DNA nano-mechanics: how proteins deform the double helix

It is a standard exercise in mechanical engineering to infer the external forces and torques on a body from its static shape and known elastic properties. Here we apply this kind of analysis to distorted double-helical DNA in complexes with proteins. We extract the local mean forces and torques acting on each base-pair of bound DNA from high-resolution complex structures. Our method relies on known elastic potentials and a careful choice of coordinates of the well-established rigid base-pair model of DNA. The results are robust with respect to parameter and conformation uncertainty. They reveal the complex nano-mechanical patterns of interaction between proteins and DNA. Being non-trivially and non-locally related to observed DNA conformations, base-pair forces and torques provide a new view on DNA-protein binding that complements structural analysis.Comment: accepted for publication in JCP; some minor changes in response to review 18 pages, 5 figure + supplement: 4 pages, 3 figure

Single-molecule stretching experiments of flexible (wormlike) chain molecules in different ensembles: Theory and a potential application of finite chain length effects to nick-counting in DNA

We propose a formalism for deriving force\u2013elongation and elongation\u2013force relations for flexible chain molecules from analytical expressions for their radial distribution function, which provides insight into the factors controlling the asymptotic behavior and finite chain length corrections. In particular, we apply this formalism to our previously developed interpolation formula for the wormlike chain end-to-end distance distribution. The resulting expression for the asymptotic limit of infinite chain length is of similar quality to the numerical evaluation of Marko and Siggia\u2019s variational theory and considerably more precise than their interpolation formula. A comparison to numerical data suggests that our analytical finite chain length corrections achieve a comparable accuracy. As an application of our results, we discuss the possibility of inferring the time-dependent number of nicks in single-molecule stretching experiments on double-stranded DNA from the accompanying changes in the effective chain length

Sequence Dependent Elasticity of DNA

The DNA contained in every living cell not only stores the genetic information; it functions in a complex molecular network that can condense, transcribe, replicate and repair genes. The essential role played by the sequence dependent structure and deformability of DNA in these basic processes of life, has received increasing attention over the past years. The present work aims at better understanding sequence dependent elasticity of double stranded DNA elasticity, across biologically relevant length scales. A theoretical description is developed that makes is possible to relate structural, biochemical and biophysical experiments and simulation. It is based on the rigid base–pair chain (rbc) model which captures all basic deformation modes on the scale of individual base–pair (bp) steps. Existing microscopic parametrizations of the rbc model rely on indirect methods. A way to relate them to biochemical experiments is provided by the indirect readout mechanism, where DNA elasticity determines protein–DNA complexation affinities. By correlating theoretical affinity predictions with in vitro measurements in a well–studied test case, different parameter sets were evaluated. As a result a new, hybrid parameter set is proposed which greatly reduces prediction errors. Indirect readout occurs mostly at particular binding subsites in a complex. A statistical marker is developed which localizes indirect readout subsites, by detecting elastically optimized sub-sequences. By a systematic coarse–graining of the rbc to the well–characterized worm–like chain (wlc) model, a quantitative connection between microscopic and kbp scale elasticity is established. The general helical rbc geometry is mapped to an effective, linear ‘on-axis’ version, yielding the full set of wlc elastic parameters for any given sequence repeat. In the random sequence case, structural variability adds conformational fluctuations which are correlated by sequence continuity. The sequence disorder correction to entropic elasticity in the rbc model is shown to coincide with the conformational correction. The results show remarkable overall agree- ment of the coarse–grained with the mesoscale wlc parameters, lending support to the model and to the microscopic parameter sets. A continuum version of the rbc is formulated as Brownian motion on the rigid motion group. Analytic expressions for angular correlation functions and moments of the end–to–end distance distribution are given. In an equivalent Lagrangian approach, conserved quantities along, and the linear response around, a general equilibrium shape are explored.Die in jeder lebenden Zelle enthaltene DNS speichert nicht nur die genetische Information; Sie funktioniert innerhalb eines komplexen molekularen Netzwerks, das in der Lage ist, Gene zu kondensieren, transkribieren, replizieren und reparieren. Die zentrale Rolle, welche der sequenzabhängigen Struktur und Deformierbarkeit von DNS in diesen grundlegenden Lebensprozessen zukommt, erregte in den letzten Jahren zunehmendes Interesse. Die vorliegende Arbeit hat ein besseres Verständnis der sequenzabhängigen elastischen Eigenschaften von DNS auf biologisch relevanten Längenskalen zum Ziel. Es wird eine theoretische Beschreibung entwickelt, die es ermöglicht, strukturbiologische, biochemische und biophysikalische Experimente und Simulationen in Beziehung zu setzen. Diese baut auf dem Modell einer Kette aus starren Basenpaaren (rbc) auf, das alle wichtigen Deformationsmoden von DNS auf der Ebene von einzelnen Basenpaar (bp)–Schritten abbildet. Bestehende Parametersätze des rbc-Modells beruhen auf indirekten Methoden. Eine direkte Beziehung zu biochemischen Experimenten kann mithilfe des indirekten Auslese-Mechanismus hergestellt werden. Hierbei bestimmt die DNS– Elastizität Komplexierungsaffinitäten von Protein–DNS–Komplexen. Durch eine Korrelation von theoretischen Vorhersagen mit in vitro Messungen in einem gut untersuchten Beispielfall werden verschiedene Parametersätze bewertet. Als Resultat wird ein neuer Hybrid–Parametersatz vorgeschlagen, der die Vorhersagefehler stark reduziert. Indirektes Auslesen tritt meistens an speziellen Teilbindungsstellen innerhalb eines Komplexes auf. Es wird eine statistische Kenngröße entwickelt, die indirektes Auslesen durch Detektion elastisch optimierter Subsequenzen erkennt. Durch ein systematisches Coarse–Graining des rbc-Modells auf das gut charakterisierte Modell der wurmartigen Kette (wlc) wird eine quantitative Beziehung zwischen der mikroskopischen und der Elastizität auf einer kbp-Skala hergestellt. Die allgemeine helikale Geometrie wird auf eine effektive, lineare Version der Kette ‘auf der Achse’ abgebildet. Dies führt zur Berechnung des vollen Satzes von wlc-elastischen Parameters für eine beliebig vorgegebene periodische Sequenz. Im Fall zufälliger Sequenz führt die Strukturvariabilität zu zusätzlichen Konformationsfluktuationen, die durch die Kontinuität der Sequenz kurzreichweitig korreliert sind. Es wird gezeigt, daß die Sequenzunordnungs-Korrektur zur entropischen Elastizität im rbc-Modell identisch ist zur Korrektur der Konformationsstatistik. Die Ergebnisse zeigen eine bemerkenswerte Übereinstimmung der hochskalierten mikroskopischen mit den mesoskopischen wlc-Parameter und bestätigen so die Wahl des Modells und seiner mikroskopischen Parametrisierung. Eine Kontinuumsversion des rbc-Modells wird formuliert als Brownsche Bewegung auf der Gruppe der Starrkörpertransformationen. Analytische Ausdrücke für Winkelkorrelationsfunktionen und Momente der Verteilung des End-zu-End–Vektors werden angegeben. In einem äquivalenten Lagrange-Formalismus werden Erhaltungsgrößen entlang von Gleichgewichtskonformationen und die lineare Antwort in ihrer Umgebung untersucht

DNA: From rigid base-pairs to semiflexible polymers

The sequence-dependent elasticity of double-helical DNA on a nm length scale can be captured by the rigid base-pair model, whose strains are the relative position and orientation of adjacent base-pairs. Corresponding elastic potentials have been obtained from all-atom MD simulation and from high-resolution structural data. On the scale of a hundred nm, DNA is successfully described by a continuous worm-like chain model with homogeneous elastic properties characterized by a set of four elastic constants, which have been directly measured in single-molecule experiments. We present here a theory that links these experiments on different scales, by systematically coarse-graining the rigid base-pair model for random sequence DNA to an effective worm-like chain description. The average helical geometry of the molecule is exactly taken into account in our approach. We find that the available microscopic parameters sets predict qualitatively similar mesoscopic parameters. The thermal bending and twisting persistence lengths computed from MD data are 42 and 48 nm, respectively. The static persistence lengths are generally much higher, in agreement with cyclization experiments. All microscopic parameter sets predict negative twist-stretch coupling. The variability and anisotropy of bending stiffness in short random chains lead to non-Gaussian bend angle distributions, but become unimportant after two helical turns.Comment: 13 pages, 6 figures, 6 table

Strengthening Deterministic Policies for POMDPs

The synthesis problem for partially observable Markov decision processes (POMDPs) is to compute a policy that satisfies a given specification. Such policies have to take the full execution history of a POMDP into account, rendering the problem undecidable in general. A common approach is to use a limited amount of memory and randomize over potential choices. Yet, this problem is still NP-hard and often computationally intractable in practice. A restricted problem is to use neither history nor randomization, yielding policies that are called stationary and deterministic. Previous approaches to compute such policies employ mixed-integer linear programming (MILP). We provide a novel MILP encoding that supports sophisticated specifications in the form of temporal logic constraints. It is able to handle an arbitrary number of such specifications. Yet, randomization and memory are often mandatory to achieve satisfactory policies. First, we extend our encoding to deliver a restricted class of randomized policies. Second, based on the results of the original MILP, we employ a preprocessing of the POMDP to encompass memory-based decisions. The advantages of our approach over state-of-the-art POMDP solvers lie (1) in the flexibility to strengthen simple deterministic policies without losing computational tractability and (2) in the ability to enforce the provable satisfaction of arbitrarily many specifications. The latter point allows taking trade-offs between performance and safety aspects of typical POMDP examples into account. We show the effectiveness of our method on a broad range of benchmarks

Stable developmental patterns of gene expression without morphogen gradients

Gene expression patterns are established by cross-regulating target genes that interpret morphogen gradients. However, as development progresses, morphogen activity is reduced, leaving the emergent pattern without stabilizing positional cues. The pattern then can be deteriorated by the intrinsically noisy biochemical processes acting at the cellular level. But remarkably, the established gene expression patterns remain spatially and temporally stable in many biological systems. Here we combine spatial-stochastic simulations with an enhanced sampling method and a recently developed stability theory to address how spatiotemporal integrity of a gene expression pattern is maintained in developing tissue lacking morphogen gradients. Using a minimal embryo model consisting of spatially coupled biochemical reactor volumes, we study a stripe pattern in which weak cross-repression between nearest neighbor domians alternates with strong repression between next-nearest neighbor domains, inspired by the gap gene system in the Drosophila embryo. We find that fine-tuning of the weak repressive interactions to an optimal level can significantly increase temporal stability of the expression patterns, allowing for stable patterns over developmentally relevant times in the absence of morphogen gradients. The numerically determined optimal parameters closely agree with the predictions of the stability theory. By analizing the dynamics of factors characterizing pattern integrity, we trace back the stability enhancement to the emergence of restoring forces, maintaining the pattern in a meta-stable basin. Altogether our results demonstrate that metastable attractors can emerge as a property of stochastic gene expression patterns even without system-wide positional cues, provided that the gene regulatory interactions shaping the pattern are optimally tuned