AI-enhanced protein design makes proteins that have never existed
On 26 January, Profluent came out of stealth mode with $9 million in seed funding to support the company’s efforts to apply machine learning (ML) to engineer novel functional proteins. This is just the latest in a steady flurry of investment in this space. Last January, Generate Biomedicines signed a $50 million drug development deal with Amgen that could potentially net the company more than $1.9 billion in total, and a few months later, Arzeda drew $33 million in series B funding to support its ongoing protein design programs. Other startups are also starting to crowd the field, such as computational company Cradle, which exited stealth in November with a $5.5 million seed investment, and Monod Bio, which launched with $25 million in seed funding in August. AI-based algorithms can guide the design of proteins exhibiting many different kinds of symmetry, from simple spherical forms to complex icosahedral designs.
This ML toolbox could generate made-to-order proteins too, including those with functions not present in nature. This is an appealing prospect because, despite natural proteins’ vast molecular diversity, there are many biomedical and industrial problems that evolution has never been compelled to solve. Scientists are now rapidly moving toward a future in which they can apply careful computational analysis to infer the underlying principles governing the structure and function of real-world proteins and apply them to construct bespoke proteins with functions devised by the user. Lucas Nivon, CEO and cofounder of Cyrus Biotechnology, believes the ultimate impact of such in silico-designed proteins will be massive and compares the field to the fledgling biotech industry of the 1980s. “I think in 30 years 30, 40 or 50% of drugs will be computationally designed proteins,” he says.
To date, companies operating in the protein design space have largely focused on retooling existing proteins to perform new tasks or enhance specific properties, rather than true design from scratch. For example, scientists at Generate Biomedicines have drawn on existing knowledge about the SARS-CoV-2 spike protein and its interactions with the receptor protein ACE2 to design a synthetic protein that can consistently block viral entry across diverse variants. “In our internal testing, this molecule is quite resistant to all of the variants that we’ve seen thus far,” says cofounder and CTO Gevorg Grigoryan, adding that Generate aims to file Investigational New Drug paperwork to clear the way for clinical testing in the second quarter of this year. More ambitious programs are on the horizon, although it remains to be seen how soon the leap to de novo design — in which new proteins are built entirely from scratch — will come.
The field of AI-assisted protein design is blossoming, but the roots of the field stretch back more than two decades, with work by academic researchers like David Baker and colleagues at what is now the Institute for Protein Design at the University of Washington. Starting in the late 1990s, Baker — who has co-founded companies in this space including Cyrus, Monod and Arzeda — oversaw the development of Rosetta, a foundational software suite for predicting and manipulating protein structures. Since then, Baker and other researchers have developed many other powerful tools for protein design, powered by rapid progress in ML algorithms — and particularly, by progress in a subset of ML techniques known as deep learning. This past September, for example, Baker’s team published their deep learning ProteinMPNN platform, which allows them to input the structure they want and have the algorithm spit out an amino acid sequence likely to produce that de novo backbone structure, achieving a >50% success rate.
Some of the greatest excitement in the deep learning world relates to generative models that can create entirely new proteins, never seen before in nature.
