Knowledge-based potential functions in protein design

Empirical force fields for biological macromolecules: overview and issues

Empirical force field-based studies of biological macromolecules are becoming a common tool for investigating their structure-activity relationships at an atomic level of detail. Such studies facilitate interpretation of experimental data and allow for information not readily accessible to experimental methods to be obtained. A large part of the success of empirical force field-based methods is the quality of the force fields combined with the algorithmic advances that allow for more accurate reproduction of experimental observables. Presented is an overview of the issues associated with the development and application of empirical force fields to biomolecular systems. This is followed by a summary of the force fields commonly applied to the different classes of biomolecules; proteins, nucleic acids, lipids, and carbohydrates. In addition, issues associated with computational studies on "heterogeneous" biomolecular systems and the transferability of force fields to a wide range of organic molecules of pharmacological interest are discussed.

Selection of stably folded proteins by phage-display with proteolysis

To facilitate the process of protein design and learn the basic rules that control the structure and stability of proteins, combinatorial methods have been developed to select or screen proteins with desired properties from libraries of mutants. One such method uses phage-display and proteolysis to select stably folded proteins. This method does not rely on specific properties of proteins for selection. Therefore, in principle it can be applied to any protein. Since its first demonstration in 1998, the method has been used to create hyperthermophilic proteins, to evolve novel folded domains from a library generated by combinatorial shuffling of polypeptide segments and to convert a partially unfolded structure to a fully folded protein.

Optimization of the Antibody CH3 Domain by Residue Frequency Analysis of IgG Sequences

In an attempt to enhance the overall assembly, yield and half-life of recombinant antibody proteins, we have cloned and expressed several IgG1 C(H)3 domains and examined their folding/refolding characteristics. We utilized a cytoplasmic bacterial expression system with a thioredoxin reductase knock-out strain of BL21(DE3) to produce bovine, murine and human C(H)3. Under identical conditions, expression of bovine C(H)3 resulted consistently in the highest yields of properly folded/oxidized protein. Circular dichroism and fluorescence experiments demonstrate that oxidized bovine and murine C(H)3 have surprisingly similar structures and stabilities, considering the marginal sequence conservation between the two molecules. Residue frequency analysis using a limited data set of 36 unique Fc sequences originating from 19 different mammalian species targeted five specific sites for optimization within bovine C(H)3. Combination of three of these mutants increased the thermal stability of the molecule to 86 degrees C. Comparison of this approach to similar studies using larger sequence databases and/or different selection criteria suggests sequence database design can increase the success rate for identifying residue sites worth optimizing. This optimized C(H)3 domain can be used as a particularly stable platform for functional design and can be grafted into full-length antibody sequences to enhance their thermodynamic parameters and shelf-life.

Combining inference from evolution and geometric probability in protein structure evaluation

Starting from the hypothesis that evolutionarily important residues form a spatially limited cluster in a protein's native fold, we discuss the possibility of detecting a non-native structure based on the absence of such clustering. The relevant residues are determined using the Evolutionary Trace method. We propose a quantity to measure clustering of the selected residues on the structure and show that the exact values for its average and variance over several ensembles of interest can be found. This enables us to study the behavior of the associated z-scores. Since our approach rests on an analytic result, it proves to be general, customizable, and computationally fast. We find that clustering is indeed detectable in a large representative protein set. Furthermore, we show that non-native structures tend to achieve lower residue-clustering z-scores than those attained by the native folds. The most important conclusion that we draw from this work is that consistency between structural and evolutionary information, manifested in clustering of key residues, imposes powerful constraints on the conformational space of a protein.