Current Pharmaceutical Biotechnology - Volume 3, Issue 4, 2002

Therapeutic proteins have been engineered for a variety of purposes including reduced antigenicity, longer halflife, simplified process development, and increased affinity. Fusion proteins bring together functions from two different molecules creating therapeutics with completely novel activities. Protein engineering technologies have relied on rational design, directed evolution, DNA shuffling, RNA-peptide fusion, phage and ribosomal display methods to select out candidate protein forms with the desired therapeutic properties. Engineered site-specific pegylation and glycosylation strategies have improved circulation half-life, reduced immunogenicity and increased protein therapeutic stability. In this review we describe how protein engineering techniques have been used to select out, improve stability and clinical efficacy of protein therapeutics.

This review considers chemical and genetic approaches to the modification of protein structure. The historical interest in chemical and site-directed modifications will be briefly covered. Current chemical modification strategies will be presented. Biosynthetic mutagenesis with unnatural aminoacyl-tRNAs and current synthetic peptide ligation technologies will be covered in greater detail. The application of combinatorial genetic methods (e.g. phage display, DNA shuffling) to protein engineering with unnatural amino acids will be briefly discussed, with emphasis on the in vitro evolution of new enzymatic function (i.e. aminoacyl-tRNA synthetases). Throughout the review, the powerful insights gained from the combined use of these technologies will be illustrated by examples that focus on the elucidation of protein-ligand interactions.

Traditionally, the development of drugs has focused on small molecule therapeutics. However, with recent advances in recombinant protein technology the potential of proteins as therapeutics is starting to be realized. Already there are protein drugs on the market, including naturally occurring proteins, engineered proteins and proteins introduced into the patient by way of gene therapy. The next generation of such drugs are likely to be designed molecules, proteins devised from scratch and specifically tailored to have a required 3-dimensional structure and biochemical function. The key to such de novo design is the extensive bioinformatics knowledge that has been obtained from experimentally determined protein structures during the past 40-50 years. The knowledge is far from complete, for example, the protein folding problem has not yet been completely solved. Despite this, bioinformatics plays a crucial role in protein design, as is outlined in this review, and a number of de novo protein structures have been successfully designed in recent years. Some examples of these successes, which are available in the Protein Data Bank, are presented. They suggest that carefully designed protein therapeutics are a genuine prospect for the future.

Protein folding, the problem of how an amino acid sequence folds into a unique three-dimensional shape, has been a long-standing problem in biology. The success of genome-wide sequencing efforts has increased the interest in understanding the protein folding enigma, because realizing the value of the genomic sequences rests on the accuracy with which the encoded gene products are understood. Although a complete understanding of the kinetics and thermodynamics of protein folding has remained elusive, there has been considerable progress in techniques to predict protein structure from amino acid sequences. The prediction techniques fall into three general classes: comparative modeling, threading and ab initio folding. The current state of research in each of these three areas is reviewed here in detail. Efforts to apply each method to proteome-wide analysis are reviewed, and some of the key technical hurdles that remain are presented. Protein folding technologies, while not yet providing a full understanding of the protein folding process, have clearly progressed to the point of being useful in enabling structure-based annotation of genomic sequences.

There is a large and increasing number of therapeutic proteins approved for clinical use and many more undergoing preclinical studies and clinical trials in humans. Most of them are human or “humanized” recombinant molecules. Virtually all therapeutic proteins elicit some level of antibody response, which in some cases, can lead to potentially serious side effects. Therefore, immunogenicity of therapeutic proteins is a concern for clinicians, manufacturers and regulatory agencies. In order to assess immunogenicity of these molecules, appropriate detection, quantitation and characterization of antibody responses are necessary. Immune responses to therapeutic proteins in conventional animal models has not been, except in rare cases, predictive of the response in humans. In recent years there has been a considerable progress in development of computational methods for prediction of epitopes in protein molecules that have the potential to induce an immune response in a recipient. Initial attempts to apply such tools in early development of therapeutic proteins have already been reported. It is expected that computer driven prediction followed by in vitro and / or in vivo testing of any potentially immunogenic epitopes will help in avoiding, or at least minimizing, immune responses to therapeutic proteins.

Efficient development of stable formulations of protein pharmaceuticals requires an intimate knowledge of the protein and its chemical and physical properties. In particular, understanding the mechanisms by which a protein could degrade is critical for designing and testing formulations. This review describes the major pathways by which proteins can degrade, including denaturation, aggregation, oxidation, and interfacial damage. The methods to detect the degradation are covered, along with generalized strategies to retard or prevent each type of decomposition. Without an appreciation of the current best practices for devising stable formulations, the formulation process will be neither efficient nor optimal.