How We Innovate

Supercharging Nature Through Intelligent Protein Design

It’s time to rethink how products are made.

Our Intelligent Protein Design Technology™ combines computational design and AI to create entirely new designer proteins and enzymes more efficiently and with less risk. It’s our way of supercharging what happens all the time in nature. Here’s a look at how we do it.

Intelligent Protein
Design Technology™

Computational Protein Design & AI

We examine more combinations of amino-acids than there are drops of water in the Pacific Ocean to create in-silico proteins and enzymes with specific functional properties to address business and sustainability needs.

We examine countless combinations of amino acids to create in-silico proteins and enzymes with specific functional properties. 

DNA Synthesis

Using our dedicated software and lab robotics, we build the synthetic DNA that encodes the exact protein sequences designed by our algorithms.

We build synthetic DNA that encodes the exact protein sequences designed by our algorithms. 

Precision Lab Testing

By utilizing our precision design software, as opposed to serendipitous protein engineering, we test a targeted number of new proteins. This means we can test each design in application relevant conditions, as opposed to relying on a high-throughput reporter assay that doesn’t correlate with what matters to our customers.

Utilizing our precision design software, we test a targeted number of new proteins in application-relevant conditions. 

Data Storage,
Analysis & Learning

We store all data generated, from expression to functional activity, in our proprietary databases. Using deep learning, we not only use the data to inform the next round of design, but also continuously improve our protein design algorithms to optimize functional properties and efficiencies at scale.

We store all data generated in our proprietary databases and use deep learning to continuously improve our protein design algorithms to optimize function and scale.

Steeped In Science

Our team of scientists and engineers are steeped in industry experience and scientific research with a mission to solve some of today’s hardest challenges through protein innovation.

From developing some of the most advanced deep learning and physics-based methods for protein design, to breakthrough synthetic biology and automation, to scalable, industrial-grade manufacturing process development, our team brings the experience to solve some of the hardest problems the industry – and humanity – is facing.

Our Heritage

And our heritage as a company co-founded by David Baker, PhD and associated with the Institute of Protein Design ensures we have direct access to the premier academic advances in protein design.

Our Passion

Our passion for proteins and AI is only matched by our desire to see the impact of protein innovation across the commercial world and the broader academic community. That’s why, with other leading innovators in protein science, we co-founded OpenFold, a non-profit aimed at developing cutting edge open-source AI software for protein structure prediction and design that is freely available to the community.

Published Research From Our Team

We’re pioneers in enzyme and protein design. Take a look at the peer-reviewed published research, handbooks and scientific papers from our founders, Scientific Advisory Board and team members which continue to inform and advance the scientific community:

An Enumerative Algorithm for de Novo Design of Proteins with Diverse Pocket Structures (2020)

Benjamin Basanta, Matthew J. Bick, Asim K. Bera, Christoffer Norn, Cameron M. Chow, Lauren P. Carter, Inna Goreshnik, Frank Dimaio, David Baker

Efficient Minimization of Multipole Electrostatic Potentials in Torsion Space (2018)

Nicholas K. Bodmer, James J. Havranek

Epitope-Focused Immunogen Design Based on the Ebolavirus Glycoprotein HR2-MPER Region (2022)

Clara T. Schoeder, Pavlo Gilchuk, Amandeep K. Sangha, Kaitlyn V. Ledwitch, Delphine C. Malherbe, Xuan Zhang, Elad Binshtein, Lauren E. Williamson, Cristina E. Martina, Jinhui Dong, Erica Armstrong, Rachel Sutton, Rachel Nargi, Jessica Rodriguez, Natalia Kuzmina, Brooke Fiala, Neil P. King, Alexander Bukreyev, James E. Crowe Jr.

Role of Non-local Interactions between CDR Loops in Binding Affinity of MR78 Antibody to Marburg Virus Glycoprotein (2017)

Amandeep K. Sangha, Jinhui Dong, Lauren Williamson, Takao Hashiguchi, Erica Ollmann Saphire, James E. Crowe Jr., Jens Meiler

De Novo Computational Enzyme Design (2014)

Alexandre Zanghellini

Accurate Design Of Co-Assembling Multi-Component Protein Nanomaterials (2015)

Neil P. King, Jacob B. Bale, William Sheffler, Dan E. McNamara, Shane Gonen, Tamir Gonen, Todd O. Yeates & David Baker

“Computational Protein Design” in Protein Engineering Handbook (2012)

Computational Design of an Enzyme Catalyst for a Stereoselective Bimolecular Dills-Alder Reaction (2010)

Justin B. Siegel, Alexandre Zanghellini, Helena M. Lovick, Gert Kiss, Abigail R. Lambert, Jennifer L. St. Clair, Jasmine L. Gallaher, Donald Hilvert, Michael H. Gelb, Barry L. Stoddard, Kendall N. Houk, Forrest E. Michael, and David Baker

Kemp Elimination Catalysts By Computational Enzyme Design (2008)

Daniela Röthlisberger, Olga Khersonsky, Andrew M. Wollacott, Lin Jiang, Jason DeChancie, Jamie Betker, Jasmine L. Gallaher, Eric A. Althoff, Alexandre Zanghellini, Orly Dym, Shira Albeck, Kendall N. Houk, Dan S. Tawfik & David Baker

De Novo Computational Design of Retro-Aldol Enzymes (2008)

Lin Jiang, Eric A. Althoff, Fernando R. Clemente, Lindsey Doyle, Daniela Röthlisberger, Alexandre Zanghellini, Jasmine L. Gallaher, Jamie L. Betker, Fujie Tanaka, Carlos F. Barbas, Iii, Donald Hilvert, Kendall N. Houk, Barry L. Stoddard, and David Baker

Contact Us

Our goal is to help you reach yours. Get in touch with our team today and reimagine what’s possible with Arzeda:

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    Working hour

    Mon – Fri: 9 a.m. – 5 p.m.
    Sat – Sun: Closed

    650 Birmingham St,
    London 3000, UK

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