Abstract

Purine riboswitches constitute a class of highly structured regions within the 5'-untranaslated sequence stretches of mRNAs that specifically bind purines to regulate the gene expression. A unique feature of the ligand binding in these riboswitches is the larger role of hydrogen bonding compared to stacking [1]. In order to study the inherent strength and specificity of molecular recognition involved in these interactions, we have carried out gas phase quantum chemical studies on the metabolite binding pockets of adenine [2] and guanine [2] binding riboswitches, with adenine, guanine and a modified pyrimidine respectively, at the B3LYP/6-31G level of theory. An analysis of optimized structures reveals that for three conserved residues - C74, U47 and U51, the ligand-base hydrogen bonding pattern remains consereved. On the other hand, the U22 residue deviates from its crystal geometary, suggesting that its is held in the crystal geometry through strong backbone and local environmental constraints. We have carried out Morokuma decomposition and interaction energy evaluations as well as NBO and AIM analysis to understand the strength and nature of binding of the individual aptamer residues with their respective purine metabolites. The dominant features of these non-covalent interactions have also been analyazed. Acknowledgements: AM thanks DBT for research grant. PS and SS thank CSIR, New Delhi for JRFs. [1] Gilbert, S. D.; Stoddard, C. D.; Wise, S. J.; Batey, R. T. J. Mol. Biol. 2006, 359, 754-768 [2] Serganov, A.; Yuan, Y.; Pikovskaya, O.; Polonskaia, A.; Malinina, L.; Phan, A. T.; Hobartner, C.; Micura, R.; Breakar, R. R.; Patel, D. J. Chem. Biol. 2004, 11, 1729-1741.

Abstract

The need for computational characterization of DNA structure in the context of DNA bending/binding, B ->A transitions in protein-DNA complexes and also in the context of strand orientation especially in DNA triplexes and quadruplexes have given rise to several fine grained parameters, such as backbone conformations, sugar pucker and chi angle, local strand orientation, base pair and base pair step parameters backbone base inclination etc. With their unpaired bases, extensive non helical regions, non-canonical base pairs and tertiary interactions, RNA molecules host a large variety of recurrent structural and functional motifs and parameters such as Zp, defined in the context of double helical DNA base pairs and base pair steps and effectively used for A and B DNA differentiations, can not be used for computationally driven automated mining of complex RNA motifs. Though several algorithms have been proposed based on backbone conformational parameters and though algorithms for computational detection of base pairs and analysis of base pair parameters, are available, there are no reports of motif mining studies involving parameters which, as with Zp, simultaneously involve backbone conformation as well as base pairing geometry. In this work, we propose a novel set of parameters, Omega Pseudo-Torsions and Omega Distances, which capture backbone geometries at base(i) - base(j) interaction points and which can be used to computationally characterize structural features both in RNA and DNA. Pseudo torsion angles: Omega eta: P(i)-C4'(j)-P(j); Omega theta: P(i+1)-C4'(j)-P(j+1) Omega 1: P(i)-C4'(i)-C4'(j)-P(j+1); Omega 2: P(i+1)-C4'(i)-C4'(j)-P(j) Pseudo bond distance: Omega distance: C4'(i)-C4'(j) Where 'i' and 'j' are bases interacting through hydrogen bonds. We report the results of our benchmarking studies of Omega parameters with standard DNA structure classification parameters and of our preliminary studies on their effectiveness in classifying local geometries of RNA strands in different base pairing (canonical and non-canonical) contexts.

Abstract

DNA structures and their variations are important in several contexts such as chromatin remodeling and transcriptional initiation and regulation etc. Theneed for computational characterization of DNA structures associated with DNA bending/binding, B ? A transitions in protein-DNA complexes, molecular dynamics trajectory analysis and also in the context of strand orientation, especially in DNA triplexes and quadruplexes, have given rise to several fine grained parameters, such as backbone conformations, sugar pucker and ? angle, local strand orientation, base pair and base pair step parameters, backbone base inclination etc. With their unpaired bases, extensive non helical regions, non-canonical base pairs and tertiary interactions, RNA molecules host a large variety of recurrent structural and functional motifs and parameters such as zp, defined in the context of double helical DNA base pairs and base pair steps and effectively used for A and B DNA differentiations, can not be used for computationally driven automated mining of complex RNA motifs. Thus for RNA structures, though several motif mining algorithms based on backbone conformational parameters and involving computational detection and analysis of base pairs and their parameters are available, there are no reports of studies involving parameters which simultaneously involve backbone conformation as well as base pairing geometry. In this work, we propose a novel set of parameters, Omega Torsions and Omega Distances, which captures backbone geometries at base(i) ? base(j) interaction points and which can be used to computationally characterize structural features both in RNA and DNA. Pseudo torsion angles: Omega ?: P(i)-C4'(i)-C4'(j)-P(j); Omega ?: P(i+1)-C4'(i)-C4'(j)-P(j+1) Omega 1: P(i)-C4'(i)-C4'(j)-P(j+1); Omega 2: P(i+1)-C4'(i)-C4'(j)-P(j) Pseudo bond distance: Omega distance: C4'(i)-C4'(j) Where 'i' and 'j' are bases interacting through hydrogen bonds. We report the results of our benchmarking studies of Omega parameters, as applied to protein DNA complexes including e.g. TATA box-TBP complexes and nucleosomal DNA, with standard DNA structure classification parameters, such as zp, and of our preliminary studies on their effectiveness in classifying local geometries of RNA strands in different base pairing contexts. We conclude that omega parameters can potentially lead to the development of a single set of parameters which can be used to characterize both DNA as well as RNA structures.

Abstract

Projection of a Markov process with constant rates of transition to a small number of observable aggregated states can result in complex kinetics with memory. Here, we define the entropy production along a single sequence of aggregated states and show that it obeys detailed and integral fluctuation theorems. More importantly, we prove that projection shifts the distribution of entropy production over the ensemble of paths for a nonequilibrium process toward one characteristic of a system at equilibrium. This statement represents an analog of the second law of thermodynamics for path-dependent entropies and thus a new form of constraint of irreversible systems. Numeric examples are presented to illustrate these ideas.

Abstract

Capillary Non-Perfusion (CNP) is a condition in diabetic retinopathy where blood ceases to flow to certain parts of the retina, potentially leading to blindness. This paper presents a solution for automatically detecting and segmenting CNP regions from fundus fluorescein angiograms (FFAs). CNPs are modelled as valleys, and a novel multiresolution technique for trough-based valley detection is presented. The proposed algorithm has been tested on 40 images and validated against expert-marked ground truth. Obtained results are presented as a receiver operating characteristic (ROC) curve. The area under this curve is 0.842 and the distance of ROC from the ideal point (0,1) is 0.31.

Abstract

In this paper we present a method for fovea localization which does not use organization information of other retinal structures like optic disk and arcades. The main advantage of this method is that it does not require segmentation/localization of other retinal structures which are required as prior knowledge in existing fovea localization methods. The key idea is to enhance the relative contrast between the fovea and its surrounding such that it is well-separated in a retinal image. The approach was tested on 520 retinal images which include normal and pathological color retinal images. An overall accuracy of 90.57% is reported independent of left eye, right eye, normal or pathological retinal images.

Abstract

We present a new method for optic disk (OD) detection in a retinal image. It is an hybrid approach which uses properties of both appearance and model-based approaches which are mostly used for OD detection. An extrema pyramidal decomposition is employed and hill type topographical features are extracted at the lowest level of the image pyramid. The detected hill points are characterised as candidate OD locations. Later, a confidence measure is derived for each candidate using vessel structure information and candidate with the highest value is declared as final OD location. This method has been tested on different retinal image dataset and quantitative results are presented

Abstract

Extraction of anatomical structures (landmarks), such as optic disk (OD), fovea and blood vessels, from fundus images is useful in automatic diagnosis. Current approaches largely use spatial relationship among the landmarks’ position for detection. In this paper, we present an appearance-based method for detecting fovea and OD from colour images. The strategy used for detection is based on improving the local contrast which is achieved by combining information from two spectral channels of the given image. The proposed method has been successfully tested on different datasets and the results show 96% detection for fovea and 91% detection for OD (a total of 502 and 531 images for fovea and OD are taken respectively).

Abstract

Retinal images are widely used to manually or automatically detect and diagnose many diseases. Due to the complex imaging setup, there is a large luminosity and contrast variability within and across images. Here, we use the knowledge of the imaging geometry and propose an enhancement method for colour retinal images, with a focus on contrast improvement with no introduction of artifacts. The method uses non-uniform sampling to estimate the degradation and derive a correction factor from a single plane. We also propose a scheme for applying the derived correction factor to enhance all the colour planes of a given image. The proposed enhancement method has been tested on a publicly available dataset [8]. Results show marked improvement over existing methods.

Abstract

Current denoising techniques use the classical orthonormal wavelets for decomposition of an image corrupted with additive white Gaussian noise, upon which various thresholding strategies are built. The use of available biorthogonal wavelets in image denoising is less common because of their poor performance. In this paper, we present a method to design image-matched biorthogonal wavelet bases and report on their potential for denoising. We have conducted experiments on various image datasets namely Natural, Satellite and Medical with the designed wavelets using two existing thresholding strategies. Test results from comparing the performance of matched and fixed biorthogonal wavelets show an average improvement of 35% in MSE for low SNR values (0 to 18db) in every dataset. This improvement was also seen in the PSNR and visual comparisons. This points to the importance of matching when using wavelet-based denoising