We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the successful probabilistic roadmap motion planners that we have previously successfully applied to protein folding. The key advantage of our method is that it provides a sparse map that captures the main features of the landscape and which can be analyzed to compute folding kinetics. In this paper, we provide evidence that this approach is also well suited to RNA. We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with results of other existing methods. Categories and Subject Descriptors: Medical Sciences]: Biology and genetics

. RNA secondary structures provide a unique computer model for investigating the most important aspects of structural and evolutionary biology. The existence of efficient algorithms for solving the folding problem, i.e., for predicting the secondary structure given only the sequence, allows the construction of realistic computer simulations. The notion of a "landscape" 2 C. Flamm, I.L. Hofacker, P.F. Stadler underlies both the structure formation (folding) and the (in vitro) evolution of RNA. Evolutionary adaptation may be seen as hill climbing process on a fitness landscape which is determined by the phenotype of the RNA molecule (within the model this is its secondary structure) and the selection constraints acting on the molecules. We find that a substantial fraction of point mutations do not change an RNA secondary structure. On the other hand, a comparable fraction of mutations leads to very different structures. This interplay of smoothness and ruggedness (or robust...

During four years of arduous service, a Ph. D. student is expected to familiarise himself with his field of research, and, hopefully, contribute to this field. This is reflected by the division of this dissertation into two parts. Part I is a (partial) overview of the field of computational biology as I conceive it, an overview that is aimed at presenting the context for my contributions to the field of computational biology. These contributions are presented in part II as five independent articles

Abstract. While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer’s disease are associated with protein misfolding and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA folding kinetics can regulate gene expression at the translational level. Knowledge of the stability, folding, kinetics and detailed mechanics of the folding process may help provide insight into how proteins and RNAs fold. In this paper, we present an overview of our work with a computational method we have adapted from robotic motion planning to study molecular motions. We have validated against experimental data and have demonstrated that our method can capture biological results such as stochastic folding pathways, population kinetics of various conformations, and relative folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes of both proteins and RNAs. We have validated these techniques by showing that we observe the same relative folding rates as shown in experiments for structurally similar protein molecules that exhibit different folding behaviors. Our analysis has also been able to predict the same relative gene expression rate for wild-type MS2 phage RNA and three of its mutants. 1. Introduction. Molecular

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Molecular motions, including both protein and RNA, play an essential role in many biochemical processes. Simulations have attempted to study these detailed large-scale molecular motions, but they are often limited by the expense of representing complex molecular structures. For example, enumerating all possible RNA conformations with valid contacts is an exponential endeavor, and the complexity of protein motion increases with the model’s detail and protein length. In this paper, we explore the use of dimensionality reduction techniques to better approximate protein and RNA motions. We present two new methods to study motions: (1) an evaluation technique to compare different distributions of conformations and (2) a way to identify likely local motion transitions. We combine these two methods in an existing motion framework to study large-scale motions for both proteins and RNA. We show that dimensionality reduction can be effectively applied, even to discrete conformation spaces (as for RNA secondary structure) that do not typically lend themselves to reduction techniques. 1