At Watchmaker Genomics, everything starts and ends with applications, in an iterative process of insight and innovation.

The Watchmaker platform draws upon five decades of spectacular achievements in molecular biology and sequencing, laboratory automation and miniaturization, and bioinformatics and computational analysis. Recent advances in all of these fields have enabled exponential improvements in the breadth, depth, and throughput of scientific interrogation, bringing previously intractable challenges within the realms of possibility. However, existing enzymes and reagents are not keeping pace with the evolving performance requirements of high-stringency clinical applications enabled by next-generation sequencing technologies. Reagent optimization enables incremental improvements, but cannot address the intrinsic efficiencies and shortcomings of enzymes used outside of their biological contexts.

Our extensive experience with the distinct challenges in inherited disease, somatic oncology, transcriptomics, and epigenomics allows us to purpose-design enzymes and workflows to support emerging applications in precision medicine, genomics, and synthetic biology. We have established an innovative, computationally driven, and vertically integrated protein engineering and production platform to create best-in-class, tailor-made solutions for the reading, writing and editing of DNA and RNA.

Watchmaker’s team of computational biologists are experienced in clinical NGS data analysis, statistics, statistical learning methods, and molecular dynamics and simulation. We have constructed a stable and rigorous framework for rapidly developing, testing, and deploying new data analysis workflows, using public-domain as well as proprietary, custom-built tools.

Through sophisticated, high-resolution, NGS-based readouts, we are able to unravel underlying molecular mechanisms and harness multidimensional Design of Experiments (DOE) data to predict the behavior of enzymes across a defined chemistry spectrum. These deep learnings that inform our protein engineering strategies are also applied further downstream in the development of application-appropriate product QC assays.

Our platform combines directed evolution with in silico rational design, and leverages massively parallel sequencing to enable a deep exploration of sequence-function landscapes for a broad range of enzymes. New microfluidic formats allow for precise control and manipulation of microreactors, thereby increasing the sophistication of functional selection assays. Innovations and cost-reductions in synthetic biology have enabled us to address traditional limitations associated with variant library diversity. Additionally, deep sequencing and machine learning allow us to cover vastly more sequence space, increasing the probability of identifying variants with specific and improved performance characteristics.

High stringency enzyme manufacturing is critical to the success of our overall enzyme engineering platform. Our expertise in this space enables the generation of high purity, small scale prototypes, ensuring that subsequent assays measure true enzyme attributes rather than byproducts of manufacturing and purification. This detailed characterization directly feeds into our iterative process of insight and innovation.

Deep domain knowledge facilitates agile scale-up from prototype to large volume production of purpose-built enzymes, while maintaining high purity and consistent quality across lots. In addition, we continually strive to enhance manufacturability, including collaboratively working to incorporate predictive in silico design to increase enzyme solubility, aggregation resistance, and stability.