Structural alphabets derived from attractors in conformational space

Background: The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis.Results: A Structural Alphabet has been derived by clustering all four-residue fragments of a high-resolution subset of the protein data bank and extracting the high-density states as representative conformational states. Each fragment is uniquely defined by a set of three independent angles corresponding to its degrees of freedom, capturing in simple and intuitive terms the properties of the conformational space. The fragments of the Structural Alphabet are equivalent to the conformational attractors and therefore yield a most informative encoding of proteins. Proteins can be reconstructed within the experimental uncertainty in structure determination and ensembles of structures can be encoded with accuracy and robustness.Conclusions: The density-based Structural Alphabet provides a novel tool to describe local conformations and it is specifically suitable for application in studies of protein dynamics. © 2010 Pandini et al; licensee BioMed Central Ltd

Workshop—Predicting the Structure of Biological Molecules

This April, in Cambridge (UK), principal investigators from the Mathematical Biology Group of the Medical Research Council's National Institute of Medical Research organized a workshop in structural bioinformatics at the Centre for Mathematical Sciences. Bioinformatics researchers of several nationalities from labs around the country presented and discussed their computational work in biomolecular structure prediction and analysis, and in protein evolution. The meeting was intensive and lively and gave attendees an overview of the healthy state of protein bioinformatics in the UK

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Expanding the applications of high-throughput DNA sequencing

textDNA sequencing is the process of determining the identities of the nucleotides that make up a molecule of DNA. The rapid pace of advancements in sequencing technologies in recent years have made it possible to simultaneously determine the sequences of hundreds of millions of short DNA fragments. The ability to perform sequencing with such high throughput has revolutionized the study of biological systems, but the types of questions that can be answered through sequencing-based experiments can be limited by the presence of different kinds of noise and biases in these experiments. One class of applications of high-throughput sequencing involves identifying genetic variation, such as finding rare mutations in the genomes of cancerous cells. In these applications, the sensitivity with which rare genetic variants can be detected is limited by the relatively high rate with which current DNA sequencing technologies incorrectly identify nucleotides. In the first half of this thesis, we present a method for dramatically reducing the rate at which these incorrect identifications occur. Our method, called circle sequencing, creates redundant copies of the sequence of each input molecule of DNA. This is accomplished by circularizing each DNA fragment and performing rolling circle amplification on these circles with a strand-displacing polymerase. The resulting products consist of several physically linked copies of the original sequence in each fragment. When these products are sequenced, this informational redundancy protects against random errors introduced during sequencing, allowing for highly accurate recovery of the original sequence of each input molecule. By eliminating the vast majority of incorrectly identified nucleotides from the resulting data, our method enables the sensitive detection of rare variants and opens up exciting new questions involving such variants to direct measurement by sequencing. An entirely different application of high-throughput sequencing is to selectively capture and sequence stretches of DNA or RNA that are participating in a process of interest within a cell. The accuracy of quantitative inferences made by this type of experiment can be severely impacted, however, by biases introduced during the experimental manipulations used to isolate biologically relevant fragments of DNA from cells. Ribosome profiling is an experimental technique that consists of sequencing short stretches of messenger RNAs that are protected from nuclease digestion by the presence of a bound ribosome. The resulting data represents millions of snapshots of the locations of actively translating ribosomes. In theory, these snapshots can be used to determine how long ribosomes take to translate each type of codon by quantifying how often ribosomes are observed positioned over that codon. In practice, different studies in yeast attempting to do this have reached contradictory and counterintuitive conclusions. In the second half of this thesis, we perform a large-scale comparative analysis of data from many different ribosome profiling experiments in order to resolve these contradictions. We identify a previously unappreciated source of systematic bias in a subset of these experiments. This bias prevents these experiments from accurately measuring ribosomes in proportion to how long they spend at each position in vivo. Understanding this bias provides insight into the true signatures of translation dynamics in yeast and offers important guidance for the future design and interpretation of sequencing-based approaches to measuring these dynamics.Computational Science, Engineering, and Mathematic