Summary:  Our main goal is to predict important aspects of protein structure and function using sequence information, evolutionary information and results from other predictions. We apply whichever type of algorithm is needed to solve a problem from modern machine learning (neural networks, SVMs, tree-algorithms, Bayesian classifiers) to established statistical means.

Protein prediction:  The lab's research is driven by a conviction that protein and DNA sequences encode a significant core of information about the ultimate structure and function of genetic material and its gene products. Research goals of the lab involve using protein and DNA sequences along with evolutionary information to predict aspects of the proteins relevant to the advance of biomedical research. Examples are the prediction of coarse-grained aspects of protein function such as the type of enzymatic activity (ECGO), the prediction of interaction partners (ISIS, DISIS, PiNAT), subcellular localization (LOCtree, LOCnet, PredictNLS), and of functional effects of point mutations/SNPs (SNAP), the prediction of disordered regions (NORSp, Ucon, IUcon), membrane spanning segments (PROF/PHDhtm), aspects of protein secondary structure (PROF/PHD, DSSPcont) and solvent accessibility (PROF/PHD), internal residue-residue contacts (PROFcon), the identification of domain-like functional and structural subunits (CHOP, CHOPnet), as well as the clustering of proteins into families (CHOP).

RNA:  We have also ventured outside the world of proteins, into the amazingly large world of long non-coding RNAs. In particular, we developed a method that distinguishes coding from non-coding regions (RIKEN). According to our estimates there are as many of these long non-coding regions in mouse as there are proteins without even considering the large universe of short sRNAS!

Our research spans from molecular details (ISIS, MD) to the level of systems biology (PiNAT); it involves the identification of binding sites and the prediction of roles by distinguishing cell cycle kinases from other kinases. We have been developing de novo prediction methods as well as alignment methods for database comparisons (AGAPE, ConsensusBLAST).

Comparative proteomics:  Most of our work aims at providing tools to annotate entire genomes, i.e. the means for comparative genomics. Another significant research focus is to improve the effectiveness and efficiency of structural genomics projects' ability to determine the structures of proteins on a large scale.

Availibilty:  We dedicate unusual amounts of resources to the maintenance of internet servers that make the fruits of our research available to the biomedical community at large. This includes, PredictProtein, the first Internet server for protein structure prediction, and META-PP, as well as more recent databases and resources.