De novo protein design. I. In search of stability and specificity.
We have developed a fully automated protein design strategy that works on the entire sequence of the protein and uses a full atom representation. At each step of the procedure, an all-atom model of the protein is built using the template protein structure and the current designed sequence. The energy of the model is used to drive a Monte Carlo optimization in sequence space: random moves are either accepted or rejected based on the Metropolis criterion. We rely on the physical forces that stabilize native protein structures to choose the optimum sequence. Our energy function includes van der Waals interactions, electrostatics and an environment free energy. Successful protein design should be specific and generate a sequence compatible with the template fold and incompatible with competing folds. We impose specificity by maintaining the amino acid composition constant, based on the random energy model. The specificity of the optimized sequence is tested by fold recognition techniques. Successful sequence designs for the B1 domain of protein G, for the lambda repressor and for sperm whale myoglobin are presented. We show that each additional term of the energy function improves the performance of our design procedure: the van der Waals term ensures correct packing, the electrostatics term increases the specificity for the correct native fold, and the environment solvation term ensures a correct pattern of buried hydrophobic and exposed hydrophilic residues. For the globin family, we show that we can design a protein sequence that is stable in the myoglobin fold, yet incompatible with the very similar hemoglobin fold.
Koehl
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Department of Structural Biology, Fairchild Building, Stanford University, Stanford, CA 94305, USA. koehl@hyper.stanford.edu
Levitt
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D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D004268:DNA-Binding Proteins; D016208:Databases, Factual; D004355:Drug Stability; D006454:Hemoglobins / Q000737:chemistry / Q000378:metabolism; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009010:Monte Carlo Method; D009154:Mutation; D009211:Myoglobin / Q000737:chemistry / Q000378:metabolism; D015202:Protein Engineering / Q000379:methods*; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012097:Repressor Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D016415:Sequence Alignment; D012984:Software; D012997:Solvents; D055672:Static Electricity; D013329:Structure-Activity Relationship; D013379:Substrate Specificity; D013816:Thermodynamics; D014764:Viral Proteins; D054334:Viral Regulatory and Accessory Proteins
D001426:Bacterial Proteins; D004268:DNA-Binding Proteins; D006454:Hemoglobins; D009211:Myoglobin; D011506:Proteins; D012097:Repressor Proteins; D012997:Solvents; D014764:Viral Proteins; D054334:Viral Regulatory and Accessory Proteins; C016649:phage repressor proteins