Abstract

In this paper, we have computationally described the interaction of yDNA bases with small gold clusters through a variety of aspects including geometrical, spectroscopic and energetic one. To get insights in to essential elements of this interaction, the contribution of typical energy interaction components have also been investigated.

Abstract

Motivation: Various types of functional RNAs-tRNA, Ribosomes, Introns, Aptamers, Ribozymes Diverse and Dynamic molecules Recent availability of RNA crystal structures has facilitated study and analysis

Abstract

Predicting the 3D native conformation of a protein given the amino acid sequence is known as protein structure prediction (PSP)problem and is one of the greatest challenge of computational biology. The current work uses a real valued Genetic Algorithm (GA), a powerful variant of conventional GA to simulate the PSP problem. It consists of three evolutionary operators to manipulate the genes and a fitness function based on a simple force field. The individuals of the population correspond to different conformations of the same polypeptide chain, represented by a string of (phi,psi)torsion angles. The conformations are generated under the constraints of Ramachandran plot along with secondary structure information, which are then screened through a set of knowledge based biophysical filters, viz. persistence length and radius of gyration. Only the structures satisfying the filtering criteria are considered for energy minimization using GA. The algorithm has been validated on a set of known globular protein containing 2-4 secondary structure elements. For all the test proteins the algorithm converges rapidly ad the converged structure shows a backbone RMSD (root mean square deviation) or 3-6A as compared to the native structure.

Abstract

Identification of base pairs which can participate, through multimodality of edge interactions, in conformational switching events, and evaluation of possible environmental factors responsible for the switching can lead to greater understanding of RNA functional dynamics. Moleculardynamics studies by Schneider et al involving multimodality in C:U W:W cis interactions in aqueous medium, arguments forwarded by Van Dongen et al in support of a pH dependent G:C W:W cis to G:C H:W cis conformational switch and the postulation of putative role of a minor motifs in ribosomal dynamics through structure analysis, are examples of efforts in this section. Here we discuss the computational analysis of a putative pH dependant conformational switching of G:G base pairs in RNA structures.

Abstract

Hoogsteen-sugar base pattern of RNA base pairing is studied using density functional theory. Hydrogen bonding patterns of these base apirs are characterized using NBO analysis and AIM analysis. Correlation between strenghth of base pairing and nature of donor-acceptor combinations is also carried out.

Abstract

In this paper, we present RBNBC, a Repeat Based Naive Bayes Classifier of bio-sequences that uses maximal fre-quent subsequences as features. RBNBC’s design is based on generic ideas that can apply to other domains where the data is organized as collections of sequences. Specifically,RBNBC uses a novel formulation of Naive Bayes that incor-porates repeated occurrences of subsequences within each sequence. Our extensive experiments on two collections of protein families show that it performs as well as existing state-of-the-art probabilistic classifiers for bio-sequences.This is surprising as it is a pure data mining based generic classifier that does not require domain-specific background knowledge. We note that domain-specific ideas could fur-ther increase its performance.

Abstract

An important problem in biological data analysis is to pre-dict the family of a newly discovered sequence like a pro-tein or DNA sequence, using the collection of available se-quences. In this paper we tackle this problem and presentREBMEC, a Repeat Based Maximum Entropy Classifier of biological sequences. Maximum entropy models are known to be theoretically robust and yield high accuracy,but are slow. This makes them useful as benchmarks to evaluate other classifiers. Specifically, REBMEC is based on the classical Generalized Iterative Scaling (GIS) al-gorithm and incorporates repeated occurrences of subse-quences within each sequence. REBMEC uses maximal frequent subsequences as features but can support other types of features as well. Our extensive experiments on two collections of protein families show that REBMEC performs as well as existing state-of-the-art probabilistic classifiers for biological sequences without using domain-specific background knowledge such as multiple align-ment, data transformation and complex feature extraction methods. The design of REBMEC is based on genericideas that can apply to other domains where data is orga-nized as collections of sequences.

Abstract

The problem of search and retrieval of images using relevance feedback has attracted tremendous attention in recent years from the research community. A real-world-deployable interactive image retrieval system must (1) be accurate, (2) require minimal user-interaction, (3) be efficient, (4) be scalable to large collections (millions) of images, and (5) support multi-user sessions. For good accuracy, we need effective methods for learning the relevance of image features based on user feedback, both within a user-session and across sessions. Efficiency and scalability require a good index structure for retrieving results. The index structure must allow for the relevance of image features to continually change with fresh queries and user-feedback. The state-of-the-art methods available today each address only a subset of these issues. In this paper, we build a complete system FISH - Fast Image Search in Huge databases. In FISH, we integrate selected techniques available in the literature, while adding a few of our own. We perform extensive experiments on real datasets to demonstrate the accuracy, efficiency and scalability of FISH. Our results show that the system can easily scale to millions of images while maintaining interactive response time.

Abstract

In this paper we consider the problem of extracting the spe-cial properties of any given record in a dataset. We are interested in determining what makes a given record unique or different from the ma-jority of the records in a dataset. In the real world, records typically represent objects or people and it is often worthwhile to know what spe-cial properties are present in each object or person, so that we can make the best use of them. This problem has not been considered earlier inthe research literature. We approach this problem using ideas from clus-tering, attribute oriented induction (AOI) and frequent itemset mining.Most of the time consuming work is done in a preprocessing stage and the online computation of the uniqueness of a given record is instantaneous

Abstract

Recently much attention is being focused on the analysis of the three dimensional structure of proteins using graph and network concepts. The simplest protein network graph is constructed by defining the amino acids as nodes and the edges drawn between amino acids (within a threshold) which attempt to capture not only covalent but also non-covalent interactions. Analysis of the topological details of proteins with known structures, such as clustering of specific types of amino acids important for structure, folding and function, is of great value and is an active field of research. Since structures of a large number of proteins is now available, automatic methods of analysis are required to analyze them and recently, the tools from graph theory are being explored for such analysis. Here, we propose to use graph properties and their spectral analysis for identifying patterns in protein structures, in particular, structural repeats or motifs. These repeats are not easily detectable at the sequence level because of low conservation or high divergence between independent repeated units. The importance of such repeats in understanding biological function resides not only in their high frequency among known sequences, but also in their abilities to confer multiple binding and structural roles on proteins, e.g., zinc finger domain, a constituent of transcription factors involved in DNA binding, where the composition and copy number of individual tandem repeats confers selectivity and activity of DNA binding. This functional versatility is apparent not only among different repeat types, but also for similar repeats from the same family. Our understanding of repeats, with respect to their structures, functions, and evolution, therefore represents a considerable challenge. Our analysis of the degree distribution and spectral analysis of a set of proteins from various repeat families show that the repeat regions exhibit similar connectivity patterns in the graph. The network property, Betweenness, was found to identify accurately the repeat boundaries in protein families. Thus, this approach can help in identifying repeated motifs in proteins which are difficult to identify at the sequence level.