title: Discretized torsional dynamics and the folding of an RNA chain.
abstract: The aim of this work is to implement a discrete coarse codification of local torsional states of the RNA chain backbone in order to explore the long-time limit dynamics and ultimately obtain a coarse solution to the RNA folding problem. A discrete representation of the soft-mode dynamics is turned into an algorithm for a rough structure prediction. The algorithm itself is inherently parallel, as it evaluates concurrent folding possibilities by pattern recognition, but it may be implemented in a personal computer as a chain of perturbation-translation-renormalization cycles performed on a binary matrix of local topological constraints. This requires suitable representational tools and a periodic quenching of the dynamics for system renormalization. A binary coding of local topological constraints associated with each structural motif is introduced, with each local topological constraint corresponding to a local torsional state. This treatment enables us to adopt a computation time step far larger than hydrodynamic drag time scales. Accordingly, the solvent is no longer treated as a hydrodynamic drag medium. Instead we incorporate its capacity for forming local conformation-dependent dielectric domains. Each translation of the matrix of local topological constraints (LTM's) depends on the conformation-dependent local dielectric created by a confined solvent. Folding pathways are resolved as transitions between patterns of locally encoded structural signals which change within the 1 ns-100 ms time scale range. These coarse folding pathways are generated by a search at regular intervals for structural patterns in the LTM. Each pattern is recorded as a base-pairing pattern (BPP) matrix, a consensus-evaluation operation subject to a renormalization feedback loop. Since several mutually conflicting consensus evaluations might occur at a given time, the need arises for a probabilistic approach appropriate for an ensemble of RNA molecules. Thus, a statistical dynamics of consensus formation is determined by the time evolution of the base pairing probability matrix. These dynamics are generated for a functional RNA molecule, a representative of the so-called group I ribozymes, in order to test the model. The resulting ensemble of conformations is sharply peaked and the most probable structure features the predominance of all phylogenetically conserved intrachain helices tantamount to ribozyme function. Furthermore, the magnesium-aided cooperativity that leads to the shaping of the catalytic core is elucidated. Once the predictive folding algorithm has been implemented, the validity of the so-called "adiabatic approximation" is tested. This approximation requires that conformational microstates be lumped up into BPP's which are treated as quasiequilibrium states, while folding pathways are coarsely represented as sequences of BPP transitions. To test the validity of this adiabatic ansatz, a computation of the coarse Shannon information entropy sigma associated to the specific partition of conformation space into BPP's is performed taking into account the LTM evolution and contrasted with the adiabatic computation. The results reveal a subordination of torsional microstate dynamics to BPP transitions within time scales relevant to folding. This adiabatic entrainment in the long-time limit is thus identified as responsible for the expediency of the folding process.
authors.affiliation: Instituto de Matemática, Universidad Nacional del Sur, Consejo Nacional de Investigaciones Científicas y Técnicas, Avenida Alem 1253, Bahía Blanca 8000, Argentina. arifer@criba.edu.ar
authors.forename: A
authors.initials: A
authors.lastname: Fernández
authors.forename: R
authors.initials: R
authors.lastname: Salthú
authors.forename: H
authors.initials: H
authors.lastname: Cendra
mesh_terms: D000465:Algorithms; D020029:Base Pairing; D003198:Computer Simulation / Q000706:statistics & numerical data; D016384:Consensus Sequence; D005544:Forecasting / Q000379:methods; D008432:Mathematical Computing; D008838:Microcomputers / Q000706:statistics & numerical data; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry* / Q000235:genetics; D012985:Software Design; D013314:Stress, Mechanical; D013816:Thermodynamics
chemical_list: D012313:RNA