Persistence of neutral polymorphisms in Lake Victoria cichlid fish

Phylogenetic trees for groups of closely related species often have different topologies, depending on the genes used. One explanation for the discordant topologies is the persistence of polymorphisms through the speciation phase, followed by differential fixation of alleles in the resulting species. The existence of transspecies polymorphisms has been documented for alleles maintained by balancing selection but not for neutral alleles. In the present study, transspecific persistence of neutral polymorphisms was tested in the endemic haplochromine species flock of Lake Victoria cichlid fish. Putative noncoding region polymorphisms were identified at four randomly selected nuclear loci and tested on a collection of 12 Lake Victoria species and their putative riverine ancestors. At all loci, the same polymorphism was found to be present in nearly all the tested species, both lacustrine and riverine. Different polymorphisms at these loci were found in cichlids of other East African lakes (Malawi and Tanganyika). The Lake Victoria polymorphisms must have therefore arisen after the flocks now inhabiting the three great lakes diverged from one another, but before the riverine ancestors of the Lake Victoria flock colonized the Lake. Calculations based on the mtDNA clock suggest that the polymorphisms have persisted for about 1.4 million years. To maintain neutral polymorphisms for such a long time, the population size must have remained large throughout the entire period.

Prediction of protein folding class using global description of amino acid sequence.

We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes. Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure. Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.