Sequence Conservation in the Prediction of Catalytic Sites

A new bioinformatics approach to natural protein collections: permutation structure contrasts of viral and cellular systems

Biological cells and viruses operate by different replication and symmetry paradigms. Cells are able to replicate independently and express little spatial symmetry; viruses require cells for replication while manifesting high symmetry. The author inquired whether different paradigms were reflected in the permutations of amino acid sequences. The hypothesis was that the permutation structure level and symmetry within viral protein collections exceed that of living cells. The rationale was that one symmetry aspect generally accompanies and promotes others in a system. The inquiry was readily answered given abundant sequence archives for proteins. The analysis of collections from diverse viral and cellular sources lends strong support. Additional insights into protein primary structure, the design of collections, and the role of information are provided as well.

CLIPS-1D: analysis of multiple sequence alignments to deduce for residue-positions a role in catalysis, ligand-binding, or protein structure

Background

One aim of the in silico characterization of proteins is to identify all residue-positions, which are crucial for function or structure. Several sequence-based algorithms exist, which predict functionally important sites. However, with respect to sequence information, many functionally and structurally important sites are hard to distinguish and consequently a large number of incorrectly predicted functional sites have to be expected. This is why we were interested to design a new classifier that differentiates between functionally and structurally important sites and to assess its performance on representative datasets.

Results

We have implemented CLIPS-1D, which predicts a role in catalysis, ligand-binding, or protein structure for residue-positions in a mutually exclusive manner. By analyzing a multiple sequence alignment, the algorithm scores conservation as well as abundance of residues at individual sites and their local neighborhood and categorizes by means of a multiclass support vector machine. A cross-validation confirmed that residue-positions involved in catalysis were identified with state-of-the-art quality; the mean MCC-value was 0.34. For structurally important sites, prediction quality was considerably higher (mean MCC = 0.67). For ligand-binding sites, prediction quality was lower (mean MCC = 0.12), because binding sites and structurally important residue-positions share conservation and abundance values, which makes their separation difficult. We show that classification success varies for residues in a class-specific manner. This is why our algorithm computes residue-specific p-values, which allow for the statistical assessment of each individual prediction. CLIPS-1D is available as a Web service at http://www-bioinf.uni-regensburg.de/.

Conclusions

CLIPS-1D is a classifier, whose prediction quality has been determined separately for catalytic sites, ligand-binding sites, and structurally important sites. It generates hypotheses about residue-positions important for a set of homologous proteins and focuses on conservation and abundance signals. Thus, the algorithm can be applied in cases where function cannot be transferred from well-characterized proteins by means of sequence comparison.