Given that the number of protein functions on earth is finite, the rapid expansion of biological knowledge and the concomitant exponential increase in the number of protein sequences should, at some point, enable the estimation of the limits of protein function space. The functional coverage of protein sequences can be investigated using computational methods, especially given the massive amount of data being generated by large-scale environmental sequencing (metagenomics). In completely sequenced genomes, the fraction of proteins to which at least some functional features can be assigned has recently risen to as much as approximately 85%. Although this fraction is more uncertain in metagenomics surveys, because of environmental complexities and differences in analysis protocols, our global knowledge of protein functions still appears to be considerable. However, when we consider protein families, continued sequencing seems to yield an ever-increasing number of novel families. Until we reconcile these two views, the limits of protein space will remain obscured.

Protein interaction networks summarize large amounts of protein-protein interaction data, both from individual, small-scale experiments and from automated high-throughput screens. The past year has seen a flood of new experimental data, especially on metazoans, as well as an increasing number of analyses designed to reveal aspects of network topology, modularity and evolution. As only minimal progress has been made in mapping the human proteome using high-throughput screens, the transfer of interaction information within and across species has become increasingly important. With more and more heterogeneous raw data becoming available, proper data integration and quality control have become essential for reliable protein network reconstruction, and will be especially important for reconstructing the human protein interaction network.

With the massive amount of sequence and structural data being produced, new avenues emerge for exploiting the information therein for applications in several fields. Fold distributions can be mapped onto entire genomes to learn about the nature of the protein universe and many of the interactions between proteins can now be predicted solely on the basis of the genomic context of their genes. Furthermore, by utilising the new incoming data on single nucleotide polymorphisms by mapping them onto three-dimensional structures of proteins, problems concerning population, medical and evolutionary genetics can be addressed.

Recently, a number of techniques have been proposed that use completely sequenced genomes for the function prediction of individual proteins encoded therein. They use the fusion of genes, their conserved location in operons or merely their co-occurrence in genomes to predict the existence of functional interactions between the proteins they encode. This type of information complements functional features that are predicted by classical homology-based search techniques.

The complete sequence of the nematode worm Caenorhabditis elegans contains the genetic machinery that is required to undertake the core biological processes of single cells. However, the genome also encodes proteins that are associated with multicellularity, as well as others that are lineage-specific expansions of phylogenetically widespread families and yet more that are absent in non-nematodes. Ongoing analysis is beginning to illuminate the similarities and differences among human proteins and proteins that are encoded by the genomes of the multicellular worm and the unicellular yeast, and will be essential in determining the reliability of transferring experimental data among phylogenetically distant species.

Protein sequence motifs are signatures of protein families and can often be used as tools for the prediction of protein function. The generalization and modification of already known motifs are becoming major trends in the literature, even though new motifs are still being discovered at an approximately linear rate. The emphasis of motif analysis appears to be shifting from metabolic enzymes, in which motifs are associated with catalytic functions and thus often readily recognizable, to structural and regulatory proteins, which contain more divergent motifs. The consideration of structural information increasingly contributes to the identification of motifs and their sensitivity. Genome sequencing provides the basis for a systematic analysis of all motifs that are present in a particular organism. A systematically derived motif database is therefore feasible, allowing the classification of the majority of the newly appearing protein sequences into known families.