Gene and Protein Sequence Optimization for High-Level Production of Fully Active and Aglycosylated Lysostaphin in Pichia Pastoris

Lysostaphin represents a promising therapeutic agent for the treatment of staphylococcal infections, in particular those of methicillin-resistant Staphylococcus aureus (MRSA). However, conventional expression systems for the enzyme suffer from various limitations, and there remains a need for an efficient and cost-effective production process to facilitate clinical translation and the development of nonmedical applications. While Pichia pastoris is widely used for high-level production of recombinant proteins, there are two major barriers to the production of lysostaphin in this industrially relevant host: lack of expression from the wild-type lysostaphin gene and aberrant glycosylation of the wild-type protein sequence. The first barrier can be overcome with a synthetic gene incorporating improved codon usage and balanced A+T/G+C content, and the second barrier can be overcome by disrupting an N-linked glycosylation sequon using a broadened choice of mutations that yield aglyscosylated and fully active lysostaphin. The optimized lysostaphin variants could be produced at approximately 500 mg/liter in a small-scale bioreactor, and 50% of that material could be recovered at high purity with a simple 2-step purification. It is anticipated that this novel high-level expression system will bring down one of the major barriers to future development of biomedical, veterinary, and research applications of lysostaphin and its engineered variants

Mapping the Pareto Optimal Design Space for a Functionally Deimmunized Biotherapeutic Candidate

The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the undominated variants for which no other single design exhibits better performance in both criteria. Here, the Pareto frontier of a therapeutic enzyme has been designed, constructed, and evaluated experimentally. Various measures of protein performance were found to map a functional sequence space that correlated well with computational predictions. These results represent the first systematic and rigorous assessment of the functional penalty that must be paid for pursuing progressively more deimmunized biotherapeutic candidates. Given this capacity to rapidly assess and design for tradeoffs between protein immunogenicity and functionality, these algorithms may prove useful in augmenting, accelerating, and de-risking experimental deimmunization efforts

Protein loop structure prediction

This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.</p

Protein loop structure prediction

This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The LOESS fitting of the clockwise and counter-clockwise distributions that achieved equilibrium.

<p>The letters correspond to the simulations given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010464#pone-0010464-t002" target="_blank">table 2</a>.</p

An outline of the MESMAX algorithm.

<p>An outline of the MESMAX algorithm.</p

The variation in number of CheY-P molecules for the freely diffusing CheZ model.

<p>The variation in number of CheY-P molecules for the freely diffusing CheZ model.</p

The <i>E. coli</i> simulation system.

<p>The <i>E. coli</i> simulation system.</p

Statistical closeness between the simulations and the experimental data.

<p>Statistical closeness between the simulations and the experimental data.</p

The Q-Q plot for the freely diffusing CheZ model, comparing the simulated quantiles against an exponential function fitted to the experimental data.

<p>The Q-Q plot for the freely diffusing CheZ model, comparing the simulated quantiles against an exponential function fitted to the experimental data.</p