A tool for the prediction of functionally important sites in proteins using a library of functional templates

Proteome-wide evidence for enhanced positive Darwinian selection within intrinsically disordered regions in proteins

BACKGROUND

Understanding the adaptive changes that alter the function of proteins during evolution is an important question for biology and medicine. The increasing number of completely sequenced genomes from closely related organisms, as well as individuals within species, facilitates systematic detection of recent selection events by means of comparative genomics.

RESULTS

We have used genome-wide strain-specific single nucleotide polymorphism data from 64 strains of budding yeast (Saccharomyces cerevisiae or Saccharomyces paradoxus) to determine whether adaptive positive selection is correlated with protein regions showing propensity for different classes of structure conformation. Data from phylogenetic and population genetic analysis of 3,746 gene alignments consistently shows a significantly higher degree of positive Darwinian selection in intrinsically disordered regions of proteins compared to regions of alpha helix, beta sheet or tertiary structure. Evidence of positive selection is significantly enriched in classes of proteins whose functions and molecular mechanisms can be coupled to adaptive processes and these classes tend to have a higher average content of intrinsically unstructured protein regions.

CONCLUSIONS

We suggest that intrinsically disordered protein regions may be important for the production and maintenance of genetic variation with adaptive potential and that they may thus be of central significance for the evolvability of the organism or cell in which they occur.

Prediction of functionally important sites from protein sequences using sparse kernel least squares classifiers

Identification of functionally important sites (FIS) in proteins is a critical problem and can have profound importance where protein structural information is limited. Machine learning techniques have been very useful in successful classification of many important biological problems. In this paper, we adopt the sparse kernel least squares classifiers (SKLSC) approach for classification and/or prediction of FIS using protein sequence derived features. The SKLSC algorithm was applied to 5435 FIS that have been extracted from 312 reliable alignments for a wide range of protein families. We obtained 68.28% sensitivity and 68.66% specificity for training dataset and 65.34% sensitivity and 66.88% specificity for testing dataset. Further, large scale benchmarking study using alignments of 101 protein families containing 1899 FIS showed that our method achieved an average approximately 70% sensitivity in predicting different types of FIS, such as active sites, metal, ligand or protein binding sites. Our findings also indicate that active sites and metal binding sites are comparably easier to predict compared to the ligand and protein binding sites. Despite moderate success, our results suggest the usefulness and potential of SKLSC approach in prediction of FIS using only protein sequence derived information.