Software to detect remote kinship
Interaction of the PAN2 and PAN3 protein. The propeller-like structure of the PAN2 is required for dimer formation with PAN3. Which residues are involved in this close interaction and how they can be predicted are research questions the Computational Biology lab tries to answer.[less]
Interaction of the PAN2 and PAN3 protein. The propeller-like structure of the PAN2 is required for dimer formation with PAN3. Which residues are involved in this close interaction and how they can be predicted are research questions the Computational Biology lab tries to answer.
More than 1,000 scientists cooperated in the Human Genome Project to determine the sequence of the human genome. It was a mammoth project that lasted more than ten years and nowadays would just take a few days. Thanks to new technology, today’s scientific research produces gigantic data sets that can be analyzed with the aid of computers within a short time. To achieve this, however, scientists are dependent on special programs and computer-based solutions. Together with her research group "Computational Biology", Bianca Habermann develops appropriate algorithms and is thus often way ahead of conventional approaches.
At the molecular level, humans resemble seemingly simple organisms more often than we realize. Most human proteins occur in the same or a similar form also in yeasts, flies or worms where they perform similar or even the same tasks. In the course of evolution they have hardly changed and are relatively easy to detect in various species. Certain proteins, however, are less easy to track. Although they are related to each other, they have developed quite differently in the various species.
If the similarity of proteins between the various species is too low, it cannot be detected by conventional software: Bianca Habermann speaks here of the midnight zone of sequence similarity, for which she has especially developed the web tool morFeus. This search engine is available online. Due to its unmatched sensitivity, it can detect the slightest molecular kinship in protein sequences. These findings help to elucidate the conservation of protein families and molecular processes in the course of evolution.
This likewise applies to specific sequence regions in proteins that are important for the interaction of molecules. Thus far, however, they were either too short to detect or were undetectable for other reasons. To identify these essential, functional motifs, Habermann‘s team is pursuing different approaches that have their origin in artificial intelligence.
Whether Habermann applies these methods to search for protein relatives, to focus on molecular changes in cancer, to enable the management of large data sets or to integrate the results of several large-scale studies – one point is particularly important to her: All of her projects arise from the close collaboration with experimental research groups and thus from specific biological problems. The opportunity for this is also provided in the "Bioinformatics Core Facility", which is led by Habermann and assists the MPIB researchers in the computer-based analysis of their data.