Research

In only a few years, the sequence of more than 100 complete genomes, ranging from microbes to human, will be known. In order to exploit this potential, the genome sequence information must be decoded in terms of the molecular and cellular functions of the gene products. For many applications, in particular the development of new drugs, information on function must be complemented by knowledge of protein tertiary structure. Functional and structural properties are best detected by detecting homology between proteins of known structure/function. We are developing such methods. Our latest methods (Pcons and Pmembr) has prooven to perform very well on detecting distantly related globular and membrane proteins respectibely. We are also developing other methods to predict the structure of proteins and better understand how proteins folds.

I have since his postdoc period, with David Eisenberg, at UCLA worked on problems related with structure prediction of proteins. Our most important work includes the development of the first successfull consensus (or meta) predictor, Pcons, for fold recognition. In the last two CASP evaluation it has been shown that Pcons (and its successor Pmodeller) performs on top among automatic predictors and actually better than most manual experts although they have access to the results from Pcons.

Lately we have turned his attention to two other problems, membrane proteins and the evolution of protein structure. For membrane proteins he has identified the importance of structural features including interface helices as well as developed improved predictors. His work on evolution of multi-domain proteins have shown that they evolve by the addition of a single domain at the N- or C-terminals. However, proteins with long repeats show a different evolutionary pattern.