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1.  Molecular evolution of cyclin proteins in animals and fungi 
Background
The passage through the cell cycle is controlled by complexes of cyclins, the regulatory units, with cyclin-dependent kinases, the catalytic units. It is also known that cyclins form several families, which differ considerably in primary structure from one eukaryotic organism to another. Despite these lines of evidence, the relationship between the evolution of cyclins and their function is an open issue. Here we present the results of our study on the molecular evolution of A-, B-, D-, E-type cyclin proteins in animals and fungi.
Results
We constructed phylogenetic trees for these proteins, their ancestral sequences and analyzed patterns of amino acid replacements. The analysis of infrequently fixed atypical amino acid replacements in cyclins evidenced that accelerated evolution proceeded predominantly during paralog duplication or after it in animals and fungi and that it was related to aromorphic changes in animals. It was shown also that evolutionary flexibility of cyclin function may be provided by consequential reorganization of regions on protein surface remote from CDK binding sites in animal and fungal cyclins and by functional differentiation of paralogous cyclins formed in animal evolution.
Conclusions
The results suggested that changes in the number and/or nature of cyclin-binding proteins may underlie the evolutionary role of the alterations in the molecular structure of cyclins and their involvement in diverse molecular-genetic events.
doi:10.1186/1471-2148-11-224
PMCID: PMC3162929  PMID: 21798004
2.  Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions 
BMC Bioinformatics  2007;8:481.
Background
Reliable transcription factor binding site (TFBS) prediction methods are essential for computer annotation of large amount of genome sequence data. However, current methods to predict TFBSs are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered.
Results
To improve this situation, we have quantified the performance of several Position Weight Matrix (PWM) algorithms, using exhaustive approaches to find their optimal length and position. We applied these approaches to bio-medically important TFBSs involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses (NF-κB, ISGF3, IRF1, STAT1), obesity and lipid metabolism (PPAR, SREBP, HNF4), regulation of the steroidogenic (SF-1) and cell cycle (E2F) genes expression. We have also gained extra specificity using a method, entitled SiteGA, which takes into account structural interactions within TFBS core and flanking regions, using a genetic algorithm (GA) with a discriminant function of locally positioned dinucleotide (LPD) frequencies.
To ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized PWM and SiteGA have shown similar recognition performances. Then we applied SiteGA and optimized PWMs (both separately and together) to sequences in the Eukaryotic Promoter Database (EPD). The resulting SiteGA recognition models can now be used to search sequences for BSs using the web tool, SiteGA.
Analysis of dependencies between close and distant LPDs revealed by SiteGA models has shown that the most significant correlations are between close LPDs, and are generally located in the core (footprint) region. A greater number of less significant correlations are mainly between distant LPDs, which spanned both core and flanking regions. When SiteGA and optimized PWM models were applied together, this substantially reduced false positives at least at higher stringencies.
Conclusion
Based on this analysis, SiteGA adds substantial specificity even to optimized PWMs and may be considered for large-scale genome analysis. It adds to the range of techniques available for TFBS prediction, and EPD analysis has led to a list of genes which appear to be regulated by the above TFs.
doi:10.1186/1471-2105-8-481
PMCID: PMC2265442  PMID: 18093302

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