Stabilizing Disulfide Bridges
Elsewhere at the PEGS event, Alan Dombkowski, Ph.D., assistant professor of clinical pharmacology and toxicology at Wayne State University School of Medicine, discussed techniques for enhancing the thermal stability of proteins through disulfide bond engineering.
He has developed a software package, Software By Design (DbD), that facilitates the rational design of disulfide bonds in proteins.
Nature uses disulfide bonds to stabilize proteins, particularly small secreted proteins that lack a stabilizing hydrophobic core. DbD locates amino acids that are candidates for site-directed mutagenesis that transforms these locations to cysteines, which are then primed for disulfide bond formation. Both intramolecular and intermolecular disulfides are possible, but the latter are more difficult to achieve.
Dr. Dombkowski noted a recent publication in which investigators enhanced the thermal stability of lipase B, which already has a disulfide, by introducing a second sulfur-sulfur linkage. Higher thermal stability provided a more robust process, in this case, for manufacturing biodiesel.
“In most industrial processes, the higher temperatures they can run the reactions at, the better,” Dr. Dombkowski observed. A recent patent application, by DbD licensee Novo Nordisk, describes introduction of a disulfide linkage in growth hormone to make the molecule more resistant to proteolytic degradation. In some cases, disulfide formation improves activity as well, but this is where care must be taken. In a third example Dr. Dombkowski relays, an antibody’s thermal properties were significantly enhanced but activity fell. “The potential effects on activity are real,” he said.
DbD begins with a protein’s structure, and suggest locations where amino acid switches to cysteine are likely to produce a disulfide bridge successfully. The locations must be relatively close in space, but the residues must also possess the correct angle and orientation.
DbD was developed by examining naturally occurring disulfides and characterizing their atomic coordinates, orientations, and geometric requirements, and extrapolating from there to the putative target protein protein. “The software is quite good at modeling these systems, and predicting if the disulfide bond will form,” Dr. Dombroski remarked.
Meantime, Curtis Knox, marketing director at Lucigen, discussed what could be a game-changer for E. coli as an expression system. Available since May, his firm’s CleanColi™ competent cell E. coli strain uses genetically modified lipopolysaccharide (LPS) that does not cause an endotoxic response in humans. Endotoxin removal has been one barrier to employing E. coli as protein expression systems, and using E. coli-derived proteins in cell-based assays.
Endotoxin-Free E. Coli
“E. coli is commonly used for research-scale protein expression but its ease of use has been limited by unwanted endotoxin contamination. These days are hopefully over,” said David Mead, Ph.D., Lucigen founder and CEO.
CleanColi is available as a research-only product, but Lucigen expects that to change. “We anticipate that manufacturing companies may want to investigate these cells for bioproduction, and we will certainly work with them on that,” said Knox. “We believe CleanColi holds that potential, but it has not yet been scaled to that level.
How can E. coli survive without the naturally occurring lipopolysaccharides that comprise their outer membrane? Lucigen has incorporated genetic deletions that alter the lipopolysaccharide into a different molecule, Lipid IVA, which allows the cells to thrive and express protein, without generating harmful endotoxins.
“Processes that avoid E. coli could now consider that expression system,” Knox said. “Many bioprocessors have shied away from E. coli, even though it’s a very easy system to work with, and easily scaled, but have not due to cost and time and yield loss associated with endotoxin removal.”
Given that organisms have 61 codons at their disposal (plus 3 stop codons), but just 20 amino acids, organisms can “select” among 3 codons for each amino acid. The preference of an expression system for particular distribution of synonymous codons, known as codon selection bias, has no effect on a protein’s primary structure but may affect expression level or yield. Controlling codon bias is one of the technologies offered by DNA2.0 for improving protein expression and yield.
Codon optimization is not strictly about selecting the “best” codon, but the most productive balance or distribution of codons throughout the protein.
“We’re trying to optimize gene performance, meaning protein output,” explained Mark Welch, Ph.D., DNA2.0 director, R&D. “This is a very big deal. Some drugs never leave the development stage because their expression is insufficient for running clinical trials.”
Among these “problem” proteins are difficult-to-express membrane proteins. Optimizing codon selection may improve expression and yield as much as 10-fold. In some situations even a small percentage increase may be worth the effort involved in redesigning a gene.
Most of the impact of codon usage optimization occurs at the RNA level, in particular in the sequences, the triplets employed to code for individual amino acids. Some effects may result from the RNA’s local structure. For example, how RNA folds may affect whether ribosomes can locate the message and translate it into protein. Another factor is stability.
“Many natural RNAs have programmed lifespans,” Dr. Welch said. “In nature, some RNAs are present at high levels for a very short time, and it is possible to build in degradation sites that limit its lifespan.” But for production systems high levels of highly stable RNA are preferred.
“You’d want to avoid sites that lead to high degradation,” he added. Other regulatory motifs that come into play include processing sites. “We consider many things in the design, which may be affected when we re-code genes,” Dr. Welch said.