Computational Modeling of DNA Sequence Effects on the Nucleosome Core Particle

The nucleosome particle is an essential biological macromolecule serving both a structural and gene regulatory roles in eukaryotic genomes. The nucleosome particle has a cylindrical shape, composed of 8 highly conserved histone proteins wrapped by a sequence of 147 base pairs of DNA. Considerable, experimental evidence has shown that different sequences of 147 base pairs have varying preferences for forming stable particles, yet atomic-level descriptions for the preferences are vague at best. Microscopic descriptions contribute to fundamental understanding of genomes process, facilitate rationale approaches to drug design for certain genetic diseases, and can contribute to genetic engineering. We have established a novel basis for computational modeling DNA interactions with the nucleosome core particle. Computational modeling can approach the complexity and vast number of DNA sequences of potential interest. Our method is the first to substitute DNA on the nucleosome core and explore the rotational degree of freedom, crucial to assessing a DNA helix in preferred, low-energy states. This work was carried out along with experimental work used as a reference for the computational studies. Specifically, we experimentally determined the relative binding affinities of 3 DNA sequences for forming nucleosomes. These experimental data provide a ranking of stability as a function of sequence on the free energy of binding. Accurate free-energies of binding for large biological systems are extremely difficult to compute. The computational modeling hinges on a high-resolution crystal structure (1.9 Å) of the nucleosome core particle. In our method, we substitute DNA molecules of interest on the crystal structure in order to study the structural dynamics using sophisticated computational models centered on molecular dynamics simulations. In this work, we performed three separate molecular dynamic simulations, one for each of the sequences, in order to explore the atomic-level basis for the differentials in binding, which were determined experimentally. Crucially, the rotational degrees of freedom are explored applying novel methods to a complex geometric and chemical problem. This method uses systematical sampling and dynamical modeling of the DNA rotational conformers. Through this work we have demonstrated the feasibility and methodology for atomic-level modeling of DNA with potential for high throughput. This thesis describes methods and results of this work

Over half of breakpoints in gene pairs involved in cancer-specific recurrent translocations are mapped to human chromosomal fragile sites.

Gene rearrangements such as chromosomal translocations have been shown to contribute to cancer development. Human chromosomal fragile sites are regions of the genome especially prone to breakage, and have been implicated in various chromosome abnormalities found in cancer. However, there has been no comprehensive and quantitative examination of the location of fragile sites in relation to all chromosomal aberrations

Adaptive Accelerated Molecular Dynamics (Ad-AMD) Revealing the Molecular Plasticity of P450cam

An extended accelerated molecular dynamics (AMD) methodology called adaptive AMD is presented. Adaptive AMD (Ad-AMD) is an efficient and robust conformational space sampling algorithm that is particularly-well suited to proteins with highly structured potential energy surfaces exhibiting complex, large-scale collective conformational transitions. Ad-AMD simulations of substrate-free P450cam reveal that this system exists in equilibrium between a fully and partially open conformational state. The mechanism for substrate binding depends on the size of the ligand. Larger ligands enter the P450cam binding pocket, and the resulting substrate-bound system is trapped in an open conformation via a population shift mechanism. Small ligands, which fully enter the binding pocket, cause an induced-fit mechanism, resulting in the formation of an energetically stable closed conformational state. These results are corroborated by recent experimental studies and potentially provide detailed insight into the functional dynamics and conformational behavior of the entire cytochrome-P450 superfamily

DNA topoisomerases participate in fragility of the oncogene RET

Fragile site breakage was previously shown to result in rearrangement of the RET oncogene, resembling the rearrangements found in thyroid cancer. Common fragile sites are specific regions of the genome with a high susceptibility to DNA breakage under conditions that partially inhibit DNA replication, and often coincide with genes deleted, amplified, or rearranged in cancer. While a substantial amount of work has been performed investigating DNA repair and cell cycle checkpoint proteins vital for maintaining stability at fragile sites, little is known about the initial events leading to DNA breakage at these sites. The purpose of this study was to investigate these initial events through the detection of aphidicolin (APH)-induced DNA breakage within the RET oncogene, in which 144 APHinduced DNA breakpoints were mapped on the nucleotide level in human thyroid cells within intron 11 of RET, the breakpoint cluster region found in patients. These breakpoints were located at or near DNA topoisomerase I and/or II predicted cleavage sites, as well as at DNA secondary structural features recognized and preferentially cleaved by DNA topoisomerases I and II. Co-treatment of thyroid cells with APH and the topoisomerase catalytic inhibitors, betulinic acid and merbarone, significantly decreased APH-induced fragile site breakage within RET intron 11 and within the common fragile site FRA3B. These data demonstrate that DNA topoisomerases I and II are involved in initiating APH-induced common fragile site breakage at RET, and may engage the recognition of DNA secondary structures formed during perturbed DNA replication

Protecting High Energy Barriers: A New Equation to Regulate Boost Energy in Accelerated Molecular Dynamics Simulations

Molecular dynamics (MD) is one of the most common tools in computational chemistry. Recently, our group has employed accelerated molecular dynamics (aMD) to improve the conformational sampling over conventional molecular dynamics techniques. In the original aMD implementation, sampling is greatly improved by raising energy wells below a predefined energy level. Recently, our group presented an alternative aMD implementation where simulations are accelerated by lowering energy barriers of the potential energy surface. When coupled with thermodynamic integration simulations, this implementation showed very promising results. However, when applied to large systems, such as proteins, the simulation tends to be biased to high energy regions of the potential landscape. The reason for this behavior lies in the boost equation used since the highest energy barriers are dramatically more affected than the lower ones. To address this issue, in this work, we present a new boost equation that prevents oversampling of unfavorable high energy conformational states. The new boost potential provides not only better recovery of statistics throughout the simulation but also enhanced sampling of statistically relevant regions in explicit solvent MD simulations

On the Use of Accelerated Molecular Dynamics to Enhance Configurational Sampling in Ab Initio Simulations

We have implemented the accelerated molecular dynamics approach (Hamelberg, D.; Mongan, J.; McCammon, J. A. J. Chem. Phys. 2004, 120 (24), 11919) in the framework of ab initio MD (AIMD). Using three simple examples, we demonstrate that accelerated AIMD (A-AIMD) can be used to accelerate solvent relaxation in AIMD simulations and facilitate the detection of reaction coordinates: (i) We show, for one cyclohexane molecule in the gas phase, that the method can be used to accelerate the rate of the chair-to-chair interconversion by a factor of ∼1 × 105, while allowing for the reconstruction of the correct canonical distribution of low-energy states; (ii) We then show, for a water box of 64 H2O molecules, that A-AIMD can also be used in the condensed phase to accelerate the sampling of water conformations, without affecting the structural properties of the solvent; and (iii) The method is then used to compute the potential of mean force (PMF) for the dissociation of Na−Cl in water, accelerating the convergence by a factor of ∼3−4 compared to conventional AIMD simulations.(2) These results suggest that A-AIMD is a useful addition to existing methods for enhanced conformational and phase-space sampling in solution. While the method does not make the use of collective variables superfluous, it also does not require the user to define a set of collective variables that can capture all the low-energy minima on the potential energy surface. This property may prove very useful when dealing with highly complex multidimensional systems that require a quantum mechanical treatment

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships

Nutrition and cancer: A review of the evidence for an anti-cancer diet

It has been estimated that 30–40 percent of all cancers can be prevented by lifestyle and dietary measures alone. Obesity, nutrient sparse foods such as concentrated sugars and refined flour products that contribute to impaired glucose metabolism (which leads to diabetes), low fiber intake, consumption of red meat, and imbalance of omega 3 and omega 6 fats all contribute to excess cancer risk. Intake of flax seed, especially its lignan fraction, and abundant portions of fruits and vegetables will lower cancer risk. Allium and cruciferous vegetables are especially beneficial, with broccoli sprouts being the densest source of sulforophane. Protective elements in a cancer prevention diet include selenium, folic acid, vitamin B-12, vitamin D, chlorophyll, and antioxidants such as the carotenoids (α-carotene, β-carotene, lycopene, lutein, cryptoxanthin). Ascorbic acid has limited benefits orally, but could be very beneficial intravenously. Supplementary use of oral digestive enzymes and probiotics also has merit as anticancer dietary measures. When a diet is compiled according to the guidelines here it is likely that there would be at least a 60–70 percent decrease in breast, colorectal, and prostate cancers, and even a 40–50 percent decrease in lung cancer, along with similar reductions in cancers at other sites. Such a diet would be conducive to preventing cancer and would favor recovery from cancer as well

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe