Google DeepMind's AlphaFold has revolutionized the protein folding prediction, addressing a long-standing challenge in biology

In 2020, the scientific community galvanized in response to the recent COVID-19 outbreak, building on decades of basic research characterizing this virus family. AlphaFold, a protein structure prediction model from Google’s DeepMind, released structured predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19. This was pivotal in helping with the effort of research to develop defenses against the SARS-CoV-2 spike protein.

So what problems does protein structure determination solve for and where are the future opportunities for innovation in the space?

What does protien structure determination unlock?

Proteins underpin every biological process in every living thing.

Solving for a protein structure refers to the process of determining the three-dimensional (3D) arrangement of atoms in a protein. Proteins are complex molecules made up of chains of amino acids that fold into specific shapes to perform their biological functions. Understanding a protein's structure is crucial because its function is directly related to its shape. Solving for a protein structure has the ability to enable us to:

• Understanding Function: A protein's structure determines how it interacts with other molecules and performs its role in the body.

• Drug Design: Structural knowledge allows researchers to design drugs that specifically bind to a protein's active site or modulate its function.

• Disease Research: Misfolded or mutated proteins are often linked to diseases, so studying their structures can help identify therapeutic strategies.

What innovations are on the horizon thanks to Alphafold?:

AlphaFold structures most recently have been determined to have a median accuracy of 0.96 Å r.m.s.d. This means the average distance between corresponding backbone atoms in AlphaFold’s predicted structures and the true structures was less than 1 angstrom. This is an extremely small deviation, indicating near-atomic accuracy.

An article out of Drug Discovery Trends highlighted a few of the following applications of Alphafold on the horizon:

1. Combating antibiotic resistance

In 2019, the CDC estimated that antimicrobial resistance costs the U.S. economy $55 billion annually, including $20 billion in healthcare costs and another $35 billion in lost productivity. Antibiotic resistance is also fueling the emergence of ‘superbugs.’

Professors Marcelo Sousa and Megan Mitchell from the University of Colorado, Boulder, are using AlphaFold to study proteins involved in antibiotic resistance. AlphaFold has helped the researchers identify protein structures they could confirm with crystallography. For example, Sousa used this technology to identify a bacterial protein structure in approximately 30 minutes that had proven hard to identify for 10 years.

2. Developing a novel malaria vaccine

The University of Oxford and the National Institute of Allergy and Infectious Diseases (NIAID) are collaborating on developing a multi-component malaria vaccine. While WHO endorsed the first malaria vaccine last year, its efficacy in curbing transmission could be limited. Vaccine immunogens that prevent transmission of the parasite-carried malaria could block transmission of the virus. Researchers at Oxford and NIAID identified the first full-length structure of Pfs48/45 with crystallography and AlphaFold. The research could pave the way for the development of future Pfs48/45-based vaccine immunogens.

3. Shedding light on Parkinson’s

An international research team has explored the use of AlphaFold to model the structure of stress-inducible phosphoprotein 1 (STIP1), exploring its role as a neuroprotective factor guarding against Parkinson’s disease. Currently, therapies that treat Parkinson’s tend to focus on symptomatic relief. No neuroprotective agents are approved to slow the rate of neurodegeneration associated with the disease. Researchers involved in the project were affiliated with Duke-NUS Medical School, Singapore General Hospital, A*STAR, Tan Tock Seng Hospital, National University of Singapore and Southern Medical University Guangdong.

Opportunities for Innovation

This technology is helping researchers understand what individual proteins do and how they interact with other molecules. So valuable time and resources can be redirected into advancing research that could help solve society’s biggest medical and environmental challenges.

The Drugs for Neglected Diseases Initiative (DNDi) is using AlphaFold to create new medicines for neglected diseases that disproportionately affect those in developing countries. Specifically, the protein-folding database could accelerate the identification of molecules to treat neglected diseases. DNDi is also working to enable researchers in low-income countries to play a more active role in drug discovery.

Examples of such diseases include:

• Chagas disease, which is spread by the parasite Trypanosoma cruzi. Chagas disease is common in Mexico, Central America, and South America.

• Another example is leishmaniasis, a parasitic disease found in tropical and subtropical regions as well as southern Europe.

As cited in the science journal Nature - there are still substantial challenges to achieve highly accurate predictions across all interaction types, but it has been demonstrated that it is possible to build a deep-learning system that shows strong coverage and generalization for all of these interactions. There is an opportunity for the introduction of new processes and compounds to scale the development of these medicines using this technology.