1
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Zhang Y, Yang C, Xiong Y, Xiao Y. 3dDNAscoreA: A scoring function for evaluation of DNA 3D structures. Biophys J 2024:S0006-3495(24)00138-3. [PMID: 38409781 DOI: 10.1016/j.bpj.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/31/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024] Open
Abstract
DNA molecules are vital macromolecules that play a fundamental role in many cellular processes and have broad applications in medicine. For example, DNA aptamers have been rapidly developed for diagnosis, biosensors, and clinical therapy. Recently, we proposed a computational method of predicting DNA 3D structures, called 3dDNA. However, it lacks a scoring function to evaluate the predicted DNA 3D structures, and so they are not ranked for users. Here, we report a scoring function, 3dDNAscoreA, for evaluation of DNA 3D structures based on a deep learning model ARES for RNA 3D structure evaluation but using a new strategy for training. 3dDNAscoreA is benchmarked on two test sets to show its ability to rank DNA 3D structures and select the native and near-native structures.
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Affiliation(s)
- Yi Zhang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chenxi Yang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yiduo Xiong
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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2
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Lawson CL, Berman H, Chen L, Vallat B, Zirbel C. The Nucleic Acid Knowledgebase: a new portal for 3D structural information about nucleic acids. Nucleic Acids Res 2024; 52:D245-D254. [PMID: 37953312 PMCID: PMC10767938 DOI: 10.1093/nar/gkad957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 11/14/2023] Open
Abstract
The Nucleic Acid Knowledgebase (nakb.org) is a new data resource, updated weekly, for experimentally determined 3D structures containing DNA and/or RNA nucleic acid polymers and their biological assemblies. NAKB indexes nucleic acid-containing structures derived from all major structure determination methods (X-ray, NMR and EM), including all held by the Protein Data Bank (PDB). As the planned successor to the Nucleic Acid Database (NDB), NAKB's design preserves all functionality of the NDB and provides novel nucleic acid-centric content, including structural and functional annotations, as well as annotations from and links to external resources. A variety of custom interactive tools have been developed to enable rapid exploration and drill-down of NAKB's content.
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Affiliation(s)
- Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers, State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Li Chen
- Institute for Quantitative Biomedicine, Rutgers, State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, State University of New Jersey, 174 Frelinghuysen Road, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Craig L Zirbel
- Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403, USA
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3
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Yella VR, Vanaja A. Computational analysis on the dissemination of non-B DNA structural motifs in promoter regions of 1180 cellular genomes. Biochimie 2023; 214:101-111. [PMID: 37311475 DOI: 10.1016/j.biochi.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/05/2023] [Accepted: 06/05/2023] [Indexed: 06/15/2023]
Abstract
The promoter regions of gene regulation are under evolutionary constraints and earlier studies uncovered that they are characterized by enrichment of functional non-B DNA structural signatures like curved DNA, cruciform DNA, G-quadruplex, triple-helical DNA, slipped DNA structures, and Z-DNA. However, these studies are restricted to a few model organisms, single non-B DNA motif types, or whole genomic sequences, and their comparative accumulation in promoter regions of different domains of life has not been reported comprehensively. In this study, for the first time, we investigated the preponderance of non-B DNA-prone motifs in promoter regions in 1180 genomes belonging to 28 taxonomic groups using the non-B DNA Motif Search Tool (nBMST). The trends suggest that they are predominant in promoters compared to the upstream and downstream regions of all three domains of life and variably linked to taxonomic groups. Cruciform DNA motif is the most abundant form of non-B DNA, spanning from archaea to lower eukaryotes. Curved DNA motifs are prominent in host-associated bacteria, and suppressed in mammals. Triplex-DNA and slipped DNA structure repeats are discretely dispersed in all lineages. G-quadruplex motifs are significantly enriched in mammals. We also observed that the unique enrichment of non-B DNA in promoters is strongly linked to genome GC, size, evolutionary time divergence, and ecological adaptations. Overall, our work systematically reports the unique non-B DNA structural landscape of cellular organisms from the perspective of the cis-regulatory code of genomes.
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Affiliation(s)
- Venkata Rajesh Yella
- Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, 522302, Andhra Pradesh, India.
| | - Akkinepally Vanaja
- Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Guntur, 522302, Andhra Pradesh, India; KL College of Pharmacy, Koneru Lakshmaiah Education Foundation, Guntur, 522302, Andhra Pradesh, India
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4
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Brázda V, Mergny JL. Quadruplexes and aging: G4-binding proteins regulate the presence of miRNA in small extracellular vesicles (sEVs). Biochimie 2023; 214:69-72. [PMID: 36690199 DOI: 10.1016/j.biochi.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/08/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
The interaction between proteins and nucleic acids is a core element of life. Many proteins bind nucleic acids via a sequence-specific manner, but there are also many types of proteins that recognize various structural motifs. Researchers have recently found that proteins that can recognize DNA and RNA G-quadruplexes (G4s) are very important for basic cellular processes, particularly in eukaryotes. Some of these proteins are located outside the nucleus and interact with RNA, potentially affecting miRNA functions in intercellular communication, which is facilitated by small extracellular vesicles (sEVs). Imbalances in the production of sEVs are associated with various pathologies and senescence in humans. The distribution of miRNA into sEVs is regulated by two RNA-binding proteins, Alyref and FUS. Both proteins possess G-rich recognition motifs that are compatible with the formation of RNA parallel G4 structures. This lends credence to the new hypothesis that G4-formation in RNAs and their interaction with G4-binding proteins can affect the fate of miRNAs and control their distribution in sEVs that are associated with senescence and aging.
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Affiliation(s)
- Václav Brázda
- Institute of Biophysics of the Czech Academy of Sciences, Brno, Czech Republic.
| | - Jean-Louis Mergny
- Institute of Biophysics of the Czech Academy of Sciences, Brno, Czech Republic; Laboratoire d'Optique et Biosciences (LOB), Ecole Polytechnique, CNRS, INSERM, Institut Polytechnique de Paris, 91128, Palaiseau, France
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5
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Stitch M, Avagliano D, Graczyk D, Clark IP, González L, Towrie M, Quinn SJ. Good Vibrations Report on the DNA Quadruplex Binding of an Excited State Amplified Ruthenium Polypyridyl IR Probe. J Am Chem Soc 2023; 145:21344-21360. [PMID: 37736878 PMCID: PMC10557146 DOI: 10.1021/jacs.3c06099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Indexed: 09/23/2023]
Abstract
The nitrile containing Ru(II)polypyridyl complex [Ru(phen)2(11,12-dCN-dppz)]2+ (1) is shown to act as a sensitive infrared probe of G-quadruplex (G4) structures. UV-visible absorption spectroscopy reveals enantiomer sensitive binding for the hybrid htel(K) and antiparallel htel(Na) G4s formed by the human telomer sequence d[AG3(TTAG3)3]. Time-resolved infrared (TRIR) of 1 upon 400 nm excitation indicates dominant interactions with the guanine bases in the case of Λ-1/htel(K), Δ-1/htel(K), and Λ-1/htel(Na) binding, whereas Δ-1/htel(Na) binding is associated with interactions with thymine and adenine bases in the loop. The intense nitrile transient at 2232 cm-1 undergoes a linear shift to lower frequency as the solution hydrogen bonding environment decreases in DMSO/water mixtures. This shift is used as a sensitive reporter of the nitrile environment within the binding pocket. The lifetime of 1 in D2O (ca. 100 ps) is found to increase upon DNA binding, and monitoring of the nitrile and ligand transients as well as the diagnostic DNA bleach bands shows that this increase is related to greater protection from the solvent environment. Molecular dynamics simulations together with binding energy calculations identify the most favorable binding site for each system, which are in excellent agreement with the observed TRIR solution study. This study shows the power of combining the environmental sensitivity of an infrared (IR) probe in its excited state with the TRIR DNA "site effect" to gain important information about the binding site of photoactive agents and points to the potential of such amplified IR probes as sensitive reporters of biological environments.
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Affiliation(s)
- Mark Stitch
- School
of Chemistry, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Davide Avagliano
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstr. 19, 1090 Vienna, Austria
- Department
of Chemistry, Chemical Physics Theory Group, University of Toronto, 80 St. George St., Toronto, Ontario M5S 3H6, Canada
| | - Daniel Graczyk
- School
of Chemistry, University College Dublin, Dublin, D04 V1W8, Ireland
| | - Ian P. Clark
- Central
Laser Facility, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
| | - Leticia González
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währingerstr. 19, 1090 Vienna, Austria
- Vienna
Research Platform on Accelerating Photoreaction Discovery, University of Vienna, Währingerstr. 19, 1090 Vienna, Austria
| | - Michael Towrie
- Central
Laser Facility, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.
| | - Susan J Quinn
- School
of Chemistry, University College Dublin, Dublin, D04 V1W8, Ireland
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6
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Derewenda ZS. C-H Groups as Donors in Hydrogen Bonds: A Historical Overview and Occurrence in Proteins and Nucleic Acids. Int J Mol Sci 2023; 24:13165. [PMID: 37685972 PMCID: PMC10488043 DOI: 10.3390/ijms241713165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 08/21/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
Hydrogen bonds constitute a unique type of non-covalent interaction, with a critical role in biology. Until fairly recently, the canonical view held that these bonds occur between electronegative atoms, typically O and N, and that they are mostly electrostatic in nature. However, it is now understood that polarized C-H groups may also act as hydrogen bond donors in many systems, including biological macromolecules. First recognized from physical chemistry studies, C-H…X bonds were visualized with X-ray crystallography sixty years ago, although their true significance has only been recognized in the last few decades. This review traces the origins of the field and describes the occurrence and significance of the most important C-H…O bonds in proteins and nucleic acids.
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Affiliation(s)
- Zygmunt Stanislaw Derewenda
- Department of Molecular Physiology and Biological Physics, School of Medicine, University of Virginia, Charlottesville, VA 22903-2628, USA
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7
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Mu ZC, Tan YL, Liu J, Zhang BG, Shi YZ. Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding. Molecules 2023; 28:4833. [PMID: 37375388 DOI: 10.3390/molecules28124833] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences.
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Affiliation(s)
- Zi-Chun Mu
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430073, China
- School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430073, China
| | - Ya-Lan Tan
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430073, China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430073, China
| | - Ben-Gong Zhang
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430073, China
| | - Ya-Zhou Shi
- Research Center of Nonlinear Science, School of Mathematical & Physical Sciences, Wuhan Textile University, Wuhan 430073, China
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8
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A Novel Fluorescent Aptasensor for Arsenic(III) Detection Based on a Triple-Helix Molecular Switch. Molecules 2023; 28:molecules28052341. [PMID: 36903586 PMCID: PMC10005410 DOI: 10.3390/molecules28052341] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/21/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
A novel aptamer-based fluorescent-sensing platform with a triple-helix molecular switch (THMS) was proposed as a switch for detecting the arsenic(III) ion. The triple helix structure was prepared by binding a signal transduction probe and arsenic aptamer. Additionally, the signal transduction probe labeled with fluorophore (FAM) and quencher (BHQ1) was employed as a signal indicator. The proposed aptasensor is rapid, simple and sensitive, with a limit of detection of 69.95 nM. The decrease in peak fluorescence intensity shows a linear dependence, with the concentration of As(III) in the range of 0.1 µM to 2.5 µM. The whole detection process takes 30 min. Moreover, the THMS-based aptasensor was also successfully used to detect As(III) in a real sample of Huangpu River water with good recoveries. The aptamer-based THMS also presents distinct advantages in stability and selectivity. The proposed strategy developed herein can be extensively applied in the field of food inspection.
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9
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Methods and Advances in the Design, Testing and Development of In Vitro Diagnostic Instruments. Processes (Basel) 2023. [DOI: 10.3390/pr11020403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
With the continuous improvement of medical testing and instrumentation engineering technologies, the design, testing and development methods of in vitro diagnostic instruments are developing rapidly. In vitro diagnostic instruments are also gradually developing into a class of typical high-end medical equipment. The design of in vitro diagnostic instruments involves a variety of medical diagnostic methods and biochemical, physical and other related technologies, and its development process involves complex system engineering. This paper systematically organizes and summarizes the design, testing and development methods of in vitro diagnostic instruments and their development in recent years, focusing on summarizing the related technologies and core aspects of the R&D process, and analyzes the development trend of the in vitro diagnostic instrument market.
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10
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan S, Ghosh S, Goodsell DS, Green RK, Guranovic V, Henry J, Hudson BP, Khokhriakov I, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Webb B, Westbrook JD, Whetstone S, Young JY, Zalevsky A, Zardecki C. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res 2023; 51:D488-D508. [PMID: 36420884 PMCID: PMC9825554 DOI: 10.1093/nar/gkac1077] [Citation(s) in RCA: 145] [Impact Index Per Article: 145.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Henry Chao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Paul A Craig
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Gregg V Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kenneth Dalenberg
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sai Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Igor Khokhriakov
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ben Webb
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Arthur Zalevsky
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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11
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Ramasanoff RR, Sokolov PA. The binding model of adenosine-specific DNA aptamer: Umbrella sampling study. J Mol Graph Model 2023; 118:108338. [PMID: 36201878 DOI: 10.1016/j.jmgm.2022.108338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/16/2022] [Accepted: 09/17/2022] [Indexed: 11/25/2022]
Abstract
We report a novel model of the selective binding mechanism of adenosine-specific DNA aptamer. Our theoretical investigations of AMP (Adenosine monophosphate) dissociation from aptamer-AMP complexes reveals new details of aptamer molecular specificity and stabilisation factors. Umbrella sampling MD calculations using parmbsc1 force field shows that the disordered structure of the internal loop of the unbound aptamer hairpin has a characteristic packing of guanines, which prevents barrier-free penetration of ligands into the site cavity. Also, this disordered structure of the unbound aptamer has a network of hydrogen bonds stabilising the cavity near the target guanines within the binding sites during the whole binding process. We suggested that the first AMP molecule binds to the disordered structure of the site closest to the aptamer hairpin stem and spends some free energy on ordering of the internal loop. Then the second AMP molecule binds to the ordered site closest to the aptamer hairpin loop with a lower energy gain. As a result, the induced-fit binding model is the most applicable for this aptamer and does not contradict the modern experimental NMR and calorimetry data.
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Affiliation(s)
- Ruslan R Ramasanoff
- Sevastopol State University, Universitetskaya 33, 299053, Sevastopol, Russia.
| | - Petr A Sokolov
- Sevastopol State University, Universitetskaya 33, 299053, Sevastopol, Russia; Saint Petersburg State University, Universitetskaya Nab. 7/9, 199034, Saint Petersburg, Russia
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12
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Jovin TM. The Origin of Left-Handed Poly[d(G-C)]. Methods Mol Biol 2023; 2651:1-32. [PMID: 36892756 DOI: 10.1007/978-1-0716-3084-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
The discovery of a reversible transition in the helical sense of a double-helical DNA was initiated by the first synthesis in 1967 of the alternating sequence poly[d(G-C)]. In 1968, exposure to high salt concentration led to a cooperative isomerization of the double helix manifested by an inversion in the CD spectrum in the 240-310 nm range and in an altered absorption spectrum. The tentative interpretation, reported in 1970 and then in detailed form in a 1972 publication by Pohl and Jovin, was that the conventional right-handed B-DNA structure (R) of poly[d(G-C)] transforms at high salt concentration into a novel, alternative left-handed (L) conformation. The historical course of this development and its aftermath, culminating in the first crystal structure of left-handed Z-DNA in 1979, is described in detail. The research conducted by Pohl and Jovin after 1979 is summarized, ending with an assessment of "unfinished business": condensed Z*-DNA; topoisomerase IIα (TOP2A) as an allosteric ZBP (Z-DNA-binding protein); B-Z transitions of phosphorothioate-modified DNAs; and parallel-stranded poly[d(G-A)], a double helix with high stability under physiological conditions and potentially also left-handed.
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Affiliation(s)
- Thomas M Jovin
- Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
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13
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Olson WK, Li Y, Fenley MO. Insights into DNA solvation found in protein-DNA structures. Biophys J 2022; 121:4749-4758. [PMID: 36380591 PMCID: PMC9808563 DOI: 10.1016/j.bpj.2022.11.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/31/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
The proteins that bind double-helical DNA present various microenvironments that sense and/or induce signals in the genetic material. The high-resolution structures of protein-DNA complexes reveal the nature of both the microenvironments and the conformational responses in DNA and protein. Complex networks of interactions within the structures somehow tie the protein and DNA together and induce the observed spatial forms. Here we show how the cumulative buildup of amino acid atoms around the sugars, phosphates, and bases in different protein-DNA complexes produces a binding cloud around the double helix and how different types of atoms fill that cloud. Rather than focusing on the principles of molecular binding and recognition suggested by the arrangements of amino acids and nucleotides in the macromolecular complexes, we consider the proteins in contact with DNA as organized solvents. We describe differences in the mix of atoms that come in closest contact with DNA, subtle sequence-dependent features in the microenvironment of the sugar-phosphate backbone, a direct link between the localized buildup of ionic species and the electrostatic potential surfaces of the DNA bases, and sites of atomic buildup above and below the basepair planes that transmit the unique features of the base environments along the chain backbone. The inferences about solvation that can be drawn from the survey provide new stimuli for improvement of nucleic acid force fields and fresh ideas for exploration of the properties of DNA in solution.
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Affiliation(s)
- Wilma K Olson
- Department of Chemistry and Chemical Biology and Center for Quantitative Biology, Rutgers, the State University of New Jersey, Piscataway, New Jersey.
| | - Yun Li
- Department of Chemistry and Chemical Biology and Center for Quantitative Biology, Rutgers, the State University of New Jersey, Piscataway, New Jersey
| | - Marcia O Fenley
- Department of Chemistry and Chemical Biology and Center for Quantitative Biology, Rutgers, the State University of New Jersey, Piscataway, New Jersey; Institute of Molecular Biophysics, Florida State University, Tallahassee, Florida
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14
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Satange R, Rode AB, Hou MH. Revisiting recent unusual drug-DNA complex structures: Implications for cancer and neurological disease diagnostics and therapeutics. Bioorg Med Chem 2022; 76:117094. [PMID: 36410206 DOI: 10.1016/j.bmc.2022.117094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
DNA plays a crucial role in various biological processes such as protein production, replication, recombination etc. by adopting different conformations. Targeting these conformations by small molecules is not only important for disease therapy, but also improves our understanding of the mechanisms of disease development. In this review, we provide an overview of some of the most recent ligand-DNA complexes that have diagnostic and therapeutic applications in neurological diseases caused by abnormal repeat expansions and in cancer associated with mismatches. In addition, we have discussed important implications of ligands targeting higher-order structures, such as four-way junctions, G-quadruplexes and triplexes for drug discovery and DNA nanotechnology. We provide an overview of the results and perspectives of such structural studies on ligand-DNA interactions.
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Affiliation(s)
- Roshan Satange
- Institute of Genomics and Bioinformatics National Chung Hsing University, Taichung 402, Taiwan; Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung 402, Taiwan
| | - Ambadas B Rode
- Regional Centre for Biotechnology, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurugram Expressway, Faridabad, Haryana 121001, India
| | - Ming-Hon Hou
- Institute of Genomics and Bioinformatics National Chung Hsing University, Taichung 402, Taiwan; Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung 402, Taiwan; Graduate Institute of Biotechnology, National Chung Hsing University, Taichung 402, Taiwan; Department of Life Sciences, National Chung Hsing University, Taichung 402, Taiwan.
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15
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Ressler AJ, Brandt ON, Weaver A, Poor JE, Ream A, Summers NA, McMillen CD, Seeram NP, Dougherty WG, Henry GE. Chromene-based Schiff Base Ligand: DNA Interaction Studies and Characterization of Tetranuclear Zinc, Nickel and Iron Complexes. Inorganica Chim Acta 2022. [DOI: 10.1016/j.ica.2022.121363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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16
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Saylor JL, Basile ON, Li H, Hunter LM, Weaver A, Shellenberger BM, Ann Tom L, Ma H, Seeram NP, Henry GE. Phenolic furanochromene hydrazone derivatives: Synthesis, antioxidant activity, ferroptosis inhibition, DNA cleavage and DNA molecular docking studies. Bioorg Med Chem 2022; 75:117088. [PMID: 36372027 DOI: 10.1016/j.bmc.2022.117088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 09/19/2022] [Accepted: 11/01/2022] [Indexed: 11/08/2022]
Abstract
Twenty-four phenolic furanochromene hydrazone derivatives were designed and synthesized in order to evaluate structure-activity relationships in a series of antioxidant-related assays. The derivatives have varying substitution patterns on the phenol ring, with some compounds having one, two or three hydroxy groups, and others containing one hydroxy group in combination with methoxy, methyl, bromo, iodo and/or nitro groups. Antioxidant activity was determined using the DPPH free radical scavenging and CUPRAC assays. Compounds containing ortho-dihydroxy and para-dihydroxy patterns had the highest free radical scavenging activity, with IC50 values ranging from 5.0 to 28 μM. Similarly, derivatives with ortho-dihydroxy and para-dihydroxy patterns, together with a 4-hydroxy-3,5‑dimethoxy pattern, displayed strong copper (II) ion reducing capacity, using Trolox as a standard. Trolox equivalent antioxidant capacity (TEAC) coefficients for these derivatives ranged from 1.75 to 3.97. As further evidence of antioxidant potential, greater than half of the derivatives reversed erastin-induced ferroptosis in HaCaT cells. In addition, twenty-three of the derivatives were effective at cleaving supercoiled plasmid DNA in the presence of copper (II) ions at 1 mM, with the 3,4‑dihydroxy derivative showing cleavage to both the linear and open circular forms at 3.9 uM. The interaction of the phenolic furanochromene derivatives with DNA was confirmed by molecular docking studies, which revealed that all the derivatives bind favorably in the minor groove of DNA.
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Affiliation(s)
- Jessica L Saylor
- Department of Chemistry, Susquehanna University, 514 University Avenue, Selinsgrove, PA 17870, USA
| | - Olivia N Basile
- Department of Chemistry, Susquehanna University, 514 University Avenue, Selinsgrove, PA 17870, USA
| | - Huifang Li
- Bioactive Botanical Research Laboratory, Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA
| | - Lindsey M Hunter
- Department of Chemistry, Susquehanna University, 514 University Avenue, Selinsgrove, PA 17870, USA
| | - Ashton Weaver
- Department of Chemistry, Susquehanna University, 514 University Avenue, Selinsgrove, PA 17870, USA
| | - Blake M Shellenberger
- Department of Chemistry, Susquehanna University, 514 University Avenue, Selinsgrove, PA 17870, USA
| | - Lou Ann Tom
- Department of Chemistry, Susquehanna University, 514 University Avenue, Selinsgrove, PA 17870, USA
| | - Hang Ma
- Bioactive Botanical Research Laboratory, Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA
| | - Navindra P Seeram
- Bioactive Botanical Research Laboratory, Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA
| | - Geneive E Henry
- Department of Chemistry, Susquehanna University, 514 University Avenue, Selinsgrove, PA 17870, USA.
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17
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Burley SK, Berman HM, Duarte JM, Feng Z, Flatt JW, Hudson BP, Lowe R, Peisach E, Piehl DW, Rose Y, Sali A, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Young JY, Zardecki C. Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students. Biomolecules 2022; 12:1425. [PMID: 36291635 PMCID: PMC9599165 DOI: 10.3390/biom12101425] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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18
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Mandal PK, Collie GW, Kauffmann B, Huc I. Racemic crystal structures of A-DNA duplexes. ACTA CRYSTALLOGRAPHICA SECTION D STRUCTURAL BIOLOGY 2022; 78:709-715. [PMID: 35647918 PMCID: PMC9159285 DOI: 10.1107/s2059798322003928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 04/10/2022] [Indexed: 11/20/2022]
Abstract
Racemic crystallography benefits the identification of a structural form of a DNA sequence that was not previously observed for the enantiopure equivalent. The ease with which racemic mixtures crystallize compared with the equivalent chiral systems is routinely taken advantage of to produce crystals of small molecules. However, biological macromolecules such as DNA and proteins are naturally chiral, and thus the limited range of chiral space groups available hampers the crystallization of such molecules. Inspiring work over the past 15 years has shown that racemic mixtures of proteins, which were made possible by impressive advances in protein chemical synthesis, can indeed improve the success rate of protein crystallization experiments. More recently, the racemic crystallization approach was extended to include nucleic acids as a possible aid in the determination of enantiopure DNA crystal structures. Here, findings are reported that suggest that the benefits may extend beyond this. Two racemic crystal structures of the DNA sequence d(CCCGGG) are described which were found to fold into A-form DNA. This form differs from the Z-form DNA conformation adopted by the chiral equivalent in the solid state, suggesting that the use of racemates may also favour the emergence of new conformations. Importantly, the racemic mixture forms interactions in the solid state that differ from the chiral equivalent (including the formation of racemic pseudo-helices), suggesting that the use of racemic DNA mixtures could provide new possibilities for the design of precise self-assembled nanomaterials and nanostructures.
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19
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McQuaid KT, Takahashi S, Baumgaertner L, Cardin DJ, Paterson NG, Hall JP, Sugimoto N, Cardin CJ. Ruthenium Polypyridyl Complex Bound to a Unimolecular Chair-Form G-Quadruplex. J Am Chem Soc 2022; 144:5956-5964. [PMID: 35324198 PMCID: PMC8991003 DOI: 10.1021/jacs.2c00178] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
![]()
The DNA G-quadruplex
is known for forming a range of topologies
and for the observed lability of the assembly, consistent with its
transient formation in live cells. The stabilization of a particular
topology by a small molecule is of great importance for therapeutic
applications. Here, we show that the ruthenium complex Λ-[Ru(phen)2(qdppz)]2+ displays enantiospecific G-quadruplex
binding. It crystallized in 1:1 stoichiometry with a modified human
telomeric G-quadruplex sequence, GGGTTAGGGTTAGGGTTTGGG (htel21T18), in an antiparallel chair topology, the first structurally
characterized example of ligand binding to this topology. The lambda
complex is bound in an intercalation cavity created by a terminal
G-quartet and the central narrow lateral loop formed by T10–T11–A12. The two remaining wide
lateral loops are linked through a third K+ ion at the
other end of the G-quartet stack, which also coordinates three thymine
residues. In a comparative ligand-binding study, we showed, using
a Klenow fragment assay, that this complex is the strongest observed
inhibitor of replication, both using the native human telomeric sequence
and the modified sequence used in this work.
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Affiliation(s)
- Kane T McQuaid
- Department of Chemistry, University of Reading, Whiteknights, Reading RG6 6AD, U.K
| | - Shuntaro Takahashi
- FIBER (Frontier Institute for Biomolecular Engineering Research), Konan University, 7-1-20 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Lena Baumgaertner
- Department of Chemistry, University of Reading, Whiteknights, Reading RG6 6AD, U.K
| | - David J Cardin
- Department of Chemistry, University of Reading, Whiteknights, Reading RG6 6AD, U.K
| | - Neil G Paterson
- Diamond Light Source Ltd., Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE, U.K
| | - James P Hall
- School of Pharmacy, University of Reading, Whiteknights, Reading RG6 6AD, U.K
| | - Naoki Sugimoto
- FIBER (Frontier Institute for Biomolecular Engineering Research), Konan University, 7-1-20 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan.,FIRST (Graduate School of Frontiers of Innovative Research in Science and Technology), Konan University, 7-1-20 Minatojima-Minamimashi, Chuo-Ku, Kobe 650-0047, Japan
| | - Christine J Cardin
- Department of Chemistry, University of Reading, Whiteknights, Reading RG6 6AD, U.K
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20
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Korn SM, Schlundt A. Structures and nucleic acid-binding preferences of the eukaryotic ARID domain. Biol Chem 2022; 403:731-747. [PMID: 35119801 DOI: 10.1515/hsz-2021-0404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/17/2022] [Indexed: 12/28/2022]
Abstract
The DNA-binding AT-rich interactive domain (ARID) exists in a wide range of proteins throughout eukaryotic kingdoms. ARID domain-containing proteins are involved in manifold biological processes, such as transcriptional regulation, cell cycle control and chromatin remodeling. Their individual domain composition allows for a sub-classification within higher mammals. ARID is categorized as binder of double-stranded AT-rich DNA, while recent work has suggested ARIDs as capable of binding other DNA motifs and also recognizing RNA. Despite a broad variability on the primary sequence level, ARIDs show a highly conserved fold, which consists of six α-helices and two loop regions. Interestingly, this minimal core domain is often found extended by helices at the N- and/or C-terminus with potential roles in target specificity and, subsequently function. While high-resolution structural information from various types of ARIDs has accumulated over two decades now, there is limited access to ARID-DNA complex structures. We thus find ourselves left at the beginning of understanding ARID domain target specificities and the role of accompanying domains. Here, we systematically summarize ARID domain conservation and compare the various types with a focus on their structural differences and DNA-binding preferences, including the context of multiple other motifs within ARID domain containing proteins.
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Affiliation(s)
- Sophie Marianne Korn
- Institute for Molecular Biosciences and Center for Biomolecular Magnetic Resonance (BMRZ), Goethe-University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
| | - Andreas Schlundt
- Institute for Molecular Biosciences and Center for Biomolecular Magnetic Resonance (BMRZ), Goethe-University Frankfurt, Max-von-Laue-Str. 9, D-60438 Frankfurt, Germany
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21
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Berman HM, Gierasch LM. How the Protein Data Bank changed biology: An introduction to the JBC Reviews thematic series, part 1. J Biol Chem 2021; 296:100608. [PMID: 33785358 PMCID: PMC8086130 DOI: 10.1016/j.jbc.2021.100608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
This collection of articles celebrates the 50th anniversary of the Protein Data Bank (PDB), the single global digital archive of biological macromolecular structures. The impact of the PDB is immense; we have invited a number of top researchers in structural biology to illustrate its influence on an array of scientific fields. What emerges is a compelling picture of the synergism between the PDB and the explosive progress witnessed in many scientific areas. Availability of reliable, openly accessible, well-archived structural information has arguably had more impact on cell and molecular biology than even some of the enabling technologies such as PCR. We have seen the science move from a time when structural biologists contributed the lion’s share of the structures to the PDB and for discussion within their community to a time when any effort to achieve in-depth understanding of a biochemical or cell biological question demands an interdisciplinary approach built atop structural underpinnings.
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Affiliation(s)
- Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, California, USA.
| | - Lila M Gierasch
- Departments of Biochemistry & Molecular Biology and Chemistry, University of Massachusetts, Amherst, Massachusetts, USA.
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