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In silico protein design by combinatorial assembly of protein building blocks

¹ Basic Research Program, Science Applications International Corp. (SAIC)-Frederick, Inc., Laboratory of Experimental and Computational Biology, Frederick, Maryland 21702, USA
² Sackler Institute of Molecular Medicine, Department of Human Genetics, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel

(RECEIVED March 29, 2004; FINAL REVISION May 23, 2004; ACCEPTED July 12, 2004)

Utilizing concepts of protein building blocks, we propose a de novo computational algorithm that is similar to combinatorial shuffling experiments. Our goal is to engineer new naturally occurring folds with low homology to existing proteins. A selected protein is first partitioned into its building blocks based on their compactness, degree of isolation from the rest of the structure, and hydrophobicity. Next, the protein building blocks are substituted by fragments taken from other proteins with overall low sequence identity, but with a similar hydrophobic/hydrophilic pattern and a high structural similarity. These criteria ensure that the designed protein has a similar fold, low sequence identity, and a good hydrophobic core compared with its native counterpart. Here, we have selected two proteins for engineering, protein G B1 domain and ubiquitin. The two engineered proteins share ~20% and ~25% amino acid sequence identities with their native counterparts, respectively. The stabilities of the engineered proteins are tested by explicit water molecular dynamics simulations. The algorithm implements a strategy of designing a protein using relatively stable fragments, with a high population time. Here, we have selected the fragments by searching for local minima along the polypeptide chain using the protein building block model. Such an approach provides a new method for engineering new proteins with similar folds and low homology.

Keywords: protein building block; computational protein design; combinatorial assembly; protein G; ubiquitin; molecular dynamics simulation

Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.04774004.

Reprint requests to: Ruth Nussinov, Basic Research Program, SAIC-Frederick, Inc., Laboratory of Experimental and Computational Biology, NCI-Frederick, Building 469, Room 145, Frederick, MD 21702, USA; e-mail: ruthn{at}ncifcrf.gov; fax: (301) 846-5598.

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