Computational protein design: validation and possible relevance as a tool for homology searching and fold recognition.
BACKGROUND
Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases.


RESULTS
WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed.


CONCLUSIONS
For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use.
Schmidt Am Busch
Marcel
M
Laboratoire de Biochimie (CNRS UMR7654), Department of Biology, Ecole Polytechnique, Palaiseau, France.
Sedano
Audrey
A
Simonson
Thomas
T
D000596:Amino Acids / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D019277:Entropy; D008958:Models, Molecular; D009154:Mutation / Q000235:genetics; D054731:PDZ Domains; D056510:Position-Specific Scoring Matrices; D055550:Protein Stability; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*; D040681:Structural Homology, Protein
D000596:Amino Acids; D011506:Proteins
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