Driven Polymer Translocation Through a Narrow Pore

Motivated by experiments in which a polynucleotide is driven through a proteinaceous pore by an electric field, we study the diffusive motion of a polymer threaded through a narrow channel with which it may have strong interactions. We show that there is a range of polymer lengths in which the system is approximately translationally invariant, and we develop a coarse-grained description of this regime. From this description, general features of the distribution of times for the polymer to pass through the pore may be deduced. We also introduce a more microscopic model. This model provides a physically reasonable scenario in which, as in experiments, the polymer's speed depends sensitively on its chemical composition, and even on its orientation in the channel. Finally, we point out that the experimental distribution of times for the polymer to pass through the pore is much broader than expected from simple estimates, and speculate on why this might be.Comment: 16 pages, 8 figures, RevTex and harvard citation style, submitted to Biophysical Journa

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

The Impact of Base Stacking on the Conformations and Electrostatics of Single-Stranded DNA

Single-stranded DNA (ssDNA) is notable for its interactions with ssDNA binding proteins (SSBs) during fundamentally important biological processes including DNA repair and replication. Previous work has begun to characterize the conformational and electrostatic properties of ssDNA in association with SSBs. However, the conformational distributions of free ssDNA have been difficult to determine. To capture the vast array of ssDNA conformations in solution, we pair small angle X-ray scattering with novel ensemble fitting methods, obtaining key parameters such as the size, shape and stacking character of strands with different sequences. Complementary ion counting measurements using inductively coupled plasma atomic emission spectroscopy are employed to determine the composition of the ion atmosphere at physiological ionic strength. Applying this combined approach to poly dA and poly dT, we find that the global properties of these sequences are very similar, despite having vastly different propensities for single-stranded helical stacking. These results suggest that a relatively simple mechanism for the binding of ssDNA to non-specific SSBs may be at play, which explains the disparity in binding affinities observed for these systems

Computational studies of origins of life scenarios

Understanding the origins of life on Earth is one of the most intriguing problems facing science today. In the research presented here, we apply computational methods to explore origins of life scenarios. In particular, we focus on the origins of the genetic code and the intersection between geochemistry and a primordial ``biochemistry" in which mononucleotides could form short oligoucleotide chains. We also apply quantum chemical methods to a modern biochemical reaction, the charging of tRNA by an aminoacyl-tRNA synthetase, in order to shed light on the possible chemistry one may want to consider in problems relating to the origins of life. The question of how codons came to be associated with specific amino acids in the present form of the genetic code is one fundamental part of gaining insight into the origins of life. Carl Woese and coworkers designed a series of experiments to test associations between amino acids and nucleobases that may have played a role in establishing the genetic code. Through these experiments it was found that a property of amino acids called the polar requirement (PR) is correlated to the organization of the codon table. No other property of amino acids has been found that correlates with the codon table as well as PR, indicating that PR is uniquely related to the modern genetic code. Using molecular dynamics simulations of amino acids in solutions of water and dimethylpyridine used to experimentally measure PR, we show that variations in the partitioning between the two phases as described by radial distribution functions correlate well with the measured PRs. Partition coefficients based on probability densities of the amino acids in each phase have the linear behavior with base concentration as suggested by the PR experiments. We also investigate the possible roles of inorganic mineral surfaces in catalysis and stabilization of reactions essential for early forms of replicating systems that could have evolved into biochemical processes we know today. We study a proposed origins of life scenario involving the clay montmorillonite, as well as a generalized form of a charged surface, and their catalytic role in forming oligonucleotides from activated mononucleotides. Clay and mineral surfaces are important for concentrating the reactants and for promoting nucleotide polymerization reactions. Using classical molecular dynamics methods we provide atomic details of reactant conformations prior to polynucleotide formation, lending insight into previously reported experimental observations of this phenomenon. The simulations clarify the catalytic role of metal ions, demonstrate that reactions leading to correct linkages take place primarily in the interlayer, and explain the observed sequence selectivity in the elongation of the chain. The study comparing reaction probabilities involving L- and D- chiral forms of the reactants has found enhancement of homochiral over heterochiral products when catalyzed by montmorillonite. Finally, we shift our perspective on the problem of the origins of life, by considering a modern biological reaction which is essential to all forms of life today: the charging of tRNA with correct amino acids according to their anticodons. These reactions are performed by amino-acyl tRNA synthetases (AARSs), and are essential for enforcing the genetic code. While studies involving the PR and code optimality apply to a more error-prone epoch of early biology, possibly forming ``statistical proteins" whose sequence is determined probabilistically by a loose mechanism of assignment of amino acids based on (possibly) PR, the mechanisms that charge tRNA today are highly refined to charge only the correct amino acid to a tRNA, and are thus essential for the high-fidelity translation mechanism present in all living cells. To gain some insight into how the charging reaction may have come about, we apply quantum chemical methods to a problem of modern biology to gain a further understanding of the mechanisms behind biochemical reactions

Prediction of Atomization Energy Using Graph Kernel and Active Learning

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effect of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 +- 0.01 kcal/mol using as few as 2000 training samples on the QM7 data set