scholarly journals Accurate modeling of DNA conformational flexibility by a multivariate Ising model

2021 ◽  
Vol 118 (15) ◽  
pp. e2021263118
Author(s):  
Korbinian Liebl ◽  
Martin Zacharias
Keyword(s):  
Ising Model ◽  
Nearest Neighbor ◽  
Regulation Of Gene Expression ◽  
Harmonic Approximation ◽  
Conformational Flexibility ◽  
Dna Elasticity ◽  
Conformational Substates ◽  
Sequence Dependent ◽  
Nucleosome Formation ◽  
Wide Range

The sequence-dependent structure and deformability of DNA play a major role for binding of proteins and regulation of gene expression. So far, most efforts to model DNA flexibility are based on unimodal harmonic stiffness models at base-pair resolution. However, multimodal behavior due to distinct conformational substates also contributes significantly to the conformational flexibility of DNA. Moreover, these local substates are correlated to their nearest-neighbor substates. A description for DNA elasticity which includes both multimodality and nearest-neighbor coupling has remained a challenge, which we solve by combining our multivariate harmonic approximation with an Ising model for the substates. In a series of applications to DNA fluctuations and protein–DNA complexes, we demonstrate substantial improvements over the unimodal stiffness model. Furthermore, our multivariate Ising model reveals a mechanical destabilization for adenine (A)-tracts to undergo nucleosome formation. Our approach offers a wide range of applications to determine sequence-dependent deformation energies of DNA and to investigate indirect readout contributions to protein–DNA recognition.

The main role of the sequence-dependent DNA elasticity in determining the free energy of nucleosome formation on telomeric DNAs

2000 ◽  
Vol 83 (3) ◽  
pp. 223-237 ◽  
Author(s):  
Ilaria Filesi ◽  
Stefano Cacchione ◽  
Pasquale De Santis ◽  
Luigi Rossetti ◽  
Maria Savino
Keyword(s):  
Free Energy ◽  
Main Role ◽  
Dna Elasticity ◽  
Sequence Dependent ◽  
Nucleosome Formation

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

LATTICE DYNAMICS OF THE BINARY APERIODIC CHAINS OF ATOMS I: FRACTAL DIMENSION OF PHONON SPECTRA

1995 ◽  
Vol 09 (12) ◽  
pp. 1429-1451 ◽  
Author(s):  
WŁODZIMIERZ SALEJDA
Keyword(s):  
Fractal Dimension ◽  
Lattice Dynamics ◽  
Nearest Neighbor ◽  
Average Distance ◽  
Local Maximum ◽  
Fractal Dimensions ◽  
Dimension Formula ◽  
Phonon Spectra ◽  
Wide Range ◽  
Silver Mean

The microscopic harmonic model of lattice dynamics of the binary chains of atoms is formulated and studied numerically. The dependence of spring constants of the nearest-neighbor (NN) interactions on the average distance between atoms are taken into account. The covering fractal dimensions [Formula: see text] of the Cantor-set-like phonon spec-tra (PS) of generalized Fibonacci and non-Fibonaccian aperiodic chains containing of 16384≤N≤33461 atoms are determined numerically. The dependence of [Formula: see text] on the strength Q of NN interactions and on R=mH/mL, where mH and mL denotes the mass of heavy and light atoms, respectively, are calculated for a wide range of Q and R. In particular we found: (1) The fractal dimension [Formula: see text] of the PS for the so-called goldenmean, silver-mean, bronze-mean, dodecagonal and Severin chain shows a local maximum at increasing magnitude of Q and R>1; (2) At sufficiently large Q we observe power-like diminishing of [Formula: see text] i.e. [Formula: see text], where α=−0.14±0.02 and α=−0.10±0.02 for the above specified chains and so-called octagonal, copper-mean, nickel-mean, Thue-Morse, Rudin-Shapiro chain, respectively.

Regulation of type II adenylyl cyclase mRNA in rabbit skeletal muscle by chronic motor nerve pacing

1996 ◽  
Vol 271 (2) ◽  
pp. E253-E260 ◽  
Author(s):  
C. E. Torgan ◽  
W. E. Kraus
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Protein Kinase ◽  
Adenylyl Cyclase ◽  
Regulation Of Gene Expression ◽  
Rabbit Skeletal Muscle ◽  
Type Ii ◽  
Wide Range ◽  
Electrical Pacing ◽  
Fast Twitch

Skeletal muscle exhibits a wide range in functional phenotype in response to changes in physiological demands. We have observed that, in response to changes in work patterns, alterations in gene expression of some proteins coincide with changes in adenylyl cyclase (AC) activity [Kraus, W.E., J.P. Longabaugh, and S. B. Liggett. Am. J. Physiol 263 (Endocrinol. Metab. 26): E266-E230, 1992]. We now examine AC isoform transcript prevalence in various rabbit skeletal muscles and in response to changing work demands. Using reverse transcriptase-polymerase chain reaction, we detected type II AC isoform transcripts in rabbit skeletal muscle. Ribonuclease protection analyses revealed that expression of the type II isoform significantly correlated with the percentage of fast-twitch type IIb/IId fibers (r2 = 0.765, P < 0.01). When a fast-twitch muscle was converted to a slow-twitch muscle via chronic electrical pacing, expression of type II AC mRNA significantly decreased. This response occurred 3 days after the onset of stimulation (78% decrease) and was still present after 21 days of stimulation (76% decrease). As type II AC is relatively insensitive to calcium regulation while sensitive to protein kinase C (PKC) signaling, these data provide further impetus for investigations of protein kinase A and PKC cross-talk signaling mechanisms in the regulation of gene expression.

scholarly journals Computational QSAR model combined molecular descriptors and fingerprints to predict HDAC1 inhibitors

2018 ◽  
Vol 34 ◽  
pp. 52-58 ◽  
Author(s):  
Jingsheng Shi ◽  
Guanglei Zhao ◽  
Yibing Wei
Keyword(s):  
Histone Deacetylases ◽  
Computational Models ◽  
Nearest Neighbor ◽  
Dynamic Balance ◽  
Regulation Of Gene Expression ◽  
Hdac Inhibitors ◽  
Qsar Model ◽  
Support Vector ◽  
C4.5 Decision Tree ◽  
Fold Cross Validation

The dynamic balance between acetylation and deacetylation of histones plays a crucial role in the epigenetic regulation of gene expression. It is equilibrated by two families of enzymes: histone acetyltransferases and histone deacetylases (HDACs). HDACs repress transcription by regulating the conformation of the higher-order chromatin structure. HDAC inhibitors have recently become a class of chemical agents for potential treatment of the abnormal chromatin remodeling process involved in certain cancers. In this study, we constructed a large dataset to predict the activity value of HDAC1 inhibitors. Each compound was represented with seven fingerprints, and computational models were subsequently developed to predict HDAC1 inhibitors via five machine learning methods. These methods include naïve Bayes, κ-nearest neighbor, C4.5 decision tree, random forest, and support vector machine (SVM) algorithms. The best predicting model was CDK fingerprint with SVM, which exhibited an accuracy of 0.89. This model also performed best in five-fold cross-validation. Some representative substructure alerts responsible for HDAC1 inhibitors were identified by using MoSS in KNIME, which could facilitate the identification of HDAC1 inhibitors.

scholarly journals Stem–loop formation drives RNA folding in mechanical unzipping experiments

2022 ◽  
Vol 119 (3) ◽  
pp. e2025575119
Author(s):  
Paolo Rissone ◽  
Cristiano V. Bizarro ◽  
Felix Ritort
Keyword(s):  
Optical Tweezers ◽  
Rna Structure ◽  
Nearest Neighbor ◽  
Rna Folding ◽  
General Mechanism ◽  
Loop Formation ◽  
Stem Loop ◽  
Wide Range ◽  
Irreversible Effects ◽  
And Function

Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force–distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping–rezipping process, we introduce a barrier energy landscape of the stem–loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem–loops that kinetically trap the long-lived intermediates observed in the FDC. Stem–loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.

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