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One of the goals in computational protein design is to develop algorithms for predicting amino acid sequences that would adopt a specified three-dimensional structure. It is an extremely progressive area of research and has been successfully applied to engineer proteins with improved stabilities and activities [refer to [1]for a recent review]. Developments in this field are beginning to have an impact on biotechnology and further advancements in improving design strategies are expected to expand the range of applicability of computational protein design to larger and more complex biological systems. Besides providing as a suitable tool for designing proteins for medicinal and industrial purposes, the development of protein design tools should also confer a deeper insight into the principles that underlie protein sequence-structure relationship.
There are three primary aspects of computational protein design that, although quite distinct from one another, are significantly interdependent. The progress achieved by several groups in this field shows that improvements in design techniques have been made possible through refinements in these areas. The first aspect concerns the energy expression used to assess and score the relative fitness of different amino acid sequences with respect to the desired protein fold. The second area deals with the way in which the protein design problem, the model, is represented. The model provides a framework for describing the target fold and its flexibility, the amino acids allowed for design positions, and the rotamer library used to represent the possible side chain conformations. The third area of enhancement is the search strategy used to scan the enormous combinatorial complexity of possible sequences and selecting those that are optimal for a given fold.
Since the inception of computational protein design, elements of a suitable energy
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expression or force field to rank the desirability of an amino acid sequence for a particular backbone structure have been suggested and evaluated. Alterations and additions to the energy terms to improve the correlation between computed and experimentally observed properties are usually achieved by iterating between theory and experiment [2]. The force- field terms describing the non-bond interactions are usually explored for improvement, while the bonded energies are taken from commonly used molecular mechanics force fields [3]. Since the rotamers derived from protein databases generally have good internal energies, and for most design schemes, rigid backbone structures are used, the usefulness of improving “bonded” energies has not been rigorously demonstrated in protein design.
For protein cores, a force field that models packing specificity is usually sufficient to design a well- folded protein [2, 4]. For designing protein surfaces however, energy terms that properly balance non-bonded polar interactions also need to be included in the potential function. Chapter 7 describes an approach used to derive a potential function for designing ß-sheet surfaces. This method was used to create a plastocyanin variant with enhanced thermostability. The derived potential placed more importance on electrostatic interactions that led to the selection of charged residues on protein surfaces. An important next step in understanding β-sheet stability was to define the role of side-chain ionic interactions. Chapter 8 outlines a study that evaluates the interaction energy of a three- residue ionic network constructed on the ß-sheet surface of protein G.
Except for a few notable exceptions, the models used to represent protein design problems in most cases, do not allow for backbone flexibility; side-chain flexibility is incorporated by selecting amino acid rotamers from a library of discrete conformations
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[5-9]. If necessary, one may use rotamer libraries with different levels of resolution.
However, because the size of the design problem grows exponentially with the increase in the number of rotamers, using a finely descretized rotamer library is often unfeasible.
The rigidity of such a framework ignores the possibility of backbone shifts to accommodate mutations and an incomprehensive rotamer library could lead to the selection of incorrect side chain conformations. These limitations of the design procedure are highlighted in Appendix 1. This section reports a study on the redesign of the core residues in T-4 lysozyme where a significant shift in the protein backbone is observed in the crystal structure of a designed variant.
The third area of thrust in computational protein design deals with refinements in the search strategy. Searches for the optimal sequence for a target protein fold are achieved using various deterministic and stochastic combinatorial optimization algorithms. However, as structural targets get larger, it has become necessary to find more powerful methods to address the increased combinatorial complexities. Efficient algorithms that take into account the limitations of computing power and computational time are being developed and applied to numerous design problems.
The ultimate goal of automated protein design is not only to be able to generate amino acid sequences that are compatible with the given backbones, but also to ensure that the selected sequences are able to perform specific functions. Intermolecular interactions lie at the core of protein function in a wide range of fundamental biochemical processes. Proteins function through their interactions with ligands, other proteins, or surfaces and these interactions are controlled by a complex array of intermolecular forces. In many instances, binding to ligands induces structural changes that allow, for
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example, signal transduction across large distances. Designing ligand binding sites and engineering ligand- induced conformation changes in proteins are very important applications of computational protein design.
Chapters 9 and 10 delve into the application of computational techniques in the area of ligand-protein interactions. Chapter 9 describes the applicability of protein design to alter the specificity of a known binding site to enable it to bind to alternate ligands. An aminoacyl tRNA synthetase with altered ligand specificity was designed and was subsequently shown to be capable of incorporating an artificial amino acid in vivo.
Chapter 10 explores the possibility of using computational methods to manipulate ligand- induced conformational change. The methodology in this study combines computational protein design with techniques from mean- field theory to generate sequences that undergo substantial conformatio nal changes upon ligand binding. The design approach and the results in this study will provide important insights and information that will aid future design efforts in this direction.
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1. Kraemer-Pecore, C.M., Wollacott, A. M., Desjarlais, J. R., Computational protein design. Curr Opin Chem Biol., 2001. 5: p. 690-5.
2. Dahiyat, B.I. and S.L. Mayo, Protein design automation. Protein Sci, 1996. 5(5):
p. 895-903.
3. Gordon, D.B., S.A. Marshall, and S.L. Mayo, Energy functio ns for protein design.
Curr Opin Struct Biol, 1999. 9(4): p. 509-13.
4. Dahiyat, B.I. and S.L. Mayo, Probing the role of packing specificity in protein design. Proc Natl Acad Sci U S A, 1997. 94(19): p. 10172-7.
5. Harbury, P.B., Tidor, B., Kim P. S., Repacking protein cores with backbone freedom: structure prediction for coiled coils. Proc Natl Acad Sci U S A., 1995.
371: p. 8408-12.
6. Harbury, P.B., Plecs, J. J., Tidor, B., Alber, T., Kim P. S., High-resolution protein design with backbone freedom. science, 1998. 92: p. 8408-12.
7. Su, A. and S.L. Mayo, Coupling backbone flexibility and amino acid sequence selection in protein design. Protein Sci, 1997. 6(8): p. 1701-7.
8. Dunbrack, R.L. and M. Karplus, Backbone-dependent rotamer library for proteins. Application to side- chain prediction. J Mol Biol, 1993. 230(2): p. 543- 74.
9. Dunbrack, R.L. and M. Karplus, Conformational analysis of the backbone- dependent rotamer preferences of protein sidechains. Nat Struct Biol, 1994. 1(5):
p. 334-40.
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