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Biomedical subjects

Stephen L Mayo

Publications and source records attributed to Stephen L Mayo.

13 recordsLinked to original sources
PubMed2003

Repacking the Core of T4 lysozyme by automated design.

Automated protein redesign, as implemented in the program ORBIT, was used to redesign the core of phage T4 lysozyme. A total of 26 buried or partially buried sites in the C-terminal domain were allowed to vary both their sequence and side-chain conformation while the backbone and non-selected side-chains remained fixed. A variant with seven substitutions ("Core-7") was identified as having the most favorable energy. The redesign experiment was repeated with a penalty for the presence of methionine residues. In this case the redesigned protein ("Core-10") had ten amino acid changes. The two designed proteins, as well as the constituent single mutants, and several single-site revertants were over-expressed in Escherichia coli, purified, and subjected to crystallographic and thermal analyses. The thermodynamic and structural data show that some repacking was achieved although neither redesigned protein was more stable than the wild-type protein. The use of the methionine penalty was shown to be effective. Several of the side-chain rotamers in the predicted structure of Core-10 differ from those observed. Rather than changing to new rotamers predicted by the design process, side-chains tend to maintain conformations similar to those seen in the native molecule. In contrast, parts of the backbone change by up to 2.8A relative to both the designed structure and wild-type. Water molecules that are present within the lysozyme molecule were removed during the design process. In the redesigned protein the resultant cavities were, to some degree, re-occupied by side-chain atoms. In the observed structure, however, water molecules were still bound at or near their original sites. This suggests that it may be preferable to leave such water molecules in place during the design procedure. The results emphasize the specificity of the packing that occurs within the core of a typical protein. While point substitutions within the core are tolerated they almost always result in a loss of stability. Likewise, combinations of substitutions may also be tolerated but usually destabilize the protein. Experience with T4 lysozyme suggests that a general core repacking methodology with retention or enhancement of stability may be difficult to achieve without provision for shifts in the backbone.

Automation↗
PubMed2003

NMR and temperature-jump measurements of de novo designed proteins demonstrate rapid folding in the absence of explicit selection for kinetics.

We address the importance of natural selection in the origin and maintenance of rapid protein folding by experimentally characterizing the folding kinetics of two de novo designed proteins, NC3-NCAP and ENH-FSM1. These 51 residue proteins, which adopt the helix-turn-helix homeodomain fold, share as few as 12 residues in common with their most closely related natural analog. Despite the replacement of up to 3/4 of their residues by a computer algorithm optimizing only thermodynamic properties, the designed proteins fold as fast or faster than the 35,000 s(-1) observed for the closest natural analog. Thus these de novo designed proteins, which were produced in the complete absence of selective pressures or design constraints explicitly aimed at ensuring rapid folding, are among the most rapidly folding proteins reported to date.

Algorithms↗
PubMed2003

Prudent modeling of core polar residues in computational protein design.

Hydrogen bond interactions were surveyed in a set of protein structures. Compared to surface positions, polar side-chains at core positions form a greater number of intra-molecular hydrogen bonds. Furthermore, the majority of polar side-chains at core positions form at least one hydrogen bond to main-chain atoms that are not involved in hydrogen bonds to other main-chain atoms. Based on this structural survey, hydrogen bond rules were generated for each polar amino acid for use in protein core design. In the context of protein core design, these prudent polar rules were used to eliminate from consideration polar amino acid rotamers that do not form a minimum number of hydrogen bonds. As an initial test, the core of Escherichia coli thioredoxin was selected as a design target. For this target, the prudent polar strategy resulted in a minor increase in computational complexity compared to a strategy that did not allow polar residues. Dead-end elimination was used to identify global minimum energy conformations for the prudent polar and no polar strategies. The prudent polar strategy identified a protein sequence that was thermodynamically stabilized by 2.5 kcal/mol relative to wild-type thioredoxin and 2.2 kcal/mol relative to a thioredoxin variant whose core was designed without polar residues.

Amino Acid Sequence↗
PubMed2003

Exact rotamer optimization for protein design.

Computational methods play a central role in the rational design of novel proteins. The present work describes a new hybrid exact rotamer optimization (HERO) method that builds on previous dead-end elimination algorithms to yield dramatic performance enhancements. Measured on experimentally validated physical models, these improvements make it possible to perform previously intractable designs of entire protein core, surface, or boundary regions. Computational demonstrations include a full core design of the variable domains of the light and heavy chains of catalytic antibody 48G7 FAB with 74 residues and 10(128) conformations, a full core/boundary design of the beta1 domain of protein G with 25 residues and 10(53) conformations, and a full surface design of the beta1 domain of protein G with 27 residues and 10(60) conformations. In addition, a full sequence design of the beta1 domain of protein G is used to demonstrate the strong dependence of algorithm performance on the exact form of the potential function and the fidelity of the rotamer library. These results emphasize that search algorithm performance for protein design can only be meaningfully evaluated on physical models that have been subjected to experimental scrutiny. The new algorithm greatly facilitates ongoing efforts to engineer increasingly complex protein features.

Algorithms↗
PubMed2003

Designing proteins for therapeutic applications.

Protein design is becoming an increasingly useful tool for optimizing protein drugs and creating novel biotherapeutics. Recent progress includes the engineering of monoclonal antibodies, cytokines, enzymes and viral fusion inhibitors.

Animals↗
PubMed2003

Library analysis of SCHEMA-guided protein recombination.

The computational algorithm SCHEMA was developed to estimate the disruption caused when amino acid residues that interact in the three-dimensional structure of a protein are inherited from different parents upon recombination. To evaluate how well SCHEMA predicts disruption, we have shuffled the distantly-related beta-lactamases PSE-4 and TEM-1 at 13 sites to create a library of 2(14) (16,384) chimeras and examined which ones retain lactamase function. Sequencing the genes from ampicillin-selected clones revealed that the percentage of functional clones decreased exponentially with increasing calculated disruption (E = the number of residue-residue contacts that are broken upon recombination). We also found that chimeras with low E have a higher probability of maintaining lactamase function than chimeras with the same effective level of mutation but chosen at random from the library. Thus, the simple distance metric used by SCHEMA to identify interactions and compute E allows one to predict which chimera sequences are most likely to retain their function. This approach can be used to evaluate crossover sites for recombination and to create highly mosaic, folded chimeras.

Algorithms↗
PubMed2002

Modulating calmodulin binding specificity through computational protein design.

We report the computational redesign of the protein-binding interface of calmodulin (CaM), a small, ubiquitous Ca(2+)-binding protein that is known to bind to and regulate a variety of functionally and structurally diverse proteins. The CaM binding interface was optimized to improve binding specificity towards one of its natural targets, smooth muscle myosin light chain kinase (smMLCK). The optimization was performed using optimization of rotamers by iterative techniques (ORBIT), a protein design program that utilizes a physically based force-field and the Dead-End Elimination theorem to compute sequences that are optimal for a given protein scaffold. Starting from the structure of the CaM-smMLCK complex, the program considered 10(22) amino acid residue sequences to obtain the lowest-energy CaM sequence. The resulting eightfold mutant, CaM_8, was constructed and tested for binding to a set of seven CaM target peptides. CaM_8 displayed high binding affinity to the smMLCK peptide (1.3nM), similar to that of the wild-type protein (1.8nM). The affinity of CaM_8 to six other target peptides was reduced, as intended, by 1.5-fold to 86-fold. Hence, CaM_8 exhibited increased binding specificity, preferring the smMLCK peptide to the other targets. Studies of this type may increase our understanding of the origins of binding specificity in protein-ligand complexes and may provide valuable information that can be used in the design of novel protein receptors and/or ligands.

Amino Acid Sequence↗
PubMed2002

A designed apoplastocyanin variant that shows reversible folding.

Plastocyanin, like many other metalloproteins, does not undergo reversible folding, which is thought to be due to an irreversible conformational change in the copper-binding site. Moreover, apoplastocyanin's ability to adopt a native tertiary structure is highly salt-dependent, and even in high salt, it has an irreversible thermal denaturation. Here, we report a designed apoplastocyanin variant, PCV, that is well folded and has reversible folding in both high and low salt conditions. This variant provides a tractable model for understanding and designing protein beta-sheets.

Apoproteins↗
PubMed2002

A designed phenylalanyl-tRNA synthetase variant allows efficient in vivo incorporation of aryl ketone functionality into proteins.

Incorporation of non-natural amino acids into proteins in vivo expands the scope of protein synthesis and design. p-Acetylphenylalanine was incorporated into recombinant dihydrofolate reductase (DHFR) in Escherichia coli via a computationally designed mutant form of the phenylalanyl-tRNA synthetase of the host. DHFR outfitted with ketone functionality can be chemoselectively ligated with hydrazide reagents under mild conditions.

Escherichia coli↗
PubMed2002

Electrostatics significantly affect the stability of designed homeodomain variants.

The role of electrostatic interactions in determining the stability of designed proteins was studied by constructing and analyzing a set of designed variants of the Drosophila engrailed homeodomain. Computational redesign of 29 surface positions results in a 25-fold mutant with moderate stability, similar to the wild-type protein. Incorporating helix dipole and N-capping considerations into the design algorithm by restricting amino acid composition at the helix termini and N-capping positions yields a ninefold mutant of the initial design (a 23-fold mutant of wild-type) that is over 3 kcal mol(-1) more stable than the protein resulting from the unbiased design. Four additional proteins were constructed and analyzed to isolate the effects of helix dipole and N-capping interactions in each helix. Based on the results of urea-denaturation experiments and calculations using the finite difference Poisson-Boltzmann method, both classes of interaction are found to increase the stability of the designed proteins significantly. The simple electrostatic model used in the optimization of rotamers by iterative techniques (ORBIT) force-field, which is similar to the electrostatic models used in other protein design force-fields, is unable to predict the experimentally determined stabilities of the designed variants. The helix dipole and N-capping restrictions provide a simple but effective method to incorporate two types of electrostatic interactions that impact protein stability significantly.

Algorithms↗
PubMed2002

De novo design of biocatalysts.

The challenging field of de novo enzyme design is beginning to produce exciting results. The application of powerful computational methods to functional protein design has recently succeeded at engineering target activities. In addition, efforts in directed evolution continue to expand the transformations that can be accomplished by existing enzymes. The engineering of completely novel catalytic activity requires traversing inactive sequence space in a fitness landscape, a feat that is better suited to computational design. Optimizing activity, which can include subtle alterations in backbone conformation and protein motion, is better suited to directed evolution, which is highly effective at scaling fitness landscapes towards maxima. Improved rational design efforts coupled with directed evolution should dramatically improve the scope of de novo enzyme design.

Antibodies, Catalytic↗
PubMed2002

Protein building blocks preserved by recombination.

Borrowing concepts from the schema theory of genetic algorithms, we have developed a computational algorithm to identify the fragments of proteins, or schemas, that can be recombined without disturbing the integrity of the three-dimensional structure. When recombination leaves these schemas undisturbed, the hybrid proteins are more likely to be folded and functional. Crossovers found by screening libraries of several randomly shuffled proteins for functional hybrids strongly correlate with those predicted by this approach. Experimental results from the construction of hybrids of two beta-lactamases that share 40% amino acid identity demonstrate a threshold in the amount of schema disruption that the hybrid protein can tolerate. To the extent that introns function to promote recombination within proteins, natural selection would serve to bias their locations to schema boundaries.

Algorithms↗