eBLOCKs: enumerating conserved protein blocks to achieve maximal sensitivity and specificity

Abstract

Classifying proteins into families and superfamilies allows identification of functionally important conserved domains. The motifs and scoring matrices derived from such conserved regions provide computational tools that recognize similar patterns in novel sequences, and thus enable the prediction of protein function for genomes. The eBLOCKs database enumerates a cascade of protein blocks with varied conservation levels for each functional domain. A biologically important region is most stringently conserved among a smaller family of highly similar proteins. The same region is often found in a larger group of more remotely related proteins with a reduced stringency. Through enumeration, highly specific signatures can be generated from blocks with more columns and fewer family members, while highly sensitive signatures can be derived from blocks with fewer columns and more members as in a superfamily. By applying PSI-BLAST and a modified K-means clustering algorithm, eBLOCKs automatically groups protein sequences according to different levels of similarity. Multiple sequence alignments are made and trimmed into a series of ungapped blocks. Motifs and position-specific scoring matrices were derived from eBLOCKs and made available for sequence search and annotation. The eBLOCKs database provides a tool for high-throughput genome annotation with maximal specificity and sensitivity. The eBLOCKs database is freely available on the World Wide Web at http://motif.stanford.edu/eblocks/ to all users for online usage. Academic and not-for-profit institutions wishing copies of the program may contact Douglas L. Brutlag (brutlag@stanford.edu). Commercial firms wishing copies of the program for internal installation may contact Jacqueline Tay at the Stanford Office of Technology Licensing (jacqueline.tay@stanford.edu; http://otl.stanford.edu/).

Figure 1

A typical PSI-BLAST result have multiple similarity modules. Group 1 contains sequences in Cluster 1; Group 2 contains sequences in Clusters 1 and 2; and Group 3 contains sequences in Clusters 1 and 3.

Figure 2

Clusters defined by K-means clustering are organized into groups. A typical conservation region is represented by multiple groups with different similarity levels, so as to maximize specificity and sensitivity. Group 8 contains sequences in Cluster 8; Group 2 contains sequences in Clusters 8 and 2; Group 9 contains sequences in Clusters 8, 2 and 9.

Figure 3

A flowchart for the eBLOCKs algorithm. Similarity groups that represent shared modules at different conservation levels are formed by the clustering and grouping of all the subject sequences returned by a PSI-BLAST search. Sequences in each group are aligned and the ungapped regions are excised to form several blocks. An eBLOCK accession number is composed of three parts: the SWISS-PROT accession number of the seed sequence, the group number as assigned by K-means clustering and the block number as the sequential number of trimmed blocks from the multiple sequence alignment for the group.

Figure 4

Statistics of the current eBLOCKs database. (a) The distribution of the average information content for the blocks. (b) The distribution of block width. (c) The distribution of the number of sequences contained in the blocks.