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Johannes Frey-Skött

Publications and source records attributed to Johannes Frey-Skött.

3 recordsLinked to original sources

Domain rearrangements in protein evolution.

Most eukaryotic proteins are multi-domain proteins that are created from fusions of genes, deletions and internal repetitions. An investigation of such evolutionary events requires a method to find the domain architecture from which each protein originates. Therefore, we defined a novel measure, domain distance, which is calculated as the number of domains that differ between two domain architectures. Using this measure the evolutionary events that distinguish a protein from its closest ancestor have been studied and it was found that indels are more common than internal repetition and that the exchange of a domain is rare. Indels and repetitions are common at both the N and C-terminals while they are rare between domains. The evolution of the majority of multi-domain proteins can be explained by the stepwise insertions of single domains, with the exception of repeats that sometimes are duplicated several domains in tandem. We show that domain distances agree with sequence similarity and semantic similarity based on gene ontology annotations. In addition, we demonstrate the use of the domain distance measure to build evolutionary trees. Finally, the evolution of multi-domain proteins is exemplified by a closer study of the evolution of two protein families, non-receptor tyrosine kinases and RhoGEFs.

Databases, Protein↗

Multi-domain proteins in the three kingdoms of life: orphan domains and other unassigned regions.

Comparative studies of the proteomes from different organisms have provided valuable information about protein domain distribution in the kingdoms of life. Earlier studies have been limited by the fact that only about 50% of the proteomes could be matched to a domain. Here, we have extended these studies by including less well-defined domain definitions, Pfam-B and clustered domains, MAS, in addition to Pfam-A and SCOP domains. It was found that a significant fraction of these domain families are homologous to Pfam-A or SCOP domains. Further, we show that all regions that do not match a Pfam-A or SCOP domain contain a significantly higher fraction of disordered structure. These unstructured regions may be contained within orphan domains or function as linkers between structured domains. Using several different definitions we have re-estimated the number of multi-domain proteins in different organisms and found that several methods all predict that eukaryotes have approximately 65% multi-domain proteins, while the prokaryotes consist of approximately 40% multi-domain proteins. However, these numbers are strongly dependent on the exact choice of cut-off for domains in unassigned regions. In conclusion, all eukaryotes have similar fractions of multi-domain proteins and disorder, whereas a high fraction of repeating domain is distinguished only in multicellular eukaryotes. This implies a role for repeats in cell-cell contacts while the other two features are important for intracellular functions.

Amino Acid Sequence↗

Using evolutionary information for the query and target improves fold recognition.

In this study, we show that it is possible to increase the performance over PSI-BLAST by using evolutionary information for both query and target sequences. This information can be used in three different ways: by sequence linking, profile-profile alignments, and by combining sequence-profile and profile-sequence searches. If only PSI-BLAST is used, 16% of superfamily-related protein domains can be detected at 90% specificity, but if a sequence-profile and a profile-sequence search are combined, this is increased to 20%, profile-profile searches detects 19%, whereas a linking procedure identifies 22% of these proteins. All three methods show equal performance, but the best combination of speed and accuracy seems to be obtained by the combined searches, because this method shows a good performance even at high specificity and the lowest computational cost. In addition, we show that the E-values reported by all these methods, including PSI-BLAST, underestimate the true rate of false positives. This behavior is seen even if a very strict E-value cutoff and a limited number of iterations are used. However, the difference is more pronounced with a looser E-value cutoff and more iterations.

Computational Biology↗