ProtoMap: Automatic classification of protein sequences, a hierarchy of protein families, and local maps of the protein space

Abstract

We investigate the space of all protein sequences in search of clusters of related proteins. Our aim is to automatically detect these sets, and thus obtain a classification of all protein sequences. Our analysis, which uses standard measures of sequence similarity as applied to an all-vs.-all comparison of SWISSPROT, gives a very conservative initial classification based on the highest scoring pairs. The many classes in this classification correspond to protein subfamilies. Subsequently we merge the subclasses using the weaker pairs in a two-phase clustering algorithm. The algorithm makes use of transitivity to identify homologous proteins; however, transitivity is applied restrictively in an attempt to prevent unrelated proteins from clustering together. This process is repeated at varying levels of statistical significance. Consequently, a hierarchical organization of all proteins is obtained. The resulting classification splits the protein space into well-defined groups of proteins, which are closely correlated with natural biological families and superfamilies. Different indices of validity were applied to assess the quality of our classification and compare it with the protein families in the PROSITE and Pfam databases. Our classification agrees with these domain-based classifications for between 64.8% and 88.5% of the proteins. It also finds many new clusters of protein sequences which were not classified by these databases. The hierarchical organization suggested by our analysis reveals finer subfamilies in families of known proteins as well as many novel relations between protein families.

Using Bayesian Networks to Analyze Expression Data

“…Among these, all those involving two known genes make sense biologically. When one of the ORFs is unknown, careful searches using Psi-Blast (Altschul et al, 1997), Pfam (Sonnhammer et al, 1998), and Protomap (Yona et al, 1998) can reveal rm homologies to proteins functionally related to the other gene in the pair. For example YHR143W, which is paired to the endochitinase CTS1, is related to EGT2-a cell-wall-maintenance protein.…”

Scale-free networks versus evolutionary drift

“…Aside from considering the interactions among different components, one may also consider the similarities among them. In this way, one may consider and investigate protein sequence similarity graphs (Yona, Linial, & Linial, 1999;Karev, Wolf, & Koonin, 2003) and protein structure similarity graphs . Here, the nodes correspond to protein domains and edges indicate a sequence/structure similarity.…”

Estimating the probability for a protein to have a new fold: A statistical computational model

“…Two more classes (designed proteins and nonprotein) are not considered in this study. Notice that some changes in SCOP class definition is associated with a recent release (SCOP 1.48, created Dec. 20,1999 (29), the Ras superfamily (30), and more. The notion of the PROTOMAP graph and the information that may be extracted from the PROTOMAP graph are illustrated by a specific example of the globin family (Fig.…”