Computational investigations of structure probing experiments for RNA structure prediction

Ribonucleic acids (RNA) transcripts, and in particular non-coding RNAs, play fundamental roles in cellular metabolism, as they are involved in protein synthesis, catalysis, and regulation of gene expression. In some cases, an RNA\u2019s biological function is mostly dependent on a specific active conformation, making the identification of this single stable structure crucial to identify the role of the RNA and the relationships between its mutations and diseases. On the other hand, RNAs are often found in a dynamic equilibrium of multiple interconverting conformations, that is necessary to regulate their functional activity. In these cases it becomes fundamental to gain knowledge of RNA\u2019s structural ensembles, in order to fully determine its mechanism of action. The current structure determination techniques, both for single-state models such as X-ray crystallography, and for multi-state models such as nuclear magnetic resonance and single-molecule methods, despite proving accurate and reliable in many cases, are extremely slow and costly. In contrast, chemical probing is a class of experimental techniques that provide structural information at single-nucleotide resolution at significantly lower costs in terms of time and required infrastructures. In particular, selective 2\u2032 hydroxyl acylation analyzed via primer extension (SHAPE) has proved a valid chemical mapping technique to probe RNA structure even in vivo. This thesis reports a systematic investi- gation of chemical probing experiments based on two different approaches. The first approach, presented in Chapter 2, relies on machine-learning techniques to optimize a model for mapping experimental data into structural information. The model relies also on co-evolutionary data, in the form of direct coupling analysis (DCA) couplings. The inclusion of this kind of data is chosen in the same spirit of reducing the costs of structure probing, as co-evolutionary analysis relies only on sequencing techniques. The resulting model is proposed as a candidate standard tool for prediction of RNA secondary structure, and some insight in the mechanism of chemical probing is gained by interpreting back its features. Importantly, this work has been developed in the per- spective of building a framework for future refinement and improvement. In this spirit, all the used data and scripts are available at https://github.com/bussilab/shape-dca-data, and the model can be easily retrained and adapted to incorporate arbitrary experimental informa- tion. As the interpretation of the model features suggests the possible emergence of cooperative effects involving RNA nucleotides interacting with SHAPE reagents, a second approach based on Molecular Dynamics simulations is proposed to investigate this hypothesis. The results, along with an originally developed methodology to analyse Molecular Dynamics simulations at variable number of particles, are presented in Chapter 3

Frustration in Biomolecules

Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.Comment: 97 pages, 30 figure

Modelling DNA Origami Self-Assembly at the Domain Level

We present a modelling framework, and basic model parameterization, for the study of DNA origami folding at the level of DNA domains. Our approach is explicitly kinetic and does not assume a specific folding pathway. The binding of each staple is associated with a free-energy change that depends on staple sequence, the possibility of coaxial stacking with neighbouring domains, and the entropic cost of constraining the scaffold by inserting staple crossovers. A rigorous thermodynamic model is difficult to implement as a result of the complex, multiply connected geometry of the scaffold: we present a solution to this problem for planar origami. Coaxial stacking and entropic terms, particularly when loop closure exponents are taken to be larger than those for ideal chains, introduce interactions between staples. These cooperative interactions lead to the prediction of sharp assembly transitions with notable hysteresis that are consistent with experimental observations. We show that the model reproduces the experimentally observed consequences of reducing staple concentration, accelerated cooling and absent staples. We also present a simpler methodology that gives consistent results and can be used to study a wider range of systems including non-planar origami

Energy Landscapes and Hybridization Pathways for DNA Hexamer Duplexes.

Strand hybridization is not only a fundamental molecular mechanism underlying the biological functions of nucleic acids but is also a key step in the design of efficient nanodevices. Despite recent efforts, the microscopic rules governing the hybridization mechanisms remain largely unknown. In this study, we exploit the energy landscape framework to assess how sequence-specificity modulates the hybridization mechanisms in DNA. We find that GG-tracts hybridize much more rapidly compared to GC-tracts, via either zippering or slithering pathways. For the hybridization of GG-tracts, both zippering and slithering mechanisms appear to be kinetically relevant. In contrast, for the GC-tracts, the zippering mechanism is dominant. Our work reveals that even for the relatively small systems considered, the energy landscapes feature multiple metastable states and kinetic traps, which is at odds with the conventional "all-or-nothing" model of DNA hybridization formulated on the basis of thermodynamic arguments alone. Interestingly, entropic effects are found to play an important role in determining the thermal stability of competing conformational ensembles and in determining the preferred hybridization pathways.epsr

The biomimetic, rational, and quanitative design of cooperative receptors and responsive materials

Because the ability to effectively detect and respond to subtle chemical cues is so crucial to biological function, evolution has invented many diverse, intricate mechanisms for the robust, precise sensing of molecular stimuli. The ability to systematically recreate such mechanisms in artificial systems would likewise be useful in many biotechnologies, for example in biosensors, synthetic biology, and targeted drug delivery. In response, the work I present here focuses on the rational, quantitative recreation of biological mechanisms of sensing and actuation in artificial biomolecular systems. The first aim of my thesis work, detailed in Chapters 2 and 3, centered on the rational engineering of allosteric cooperativity into normally non-cooperative artificial receptors. This mechanism, which occurs when multiple copies of identical target molecule bind to a receptor in an “all-or-nothing” fashion, increases the order of the binding curve, narrowing the transition window between bound and unbound, and enhancing the receptors’ sensitivity to small changes in target concentration. To achieve this effect requires that the first copy of target molecule to bind shift the receptor from a low-affinity to a high-affinity conformation, thus increasing its affinity for the binding of subsequent copies of target molecule. Chapter 2 centers on proof-of-concept efforts to engineering this mechanism into a particularly simple and well-understood model receptor, a DNA-binding molecular beacon. I follow this in Chapter 3 with the development of a disorder-based strategy suitable for the introduction of cooperativity into more complex receptors, including even those of unknown structure. The second aim of my thesis work, detailed in Chapters 4 and 5, focused on the engineering of multicomponent, stimulus-responsive biomolecular systems from a different perspective, specifically on the development of a quantitative understanding of the physics of stimulus-responsive materials. Materials that assemble and dissolve in response to chemical stimuli are ubiquitous in Biology, for example in transport vesicles, viruses, and cell membranes. As with the imitation of allosteric cooperativity, the ability to rationally imitate the properties of these materials in an artificial, technological context would be useful in many applications. Although there exist many successful examples of such artificial, stimulus-responsive materials, design efforts thus far have been fairly qualitative, and systematic approaches to systematically control their properties do not exist. Part of the reason for this is that the relationship between properties such as network architecture and cooperativity, thermodynamic stability and molecular and micron scale behavior are not well understood, and there is a lack of simple, quantitative techniques for measuring the response of these materials. In response, I developed simple, straightforward techniques to measure the dissolution of a model hydrogel simultaneously at both the molecular and micron length scales, which I describe in Chapter 4. In Chapter 5, I employed these methods to explore the relationship between the thermodynamic stability and response kinetics of a model hydrogel, demonstrating the ability to quantitatively control the materials response kinetics using simple strategies previously applied for the quantitative tuning of solution-phase biomolecular switches

Monte Carlo simulation studies of DNA hybridization and DNA-directed nanoparticle assembly

A coarse-grained lattice model of DNA oligonucleotides is proposed to investigate how fundamental thermodynamic processes are encoded by the nucleobase sequence at the microscopic level, and to elucidate the general mechanisms by which single-stranded oligonucleotides hybridize to their complements either in solution or when tethered to nanoparticles. Molecular simulations based on a high-coordination cubic lattice are performed using the Monte Carlo method. The dependence of the model's thermal stability on sequence complementarity is shown to be qualitatively consistent with experiment and statistical mechanical models. From the analysis of the statistical distribution of base-paired states and of the associated free-energy landscapes, two general hybridization scenarios are found. For sequences that do not follow a two-state process, hybridization is weakly cooperative and proceeds in multiple sequential steps involving stable intermediates with increasing number of paired bases. In contrast, sequences that conform to two-state thermodynamics exhibit moderately rough landscapes, in which multiple metastable intermediates appear over broad free-energy barriers. These intermediates correspond to duplex species that bridge the configurational and energetic gaps between duplex and denatured states with minimal loss of conformational entropy, and lead to a strongly cooperative hybridization. Remarkably, two-state thermodynamic signatures are generally observed in both scenarios. The role of cooperativity in the assembly of nanoparticles tethered with model DNA oligonucleotides is similarly addressed with the Monte Carlo method, where nanoparticles are represented as finely discretized hard-core spheres on a cubic lattice. The energetic and structural mechanisms of self-assembling are investigated by simulating the aggregation of small "satellite" particles from the bulk onto a large "core" particle. A remarkable enhancement of the system's thermal stability is attained by increasing the number of strands per satellite particle available to hybridize with those on the core particle. This cooperative process is driven by the formation of multiple bridging duplexes under favorable conditions of reduced translational entropy and the resultant energetic compensation; this behavior rapidly weakens above a certain threshold of linker strands per satellite particle. Cooperativity also enhances the structural organization of the assemblies by systematically narrowing the radial distribution of the satellite particles bound the core

Monte Carlo simulation of DNA origami self-assembly

The optimal design of DNA origami systems that assemble rapidly and robustly is hampered by the lack of a model capable of simulating the self-assembly process that is sufficiently detailed yet computationally tractable. In this thesis, we propose a model for DNA origami that strikes a balance between these two criteria by representing DNA origami systems on a lattice at the level of binding domains. Particular attention is paid to the constraints imposed by the double-helical twist, as they determine where strand crossovers between adjacent helices can occur. Because of the highly specific types of interaction and the length of the scaffold, standard Monte Carlo simulation methods for polymeric systems are found to be ineffective at sampling the dense, near-assembled states considered here. In order to address the issue of sampling such states, we develop Monte Carlo methods that extend the configurational bias and recoil growth methods, and consider the sampling of scaffold conformations independently from the sampling of staple binding states. We demonstrate the validity of our model and the feasibility of our sampling methods with simulations of a small origami design previously studied with the oxDNA model, as well as with designs that include staples that span longer scaffold segments. In other self-assembling systems, it is often the case that nucleation barriers control the self-assembly behaviour. We investigate whether there is a nucleation barrier along the self-assembly pathway of DNA origami. Our simulations reveal that for simple systems, stacking interactions govern a nucleation barrier, albeit one that is never prohibitively large relative to thermal fluctuations. These findings may prove useful in the design of DNA origami structures capable of controllable reversible folding for functional purposes and in assisting the optimization of assembly pathways at the design stage

Targeting and function of mammalian microRNAs

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 2009.Cataloged from PDF version of thesis.Includes bibliographical references.In the span of a few short years, animal microRNAs have become recognized as broad regulators of gene expression, largely in part due to our improved understanding of how animal microRNAs recognize their targets. Crucial to microRNA targeting are the ~7-nt seed sites complementary to nucleotides 2-8 at the 5' end of the microRNA. We show that protein-coding genes preferentially expressed at the same time and place as a highly expressed microRNA have evolved their 3' UTR sequence to specifically avoid seed sites matching that microRNA. In contrast, conserved sites appear to be preferentially expressed in developmental states prior to microRNA expression, and are downregulated upon induction of that microRNA. Combined with the result that both conserved and nonconserved seed sites are generally functional, our findings extend the direct and indirect influence of mammalian microRNAs to the majority of protein-coding genes. Although seed sites account for much of the specificity of microRNA regulation, they are not always sufficient for repression, suggesting the contribution of additional specificity determinants. Combining independent computational and experimental approaches, we found five general features associated with site efficacy: AU-rich nucleotide composition near the site, proximity to sites for coexpressed microRNAs, pairing outside of the seed region at microRNA nucleotides 13-16, and positioning within the 3' UTR at least 15nt from the stop codon and away from the center of long UTRs. By incorporating these five features, we are able to explain much of the differences in site efficacy for both exogenously added microRNAs and for endogenous microRNA-message interactions. We further refined the seed site motif involved in microRNA repression, by demonstrating experimentally an Adenosine preference across from the unpaired first nucleotide of the microRNA and ranking the relative effectiveness of different classes of seed sites. Although sites lacking perfect seed pairing were generally ineffective, a fraction of these sites were supplemented by detectable compensatory 3' pairing. In addition, by extending our conservation analysis to 11 genomes, we show that the confidence with which conserved target sites can be predicted is a function of the conservation of the seed site itself relative to the conservation of surrounding sequence. This allows individual conserved sites to be assigned a confidence score reflecting the likelihood that the site is being conserved due to selection rather than by chance.by Kyle Kai-How Farh.Ph. D