Knowledge-based potentials in protein design.

Knowledge-based potentials are statistical parameters derived from databases of known protein properties that empirically capture aspects of the physical chemistry of protein structure and function. These potentials play a key role in protein design by improving the accuracy of physics-based models of interatomic interactions and enhancing the computational efficiency of the design process by limiting the complexity of searching sequence space. Recently, knowledge-based potentials (in isolation or in combination with physics-based potentials) have been applied to the modification of existing protein function, the redesign of natural protein folds and the complete design of a non-natural protein fold. In addition, knowledge-based potentials appear to be providing important information about the global topology of amino acid interactions in natural proteins. A detailed study of the methods and products of these protein design efforts promises to greatly expand our understanding of proteins and the evolutionary process that created them.

[1]  D. Woolfson The design of coiled-coil structures and assemblies. , 2005, Advances in protein chemistry.

[2]  S. A. Marshall,et al.  Achieving stability and conformational specificity in designed proteins via binary patterning. , 2001, Journal of molecular biology.

[3]  L. Looger,et al.  Computational design of receptor and sensor proteins with novel functions , 2003, Nature.

[4]  Ned S Wingreen,et al.  Fast accurate evaluation of protein solvent exposure , 2004, Proteins.

[5]  R. Ranganathan,et al.  Evolutionarily conserved pathways of energetic connectivity in protein families. , 1999, Science.

[6]  Loren L Looger,et al.  Computational Design of a Biologically Active Enzyme , 2004, Science.

[7]  W. Atchley,et al.  Correlations among amino acid sites in bHLH protein domains: an information theoretic analysis. , 2000, Molecular biology and evolution.

[8]  Roland L. Dunbrack Rotamer libraries in the 21st century. , 2002, Current opinion in structural biology.

[9]  L. H. Bradley,et al.  Protein design by binary patterning of polar and nonpolar amino acids. , 1993, Methods in molecular biology.

[10]  D. Thirumalai,et al.  Determination of network of residues that regulate allostery in protein families using sequence analysis , 2006, Protein science : a publication of the Protein Society.

[11]  M. Donoghue,et al.  Recreating a functional ancestral archosaur visual pigment. , 2002, Molecular biology and evolution.

[12]  D. Baker,et al.  Improved recognition of native‐like protein structures using a combination of sequence‐dependent and sequence‐independent features of proteins , 1999, Proteins.

[13]  M. Matz,et al.  Evolution of Coral Pigments Recreated , 2004, Science.

[14]  John R Desjarlais,et al.  A de novo redesign of the WW domain , 2003, Protein science : a publication of the Protein Society.

[15]  Wendell A. Lim,et al.  Optimization of specificity in a cellular protein interaction network by negative selection , 2003, Nature.

[16]  Jeffrey B. Endelman,et al.  Structure-Guided Recombination Creates an Artificial Family of Cytochromes P450 , 2006, PLoS biology.

[17]  D. Baker,et al.  An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. , 2003, Journal of molecular biology.

[18]  Rama Ranganathan,et al.  Knowledge-based potential functions in protein design. , 2002, Current opinion in structural biology.

[19]  W. Aehle,et al.  Construction of stabilized proteins by combinatorial consensus mutagenesis. , 2004, Protein engineering, design & selection : PEDS.

[20]  Jon E. Ness,et al.  Synthetic shuffling expands functional protein diversity by allowing amino acids to recombine independently , 2002, Nature Biotechnology.

[21]  P. Harbury,et al.  Automated design of specificity in molecular recognition , 2003, Nature Structural Biology.

[22]  A. D. McLachlan,et al.  Solvation energy in protein folding and binding , 1986, Nature.

[23]  W. P. Russ,et al.  Evolutionary information for specifying a protein fold , 2005, Nature.

[24]  S. Demarest,et al.  Optimization of the antibody C(H)3 domain by residue frequency analysis of IgG sequences. , 2004, Journal of molecular biology.

[25]  Thomas E. Ferrin,et al.  Designed divergent evolution of enzyme function , 2006, Nature.

[26]  Hidetoshi Kono,et al.  Computational design and characterization of a monomeric helical dinuclear metalloprotein. , 2003, Journal of molecular biology.

[27]  David Crews,et al.  Resurrecting the Ancestral Steroid Receptor: Ancient Origin of Estrogen Signaling , 2003, Science.

[28]  D. Baker,et al.  A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins. , 2003, Journal of molecular biology.

[29]  L. H. Bradley,et al.  De novo proteins from designed combinatorial libraries , 2004, Protein science : a publication of the Protein Society.

[30]  Julia M. Shifman,et al.  Exploring the origins of binding specificity through the computational redesign of calmodulin , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Design of lambda Cro fold: solution structure of a monomeric variant of the de novo protein. , 2005, Journal of molecular biology.

[32]  Julia M. Shifman,et al.  Modulating calmodulin binding specificity through computational protein design. , 2002, Journal of molecular biology.

[33]  Yinan Wei,et al.  Enzyme-like proteins from an unselected library of designed amino acid sequences. , 2004, Protein engineering, design & selection : PEDS.

[34]  W. P. Russ,et al.  Natural-like function in artificial WW domains , 2005, Nature.

[35]  M. Ota,et al.  Design of λ Cro Fold: Solution Structure of a Monomeric Variant of the De Novo Protein , 2005 .

[36]  S L Mayo,et al.  Pairwise calculation of protein solvent-accessible surface areas. , 1998, Folding & design.

[37]  Jeffery G. Saven,et al.  Computational design of water-soluble analogues of the potassium channel KcsA , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Linda A. Castle,et al.  Discovery and Directed Evolution of a Glyphosate Tolerance Gene , 2004, Science.

[39]  Rama Ranganathan,et al.  Structural Determinants of Allosteric Ligand Activation in RXR Heterodimers , 2004, Cell.

[40]  S. L. Mayo,et al.  De novo backbone and sequence design of an idealized α/β-barrel protein: evidence of stable tertiary structure , 2003 .

[41]  Loren L Looger,et al.  Computational design of receptors for an organophosphate surrogate of the nerve agent soman. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[42]  W. DeGrado,et al.  Computational design of a water‐soluble analog of phospholamban , 2003, Protein science : a publication of the Protein Society.

[43]  Rama Ranganathan,et al.  Allosteric determinants in guanine nucleotide-binding proteins , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[44]  F. Cohen,et al.  An evolutionary trace method defines binding surfaces common to protein families. , 1996, Journal of molecular biology.

[45]  L L Looger,et al.  Computational design of a Zn2+ receptor that controls bacterial gene expression , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[46]  S. L. Mayo,et al.  De novo backbone and sequence design of an idealized alpha/beta-barrel protein: evidence of stable tertiary structure. , 2003, Journal of molecular biology.

[47]  J G Saven,et al.  Statistical theory for protein combinatorial libraries. Packing interactions, backbone flexibility, and the sequence variability of a main-chain structure. , 2001, Journal of molecular biology.

[48]  D. Baker,et al.  Design of a Novel Globular Protein Fold with Atomic-Level Accuracy , 2003, Science.

[49]  Stefan M. Larson,et al.  Analysis of covariation in an SH3 domain sequence alignment: applications in tertiary contact prediction and the design of compensating hydrophobic core substitutions. , 2000, Journal of molecular biology.

[50]  S. Benner,et al.  Inferring the palaeoenvironment of ancient bacteria on the basis of resurrected proteins , 2003, Nature.

[51]  Wei Yang,et al.  Design of a calcium-binding protein with desired structure in a cell adhesion molecule. , 2005, Journal of the American Chemical Society.

[52]  E. Shakhnovich,et al.  Understanding hierarchical protein evolution from first principles. , 2001, Journal of molecular biology.

[53]  Vikas Nanda,et al.  De novo design of a redox-active minimal rubredoxin mimic. , 2005, Journal of the American Chemical Society.

[54]  Ke Liu,et al.  A despecialization step underlying evolution of a family of serine proteases. , 2003, Molecular cell.

[55]  C. Anfinsen Principles that govern the folding of protein chains. , 1973, Science.