Searching for transcription factor binding site clusters: how true are true positives?

The computational detection of functional transcription factor binding sites in genomic sequence is one of the challenges of the post-genomic era. Several groups have approached this problem from different directions and have demonstrated considerable success. The purpose of this communication, however, is to point out an imperfection in the way computational results are commonly reported that may lead to a distorted picture of the performance of existing algorithms.

Flexgrepps--flexible greedy peptide pool search: computation of near-optimal sets of degenerate polypeptides for antigenic screening.

Although synthesizing and utilizing individual peptides and DNA primers has become relatively inexpensive, massively parallel probing and next-generation sequencing approaches have dramatically increased the number of molecules that can be subjected to screening; this, in turn, requires vast numbers of peptides and therefore results in significant expenses. To alleviate this issue, pools of related molecules are often used to downselect prior to testing individual sequences. A computational selection process to create pools of related sequences at large scale has not been reported for peptides. In the case of PCR primers, there have been successful attempts to address this problem by designing degenerate primers that can be produced at the same cost as conventional, unique primers and then be used to amplify several different genomic regions. We present an algorithm, "FlexGrePPS" (Flexible Greedy Peptide Pool Search), that can create a near-optimal set of peptide pools. This approach is also applicable to nucleotide sequences and outperforms most DNA primer selection programs. For the proteomic compression with FlexGrePPS, the main body of our work presented here, we demonstrate the feasibility of the computation of an exhaustive cover of pathogenic proteomes with degenerate peptides that lend themselves to antigenic screening. Furthermore, we present preliminary data that demonstrate the experimental utility of highly degenerate peptides for antigenic screening. FlexGrePPS provides a near-optimal solution for proteomic compression and there are no programs available for comparison. We also demonstrate computational performance of our GreedyPrime implementation, which is a modified version of FlexGrePPS applicable to the design of degenerate primers and is comparable to existing programs for the design of degenerate primers. Specifically, we focus on the comparisons with PAMPS and DPS-DIP, software tools that have recently been shown to be superior to other methods. FlexGrePPS forms the foundation of a novel antigenic screening methodology that is based on the representation of an entire proteome by near-optimal degenerate peptide pools. Our preliminary wet lab data indicate that the approach will likely prove successful in comprehensive wet lab studies, and hence will dramatically reduce the expenses for antigenic screening and make whole proteome screening feasible. Although FlexGrePPS was designed for computational performance in order to handle vast data sets, there is the very surprising finding that even for small data sets the primer design version of FlexGrePPS, GreedyPrime, offers similar or even superior results for MP-DPD and most MDPD instances when compared to existing methods; despite the much longer run times, other approaches did not fare significantly better in reducing the original data sets to degenerate primers. The FlexGrePPS and GreedyPrime programs are available at no charge under the GNU LGPL license at http://sourceforge.net/projects/flexgrepps/.

Exploring the protein landscape in ramachandran space: it's not just psi-phi.

Most methods for the structural comparison of proteins utilize molecular coordinates in the three-dimensional physical space. Recently, a group has presented an elegant novel approach based on the characterization of protein shape in terms of backbone torsion angles. They have demonstrated considerable success in direct comparisons with other techniques, and their method lends itself to rapid screening of structural information from rapidly growing databases. We think that the torsion angle approach can be further strengthened by refining the distance notion that forms the basis of the computational scheme. In particular, we are suggesting to compute the distance along the path that minimizes the transition cost between aligned pairs of angles and therefore likely provides a more meaningful representation of distances between points in Ramachandran space.

In silico identification of metazoan transcriptional regulatory regions.

Transcriptional regulation remains one of the most intriguing and challenging subjects in biomedical research. The catalysis of transcription is a clear example of multiple proteins interacting to orchestrate a biological process, offering a starting point for the study of biological systems. Transcriptional regulation is viewed as one of the principal mechanisms governing the spatial and temporal distribution of gene expression, thus the field of transcriptional regulation provides a natural stage for quantitative studies of multiple gene systems. Building on the body of focused experimental studies and new genomics-driven data, computational biologists are making significant strides in accelerating our understanding of the transcriptional regulatory process in metazoan cells. Recent advances in the computational analysis of the interplay between factors have been fueled by well-defined computational methods for the modeling of the binding of individual transcription factors. We present here an overview of advances in the analysis of regulatory systems and the fundamental methods that underlie the recent developments.

IL-28, IL-29 and their class II cytokine receptor IL-28R.

Cytokines play a critical role in modulating the innate and adaptive immune systems. Here, we have identified from the human genomic sequence a family of three cytokines, designated interleukin 28A (IL-28A), IL-28B and IL-29, that are distantly related to type I interferons (IFNs) and the IL-10 family. We found that like type I IFNs, IL-28 and IL-29 were induced by viral infection and showed antiviral activity. However, IL-28 and IL-29 interacted with a heterodimeric class II cytokine receptor that consisted of IL-10 receptor beta (IL-10Rbeta) and an orphan class II receptor chain, designated IL-28Ralpha. This newly described cytokine family may serve as an alternative to type I IFNs in providing immunity to viral infection.