組換えタンパク質に対する需要の高まりにより、新たなエンジニアリング戦略の模索やタンパク質発現宿主細胞株の拡大が進んでいます。ただし、この試みは、気の弱い人には向いていません。開発プロセスでは、目的の遺伝子またはタンパク質の検証や配列解析、コドンの最適化、ベクターの構築、クローン/宿主の選択など、多くの変数を考慮する必要があります。このような問題が発生した場合、タンパク質発現科学者は、DNAまたはアミノ酸配列を変更する、コドンを別のコドンに再定義する、あるベクターの遺伝子を別のベクターに移動する、ベクターを別の宿主にトランスフェクトする、クローンを再選択する、発現タンパク質を再特性評価するなど、新しいクローニングスキームを設計しなければならず、手間と時間、コストのかかるプロセスとなります。Cambridge Healthtech Instituteの第6回年次会議「細胞株とシステム工学」では、機能的なタンパク質製品につながる、組換えタンパク質の発現と生産に関する効果的なエンジニアリング戦略を特集します。経験豊富で精通した研究者による、実際の経験・応用・結果の共有から学ぶことができます。
Recommended Short Course*
Monday, 13 November, 14:00 - 17:00
SC4: The Use and Optimization of Eukaryotic Expression Systems to Support Therapeutic Generation and Structural Biology
*Separate registration required. See short courses page for details. All short courses take place in-person only.
Registration Open and Morning Coffee07:30
APPLYING DATA SCIENCE TO ENHANCE PROTEIN EXPRESSION
FEATURED PRESENTATION: Accuracy and Data Efficiency in Deep Learning Models of Protein Expression
Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for strain optimization. But such models need large and costly training data that create steep entry barriers for many laboratories. Here we study the relation between accuracy and data efficiency in an atlas of machine learning models trained on datasets of varied size and sequence diversity. Our results provide guidelines for balancing cost and quality of training data, thus helping promote the wider adoption of deep learning in strain engineering.
Codon Language Models for Protein Engineering
Protein representations from language models have yielded state-of-the-art performance across many tasks in computational protein engineering. In recent years, progress has focused on parameter count, with recent models' capacities surpassing the size of the very datasets they were trained on. Here, we propose an alternative direction. We show that large language models trained on codons, instead of amino acid sequences, provide high-quality representations that outperform comparable state-of-the-art models across. These results suggest that, in addition to commonly studied scale and complexity, the information content of biological data provides an orthogonal direction to improve the power of machine learning in biology.
Gene Expression Prediction from DNA Sequence with Enformer
How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications depend on improved solutions. To advance this, we developed Enformer, a hybrid convolutional and transformer model trained to predict thousands of epigenomic experiments including gene expression from human and mouse reference genomes. I will discuss Enformer’s performance on variant effect prediction which may be relevant for regulatory DNA sequence design.
Grand Opening Coffee Break in the Exhibit Hall with Poster Viewing10:30
Using Machine Learning to Predict Protein Expression
We have developed and implemented a machine learning model to predict protein expression. The model was coupled to an in silico screening procedure that systematically designs and assesses thousands of constructs in a high-throughput manner. We will share our plans to improve the model by (1) streamlining internal data registration, (2) considering yield values instead of classes, (3) incorporating protein sequence embeddings based on AI language models, and (4) leveraging external datasets. Limited availability of training data is a key blocker, so we are exploring sharing data via a pre-competitive consortium in collaboration with EMBL-EBI and other academic and industry partners.
KEYNOTE PRESENTATION: Approaches to High-Throughput Expression and Machine Learning at GSK
Rapid generation of quality reagents is essential for successful drug discovery. Minimizing reagent design-make-test cycles decreases cost and increases probability of success. At GSK we have developed high-throughput mammalian and E. coli expression systems. We are using our high-throughput expression pipelines to screen constructs for optimal expression to generate protein and cellular reagents. We are using machine learning and design approaches to inform construct design and increase the success of our reagents for a range of applications. This presentation will introduce our high-throughput expression systems, our approaches to organizing our data and our design approaches.
Engineered cells are the workhorses of many labs, from protein engineering to therapeutic cell line development. However, researchers are delayed by significant bottlenecks in building critical sequences necessary to introduce genetic material into cells in a fast, effective, and scalable way.
Join us to learn how automated workflow solutions can accelerate cell engineering by enabling synthesis and analysis of DNA/mRNA designs on your timeline-when and where you need them most.
ENGINEERING AND DEVELOPING HOST CELL LINES
Directed Evolution of Bovine Enterokinase from Inclusion Body to Soluble Protein Expression
Bovine enterokinase light chain is used for affinity-tag removal. Expression in E. coli leads to insoluble inclusion bodies. Directed evolution yielded 321 unique variants, with up to >11,000-fold increased soluble expression, mainly due to stability. Codon optimisation improved expression at 37°C. However, non-optimised codons and expression at 30°C gave the highest activities. Partial least squares analysis revealed that soluble variants tended to combine stabilising mutations outside the active site.
A Next-Generation pET System for Bacterial Protein Production
The pET plasmids constitute the most popular protein production system. Using synthetic experimental evolution, we have improved the performance of several of the pET genetic modules and bacterial strains. In addition, we have developed an extremely simple method for making recombinant DNA. The approach and the genetic modules will be combined into a next-generation pET system.
Novel Strategies for Protein Production Using Pichia pastoris
Recombinant protein production allows us to create smart materials and catalysts. Our mission is to find solutions to produce proteins, enzymes and metabolites using the yeast Pichia pastoris, which is an efficient alternative for recombinant production combining the simplicity of bacterial expression systems with some essential features of higher eukaryotic hosts. We can build on over 15 years of experience in toolbox development and gene expression using Pichia pastoris to overcome hurdles in recombinant production. We adopt available technology to our needs and evaluate innovative new strategies for the expression of our proteins and enzymes.
This presentation explores microalgae as a sustainable source of food ingredients, including natural pigments, omega-3, and alternative proteins. High throughput automation is needed due to limited color range and sensitivity to environmental conditions. Molecular Devices provides innovative technologies to support customers when facing these challenges.
The development process for cell lines is complex and laborious, with increasing expectations for supporting in-process data. We will show how microfluidic-enabled picodroplets deliver integrated, user-friendly, automated workflows where millions of individual cells are assessed daily, and the best single cells selected - in an environment that maintains high cell viability and outgrowth. We will introduce Cyto-Mine®, a platform that enables a step-change in speed and scale of working.
Refreshment Break in the Exhibit Hall with Poster Viewing16:10
Genome-Wide Virus-Free CRISPR Screening Platform for Identifying Novel Engineering Targets in Mammalian Cells
Mammalian cells are the preferred host cells for therapeutic protein production and have been engineered to contain desired attributes for increased protein production. To identify novel engineering targets, laborious and time-consuming empirical approaches have been attempted. Here, I present a genome-wide CRISPR-Cas9 screening platform for CHO and HEK293 cells using a virus-free, recombinase-mediated, cassette exchange-based gRNA integration method to identify novel targets for high productivity and culture-stress resistance.
The Potential of Emerging Sub-Omics Technologies for CHO Cell Engineering
In recombinant protein production with CHO cells, bottlenecks in productivity or product purity issues require a particular cellular or clonal mechanism to be analyzed. Emerging analytical techniques allow ever more detailed insights into cellular processes involved in protein expression or cultivation performance. Thus, we performed targeted studies on CHO sub-OMICs, including the miRNome, cell surfaceome, as well as secreted HCPs and extracellular vesicles, to address specific issues of biopharmaceutical production.
Getting the Most Out of Your Cells: Refining the Process for Higher Protein Yields
In the last years, we have been dealing with the production of different recombinant proteins in human cells, by means of PEI-based transfection and further transient gene expression. We have been constantly pursuing the improvement of the process, in terms of higher expression levels. We have explored different parameters and conditions (some of them already published for other recombinant proteins), and we have implemented in our process those changes that improved protein yield. In our talk, we will detail such continuous process, and show where we started, and where we are now.
Welcome Reception in the Exhibit Hall with Poster Viewing18:30
Close of Cell Line and Systems Engineering Conference19:30
- Antibody-Based Therapies
- Emerging Targets & Approaches
- Membrane Protein Targets
- Safety & Efficacy of Bispecifics
- Advancing Bispecifics
- Engineering Bispecifics
- Optimisation & Developability
- Analytical Characterisation
- Protein Stability & Formulation