Current Bioinformatics - Volume 3, Issue 1, 2008

Understanding the relationship between amino acid sequences and protein folding rates is a challenging task similar to the protein folding problem. In this review, after a brief definition of protein folding rates, we describe various methods including contact order, long-range order, total contact distance etc. for understanding/predicting protein folding rates from three-dimensional structures of proteins. In addition, the applications of secondary structure content, length of secondary structures and solvent accessibility for understanding protein folding rates will be discussed. Further, the methods based on amino acid properties, composition, long-range contacts etc. for predicting protein folding rates from amino acid sequences will be discussed. The importance of amino acid properties, hydrophobic cluster formation and long-range contact network for understanding the transition state structures of proteins, which are related to protein folding rates, will be outlined.

The acquisition of biological data, ranging from molecular characterization and simulations (e.g. protein folding dynamics), to systems biology endeavors (e.g. whole organ simulations) all the way up to ecological observations (e.g. as to ascertain climate change's impact on the biota) is growing at unprecedented speed. The use of computational and networking resources is thus unavoidable. As the datasets become bigger and the acquisition technology more refined, the biologist is empowered to ask deeper and more complex questions. These, in turn, drive a runoff effect where large research consortia emerge that span beyond organizations and national boundaries. Thus the need for reliable, robust, certified, curated, accessible, secure and timely data processing and management becomes entrenched within, and crucial to, 21st century biology. Furthermore, the proliferation of biotechnologies and advances in biological sciences has produced a strong drive for new informatics solutions, both at the basic science and technological levels. The previously unknown situation of dealing with, on one hand, (potentially) exabytes of data, much of which is noisy, has large experimental errors or theoretical uncertainties associated with it, or on the other hand, large quantities of data that require automated computationally intense analysis and processing, have produced important innovations in web and grid technology. In this paper we present a trace of these technological changes in Web and Grid technology, including details of emerging infrastructures, standards, languages and tools, as they apply to bioinformatics, computational biology and systems biology. A major focus of this technological review is to collate up-to-date information regarding the design and implementation of various bioinformatics Webs, Grids, Web-based grids or Grid-based webs in terms of their infrastructure, standards, protocols, services, applications and other tools. This review, besides surveying the current state-of-the-art, will also provide a road map for future research and open questions.

The view of RNA as simple information transfer molecule has been continuously challenged since the discovery of ribozymes, a class of RNA molecules with enzyme-like function. Moreover, the recent discovery of tiny RNA molecules such as μRNAs and small interfering RNA, is transforming our thinking about how gene expression is regulated. Thus, RNA molecules are now known to carry a large repertory of biological functions within cells including information transfer, enzymatic catalysis and regulation of cellular processes. Similar to proteins, functional RNA molecules fold into their native three-dimensional (3D) conformation, which is essential for performing their biological activity. Despite advances in understanding the folding and unfolding of RNA, our knowledge of the atomic mechanism by which RNA molecules adopt their biological active structure is still limited. In this review, we outline the general principles that govern RNA structure and describe the databases and algorithms for analyzing and predicting RNA secondary and tertiary structure. Finally, we assess the impact of the current coverage of the RNA structural space on comparative modeling RNA structures.

A central focus of cancer genetics is the study of mutations that are causally implicated in tumorigenesis. Although missense variants are commonly identified in genomic sequence, only a small fraction directly contributes to oncogenesis. The ability to distinguish those somatic missense changes that contribute to cancer progression from those that do not is a difficult problem usually accomplished through functional in vivo analyses. With the advent of several largescale cancer genome projects geared toward identifying mutations that are causally implicated in cancer, it is becoming increasingly important to develop methods for distinguishing functionally relevant mutations from those passenger mutations and other innocuous polymorphisms. Here we review two general strategies that are based on either mutation frequency data or the nature of amino acid substitutions. Frequency-based methods are commonly used for estimating the enrichment of causal mutations and for identifying specific mutations under positive selection pressure. The statistical power of these methods is dependent on the number of cancer samples being surveyed. The potential functional consequences of missense mutations can also be examined by bioinformatics approaches since multiple computational methods have been developed to estimate the deleterious effect of amino acid substitutions. It is likely that many of the existing methods can potentially be applied to large-scale cancer genome data to detect relevant causal mutations regardless of their prevalence. Future data analysis of missense somatic mutations will likely benefit from continual development of integrated and automated methods for combining all available information to predict whether a particular mutation is causally implicated.

Expert systems, or decision support systems, are artificial intelligence systems that have been trained with real cases to perform complicated tasks. They are used in a variety of areas and are among the most popular application fields in artificial intelligence. Expert systems have applications in different areas of medicine. Here we present a short history of medical expert systems and the characteristics of these systems. Medical expert systems were initially developed for academic areas and later for clinical applications also. Health care systems produce tremendous amounts of information (patient, demographic, clinical and billing data), which are susceptible to analysis by intelligent software and need new techniques to extract new knowledge. A variety of medical expert systems tools are available and can function as intelligent assistants to clinicians, helping in diagnostic processes, laboratory analysis, treatment protocol, and teaching of medical students and residents. A critical review of the strengths and limitations, as well as the latest trends in decision support systems, is discussed. In addition, a model for computer-based medical diagnoses of primary immunodeficiencies is presented.