Toward rational protein crystallization: A Web server for the design of crystallizable protein variants

Abstract

Growing well-diffracting crystals constitutes a serious bottleneck in structural biology. A recently proposed crystallization methodology for "stubborn crystallizers" is to engineer surface sequence variants designed to form intermolecular contacts that could support a crystal lattice. This approach relies on the concept of surface entropy reduction (SER), i.e., the replacement of clusters of flexible, solvent-exposed residues with residues with lower conformational entropy. This strategy minimizes the loss of conformational entropy upon crystallization and renders crystallization thermodynamically favorable. The method has been successfully used to crystallize more than 15 novel proteins, all stubborn crystallizers. But the choice of suitable sites for mutagenesis is not trivial. Herein, we announce a Web server, the surface entropy reduction prediction server (SERp server), designed to identify mutations that may facilitate crystallization. Suggested mutations are predicted based on an algorithm incorporating a conformational entropy profile, a secondary structure prediction, and sequence conservation. Minor considerations include the nature of flanking residues and gaps between mutation candidates. While designed to be used with default values, the server has many user-controlled parameters allowing for considerable flexibility. Within, we discuss (1) the methodology of the server, (2) how to interpret the results, and (3) factors that must be considered when selecting mutations. We also attempt to benchmark the server by comparing the server's predictions with successful SER structures. In most cases, the structure yielding mutations were easily identified by the SERp server. The server can be accessed at http://www.doe-mbi.ucla.edu/Services/SER.

Scheme 1.

Flowchart summarizing the SER prediction process. The submitted gene or peptide sequence undergoes three principal analyses: secondary structure prediction, computation of its side chain entropy profile, and a search for homologous sequences. Potential residue candidates are grouped into clusters and scored using several key principles (see text). Additional meta searches identify functionally important regions, structural homologs, and linkages to potential interacting protein partners.

Figure 1.

(A) Summary of candidate clusters and proposed mutations therein for YkuD (PDB code 1Y7M). The SERp score (arbitrary units) is shown for each cluster. (B) A stacked summary graph showing the contribution from each principal analysis. The blue peaks (top) indicate residues predicted to be in loops. Red peaks correspond to high entropy regions (middle). Cyan peaks (bottom) indicate residues that in homologous structures are changed to one of the target residues. Residues at peaks of this stacked graph are preferred for mutation. Proposed mutations are highlighted in green. Residue types are shown below the protein sequence: high entropy residues in pink, mutable residues in red, low entropy target residues in yellow, and high entropy residues proposed for mutation in green. Selected results from performed meta searches are also shown. The solvent accessibility estimated from homologous structures (Kabsch and Sander 1983) is shown as shades of gray (darker shades indicate higher solvent accessibility). Highly conserved sequence regions identified with Blocks (Henikoff et al. 1999) are marked in magenta. Predicted secondary structure is shown schematically at the very bottom in blue.