Collaborative Research: Topological Characterization of DNA Organization in Bacteriophages

San Francisco State University, San Francisco CA

Investigators

Abstract

Macromolecular self-assembly processes are key players in the complex network of interactions that take place in every organism. In viruses proper self-assembly of the viral proteins and of the packaged genome determine the formation of an infective virus. Two critical and interrelated aspects of this pathway that remain mostly unknown in double-stranded DNA (dsDNA) viruses are the packing reaction and the subsequent folding of the DNA genome inside the virus. The aim of this project is to make progress towards a better understanding of such DNA packing and folding under a condensed condition in general. The long-term goal of this project is to provide a detailed quantitative description of the DNA packing and folding processes in dsDNA viruses. There are two specific objectives in the proposal. The first one is to revisit and improve current models of DNA folding in bacteriophages. These models of DNA folding will be tested for knotting using computer simulations. The PIs will determine how closely the knot distributions they generate match the distributions of knots observed experimentally in bacteriophage P4. This information will be used to guide the rejection or modification and improvement of the models. Physical parameters such as the chain flexibility of these improved models will be estimated. Resulting models will be validated by the criterion whether they produce DNA density maps that can be compared to cryoEM data. The second objective is to reconstruct the DNA folding inside bacteriophage P4 using topology and stochastic processes. Data from DNA knots observed experimentally implicates a certain degree of randomness in the DNA packing in bacteriophages. Starting from a completely random model and using the knot distribution from P4 as a guide, the PIs will develop a model that accounts for the knots observed experimentally. This model will be compared against models obtained in the first objective and also against DNA density maps from CryoEM data. Double-stranded DNA (dsDNA) viruses cause a number of human diseases ranging from the common cold to certain cancers. If drugs and vaccines are to be developed to neutralize the effects of these viruses, a better understanding of the self-assembly pathway through which infective viruses are produced and replicated is needed. Results obtained in the laboratory have yielded only partial information. Part of the reason is that because the extreme levels of condensation to which DNA is subjected during packing and replication, the behavior of the DNA is difficult to detect experimentally. Furthermore, though various models of the process of DNA packing have been proposed, none of these adequately accounts for the experimental data that have been obtained. The PIs will employ mathematical modeling, using a tool called knot theory, to develop and test a more refined theory of DNA packing and folding in dsDNA viruses. Computer simulations based on existing models will be tested against experimental data obtained on bacteriophage P4 to determine which features of the models best predict the experimental results. The aim of this work is to generate a revised model which can more fully account for the experimental data. This work will advance knowledge of DNA folding and packing in viruses and will make an important contribution to the study of chromosome structure and dynamics, leading to a better understanding of biological processes of replication, transcription, segregation and repair. Additionally, this work will advance the mathematical and computational theory of random walks under confinement. Since random walk theory is an important and commonly used tool in other scientific studies dealing with random string like objects (such as long polymer chains), this work can potentially benefit other fields of science as well. Research tools developed through the project will be made freely available to the scientific community.