Ingen, Vlad Cojocaru

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Special issue: Multiscale simulations of DNA from electrons to nucleosomes. 特刊：从电子到核小体的 DNA 多尺度模拟。

IF 4.9

Biophysical reviews Pub Date : 2024-07-03 eCollection Date: 2024-06-01 DOI: 10.1007/s12551-024-01204-7

John H Maddocks, Pablo D Dans, Thomas H Cheatham, Sarah Harris, Charles Laughton, Modesto Orozco, Lois Pollack, Wilma K Olson

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Structure-based prediction of protein-nucleic acid binding using graph neural networks. 利用图神经网络进行基于结构的蛋白质-核酸结合预测。

IF 4.9

Biophysical reviews Pub Date : 2024-06-26 eCollection Date: 2024-06-01 DOI: 10.1007/s12551-024-01201-w

Jared M Sagendorf, Raktim Mitra, Jiawei Huang, Xiaojiang S Chen, Remo Rohs

{"title":"Structure-based prediction of protein-nucleic acid binding using graph neural networks.","authors":"Jared M Sagendorf, Raktim Mitra, Jiawei Huang, Xiaojiang S Chen, Remo Rohs","doi":"10.1007/s12551-024-01201-w","DOIUrl":"10.1007/s12551-024-01201-w","url":null,"abstract":"Protein-nucleic acid (PNA) binding plays critical roles in the transcription, translation, regulation, and three-dimensional organization of the genome. Structural models of proteins bound to nucleic acids (NA) provide insights into the chemical, electrostatic, and geometric properties of the protein structure that give rise to NA binding but are scarce relative to models of unbound proteins. We developed a deep learning approach for predicting PNA binding given the unbound structure of a protein that we call PNAbind. Our method utilizes graph neural networks to encode the spatial distribution of physicochemical and geometric properties of protein structures that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein, and using local encodings, they predict the location of individual NA binding residues. Our models can discriminate between specificity for DNA or RNA binding, and we show that predictions made on computationally derived protein structures can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92-0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and showed that our model predictions are consistent with and help explain experimental RNA binding data.Supplementary information: The online version contains supplementary material available at 10.1007/s12551-024-01201-w.","PeriodicalId":9094,"journal":{"name":"Biophysical reviews","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11427629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142341545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DNA simulation benchmarks revealed with the accumulation of high-resolution structures. 随着高分辨率结构的积累而揭示的 DNA 模拟基准。

IF 4.9

Biophysical reviews Pub Date : 2024-06-18 eCollection Date: 2024-06-01 DOI: 10.1007/s12551-024-01198-2

Wilma K Olson, Robert T Young, Luke Czapla

{"title":"DNA simulation benchmarks revealed with the accumulation of high-resolution structures.","authors":"Wilma K Olson, Robert T Young, Luke Czapla","doi":"10.1007/s12551-024-01198-2","DOIUrl":"10.1007/s12551-024-01198-2","url":null,"abstract":"DNA carries more than the list of biochemical instructions that drive the basic functions of living systems. The sequence of base pairs includes a multitude of structural and energetic signals that determine the degree to which the long, threadlike molecule moves and how it responds to proteins and other molecules involved in its processing and packaging. The arrangements of successive base pairs in high-resolution protein-DNA crystal structures provide useful benchmarks for atomic-level simulations of double-helical DNA as well as information potentially useful in interpreting the properties of specific DNA sequences. The set of currently available structures has enough examples to characterize the conformational preferences of the DNA base-pair steps within the context of their immediate neighbors, i.e., in the context of tetramers, and reveals surprising effects of certain neighbors on local chain properties. The proteins in contact with DNA present various microenvironments that sense and/or induce the observed spatial forms. The cumulative buildup of amino-acid atoms in different protein-DNA complexes produces a binding cloud around the double helix with subtle sequence-dependent features. While the microenvironment presented by each protein to DNA is highly unique, the overall composition of amino-acid atoms within close range of DNA in a broad collection of structures is fairly uniform. The buildup of protein atoms of different types around the DNA provides new information for the improvement of nucleic acid force fields and fresh ideas for the exploration of the properties of DNA in solution.","PeriodicalId":9094,"journal":{"name":"Biophysical reviews","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11297015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing RNA atomistic force fields via energy landscape explorations in implicit solvent. 通过隐含溶剂中的能量景观探索评估 RNA 原子力场

IF 4.9

Biophysical reviews Pub Date : 2024-06-17 eCollection Date: 2024-06-01 DOI: 10.1007/s12551-024-01202-9

Konstantin Röder, Samuela Pasquali

{"title":"Assessing RNA atomistic force fields via energy landscape explorations in implicit solvent.","authors":"Konstantin Röder, Samuela Pasquali","doi":"10.1007/s12551-024-01202-9","DOIUrl":"10.1007/s12551-024-01202-9","url":null,"abstract":"Predicting the structure and dynamics of RNA molecules still proves challenging because of the relative scarcity of experimental RNA structures on which to train models and the very sensitive nature of RNA towards its environment. In the last decade, several atomistic force fields specifically designed for RNA have been proposed and are commonly used for simulations. However, it is not necessarily clear which force field is the most suitable for a given RNA molecule. In this contribution, we propose the use of the computational energy landscape framework to explore the energy landscape of RNA systems as it can bring complementary information to the more standard approaches of enhanced sampling simulations based on molecular dynamics. We apply the EL framework to the study of a small RNA pseudoknot, the Aquifex aeolicus tmRNA pseudoknot PK1, and we compare the results of five different RNA force fields currently available in the AMBER simulation software, in implicit solvent. With this computational approach, we can not only compare the predicted 'native' states for the different force fields, but the method enables us to study metastable states as well. As a result, our comparison not only looks at structural features of low energy folded structures, but provides insight into folding pathways and higher energy excited states, opening to the possibility of assessing the validity of force fields also based on kinetics and experiments providing information on metastable and unfolded states.Supplementary information: The online version contains supplementary material available at 10.1007/s12551-024-01202-9.","PeriodicalId":9094,"journal":{"name":"Biophysical reviews","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11297004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0