Recurrent structural motifs in non-homologous protein structures

HH-MOTiF: de novo detection of short linear motifs in proteins by Hidden Markov Model comparisons

Short linear motifs (SLiMs) in proteins are self-sufficient functional sequences that specify interaction sites for other molecules and thus mediate a multitude of functions. Computational, as well as experimental biological research would significantly benefit, if SLiMs in proteins could be correctly predicted de novo with high sensitivity. However, de novo SLiM prediction is a difficult computational task. When considering recall and precision, the performances of published methods indicate remaining challenges in SLiM discovery. We have developed HH-MOTiF, a web-based method for SLiM discovery in sets of mainly unrelated proteins. HH-MOTiF makes use of evolutionary information by creating Hidden Markov Models (HMMs) for each input sequence and its closely related orthologs. HMMs are compared against each other to retrieve short stretches of homology that represent potential SLiMs. These are transformed to hierarchical structures, which we refer to as motif trees, for further processing and evaluation. Our approach allows us to identify degenerate SLiMs, while still maintaining a reasonably high precision. When considering a balanced measure for recall and precision, HH-MOTiF performs better on test data compared to other SLiM discovery methods. HH-MOTiF is freely available as a web-server at http://hh-motif.biochem.mpg.de.

Artificial Diiron Enzymes with a De Novo Designed Four-Helix Bundle Structure

A single polypeptide chain may provide an astronomical number of conformers. Nature selected only a trivial number of them through evolution, composing an alphabet of scaffolds, that can afford the complete set of chemical reactions needed to support life. These structural templates are so stable that they allow several mutations without disruption of the global folding, even having the ability to bind several exogenous cofactors. With this perspective, metal cofactors play a crucial role in the regulation and catalysis of several processes. Nature is able to modulate the chemistry of metals, adopting only a few ligands and slightly different geometries. Several scaffolds and metal-binding motifs are representing the focus of intense interest in the literature. This review discusses the widespread four-helix bundle fold, adopted as a scaffold for metal binding sites in the context of de novo protein design to obtain basic biochemical components for biosensing or catalysis. In particular, we describe the rational refinement of structure/function in diiron-oxo protein models from the due ferri (DF) family. The DF proteins were developed by us through an iterative process of design and rigorous characterization, which has allowed a shift from structural to functional models. The examples reported herein demonstrate the importance of the synergic application of de novo design methods as well as spectroscopic and structural characterization to optimize the catalytic performance of artificial enzymes.