Model studies of the effects of intracellular crowding on nucleic acid interactions

The assembly of bacteriophages provides experimental model systems for the study of regulation at the level of gene products. We discuss the hypothesis of regulation through sequentially induced conformational changes by which precursor-assemblies become ready at a specific stage of maturation to interact with an additional gene product or nucleic acids. Phage mutants provide excellent experimental model systems for studying, for example, the role and fate of the core in the prehead assembly. The conservative maturation of the prehead to the final, stable head consists of several steps whose complexity reflect that of the virus. It includes packaging of DNA. The surface lattice of maturing preheads apparently undergoes several steps characterized by different conformational states as suggested by in vitro studies on a morphological variant of the prehead, the polyhead of phage T4 (Steven, Couture, Aebi & Showe 1976; Laemmli, Amos & Klug 1976). Addition of a partly purified, enriched proteolytic fraction - which is gene 21-dependent - to empty purified polyheads leads to different conformational states. These seem to go in a direction approaching the structure of the surface of finished capsids as studied by Aebi et al . on gene 24 related (Bijlenga, Aebi & Kellenberger 1976) and other genetically defined giant-variants of T4 phage (Doermann, Eiserling & Boehner 1973). We show some experiments which suggest that high cooperativity is responsible for the stabilization of capsids. The activation energy necessary for dissociation of capsids is very high, 247 kJ for T4 capsids, and 10% smaller for T2. Once the energy barrier has been overcome, the capsids are first structurally modified before they undergo partial and finally complete dissociation.

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Aminoglycoside−Nucleic Acid Interactions: Remarkable Stabilization of DNA and RNA Triple Helices by Neomycin

Journal of the American Chemical Society ◽

10.1021/ja003052x ◽

2001 ◽

Vol 123 (23) ◽

pp. 5385-5395 ◽

Cited By ~ 66

Author(s):

Dev P. Arya ◽

R. Lane Coffee ◽

Bert Willis ◽

Anna I. Abramovitch

Keyword(s):

Nucleic Acid ◽

Dna And Rna ◽

Triple Helices ◽

Nucleic Acid Interactions

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ChemInform Abstract: Aminoglycoside-Nucleic Acid Interactions: Remarkable Stabilization of DNA and RNA Triple Helices by Neomycin.

ChemInform ◽

10.1002/chin.200138197 ◽

2010 ◽

Vol 32 (38) ◽

pp. no-no

Author(s):

Dev P. Arya ◽

R. Lane Coffee Jr. ◽

Bert Willis ◽

Anna I. Abramovitch

Keyword(s):

Nucleic Acid ◽

Dna And Rna ◽

Triple Helices ◽

Nucleic Acid Interactions

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Convolutional models of RNA energetics

10.1101/470740 ◽

2018 ◽

Author(s):

Michelle J. Wu

Keyword(s):

Nucleic Acid ◽

Network Architecture ◽

Nearest Neighbor ◽

Current Model ◽

Mrna Translation ◽

Large Space ◽

Dna And Rna ◽

Nucleic Acid Interactions

AbstractNucleic acid molecular biology and synthetic biology are undergoing rapid advances with the emergence of designer riboswitches controlling living cells, CRISPR/Cas9-based genome editing, high-throughput RNA-based silencing, and reengineering of mRNA translation. Many of these efforts require the design of nucleic acid interactions, which relies on accurate models for DNA and RNA energetics. Existing models utilize nearest neighbor rules, which were parameterized through careful optical melting measurements. However, these relatively simple rules often fail to quantitatively account for the biophysical behavior of molecules even in vitro, let alone in vivo. This is due to the limited experimental throughput of optical melting experiments and the infinitely large space of possible motifs that can be formed. Here, we present a convolutional neural network architecture to model the energies of nucleic acid motifs, allowing for learning of representations of physical interactions that generalize to arbitrary unmeasured motifs. First, we used existing parameterizations of motif energies to train the model and demonstrate that our model is expressive enough to recapitulate the current model. Then, through training on optical melting datasets from the literature, we have shown that the model can accurately predict the thermodynamics of hairpins containing unmeasured motifs. This work demonstrates the utility of convolutional models for capturing the thermodynamic parameters that underlie nucleic acid interactions.

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Systems Biology and Experimental Model Systems of Cancer

Journal of Personalized Medicine ◽

10.3390/jpm10040180 ◽

2020 ◽

Vol 10 (4) ◽

pp. 180

Author(s):

Gizem Damla Yalcin ◽

Nurseda Danisik ◽

Rana Can Baygin ◽

Ahmet Acar

Keyword(s):

Systems Biology ◽

Experimental Model ◽

High Throughput Sequencing ◽

Critical Role ◽

Genetic Alterations ◽

Model Systems ◽

Treatment Modalities ◽

Omics Data ◽

Cancer Systems Biology ◽

Experimental Model Systems

Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

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