The CRISPR-Cas gene scissors offer a wide range of potential applications, from the treatment of genetic diseases to antiviral therapies and diagnostics. However, to safely harness their powers, scientists are searching for mechanisms that can regulate or inhibit the systems’ activity. Enter the anti-CRISPR protein AcrVIB1, a promising inhibitor whose exact function has remained a mystery—until now.

A research team from the Helmholtz Institute for RNA-based Infection Research (HIRI) in Würzburg, in collaboration with the Helmholtz Centre for Infection Research (HZI) in Braunschweig, has uncovered the precise way AcrVIB1 works that expands the known means by which Acrs can shut down CRISPR. The results are published in the journal Molecular Cell.

Bacteria and their viruses, known as phages, are locked in an age-old arms race. To defend against phage attacks, bacteria have evolved sophisticated mechanisms to recognize and counteract invading viruses. In turn, phages have developed innovative strategies to evade these defenses. A prime example of this ongoing battle is the CRISPR-Cas defense system in bacteria, countered by anti-CRISPR proteins (Acrs) in phages, which specifically block these bacterial “gene scissors.”

Apart from their counter-defensive function, anti-CRISPR proteins hold great promise for enabling more precise control over CRISPR technologies. The research team has now further elucidated the function of an important yet so far uncharacterized anti-CRISPR protein.

“In a previous study, we used a deep learning algorithm to predict new Acrs. This led to the identification of AcrVIB1, the first anti-CRISPR protein targeting the Cas13b nuclease,” says HIRI department head Prof. Chase Beisel, who led the study together with the department of Prof. Wulf Blankenfeldt at HZI.

“The nuclease Cas13b can recognize and cut RNA. It is currently used to silence genes, whether to study their function, clear viruses, or counteract genetic diseases linked to the gene.” However, how the protein AcrVIB1 inhibits Cas13b remained unknown until now. In their study, the research team presents this entirely new blocking mechanism.

An RNA dead end

The Cas13b nuclease operates by interacting with a CRISPR ribonucleic acid (crRNA), which serves as a guide to identify and bind to complementary RNA sequences, for example those from phages. Once the target RNA is bound, Cas13b can cleave and degrade not only these complementary RNA molecules but also all other RNAs in the vicinity.

While most known anti-CRISPR proteins block steps along this path such as crRNA binding or target recognition, AcrVIB1 adopts a radically different strategy: Rather than blocking the binding of the crRNA to Cas13b, AcrVIB1 even improves it. The formed pair is dysfunctional though, meaning that the enzyme cannot begin degrading RNAs even when its target is present. Furthermore, the bound crRNA becomes vulnerable to attack by cellular ribonucleases, which break down RNA molecules.

“The tighter binding between nuclease and guide RNA was entirely unexpected. The simpler and therefore initially expected mechanism would have been to just prevent the guide RNA from binding in the first place,” says first author Dr. Katharina Wandera, who completed her doctorate in Chase Beisel’s laboratory.

“Nevertheless, the path taken by AcrVIB1 appears to be more effective: AcrVIB1 binds tightly to and thereby renders Cas13b inactive. At the same time, it increases the turnover of guide RNAs, making Cas13b a dead end for crRNAs.”

Chase Beisel’s team at HIRI and the laboratory of Wulf Blankenfeldt at HZI have joined forces to decipher the structure of the inhibition mechanism more precisely. Using cryo-electron microscopy, Blankenfeldt’s group showed that AcrVIB1 binds to Cas13b, leaving the crRNA-binding domain untethered.

“Our finding provides a blueprint for the development of molecules that could mimic or modify the function of the anti-CRISPR protein,” says Blankenfeldt. These are the first data from the HZI’s new cryo-electron microscopy facility to be published.

Enzymes are molecular machines that carry out the chemical reactions that sustain all life, an ability that has captured the attention of scientists like me.

Consider muscle movement. Your body releases a molecule called acetylcholine to trigger your muscle cells to contract. If acetylcholine sticks around for too long, it can paralyze your muscles—including your heart muscle cells—and, well, that’s that. This is where the enzyme acetylcholinesterase comes in. This enzyme can break down thousands of acetylcholine molecules per second to ensure muscle contraction is stopped, paralysis avoided and life continued. Without this enzyme, it would take a month for a molecule of acetylcholine to break down on its own—about 10 billion times slower.

You can imagine why enzymes are of particular interest to scientists looking to solve modern problems. What if there were a way to break down plastic, capture carbon dioxide or destroy cancer cells as fast as acetylcholinesterase breaks down acetylcholine? If the world needs to take action quickly, enzymes are a compelling candidate for the job—if only researchers could design them to handle those challenges on demand.

Designing enzymes, unfortunately, is very hard. It’s like working with an atom-sized Lego set, but the instructions were lost and the thing won’t hold together unless it’s assembled perfectly. Newly published research from our team suggests that machine learning can act as the architect on this Lego set, helping scientists build these complex molecular structures accurately.

What’s an enzyme?

Let’s take a closer look at what makes up an enzyme.

Enzymes are proteins—large molecules that do the behind-the-scenes work that keep all living things alive. These proteins are made up of amino acids, a set of building blocks that can be stitched together to form long strings that get knotted up into specific shapes.

The specific structure of a protein is key to its function in the same way that the shapes of everyday objects are. For example, much like a spoon is designed to hold liquid in a way that a knife simply can’t, the enzymes involved in moving your muscles aren’t well suited for photosynthesis in plants.

For an enzyme to function, it adopts a shape that perfectly matches the molecule it processes, much like a lock matches a key. The unique grooves in the enzyme—the lock—that interact with the target molecule—the key—are found in a region of the enzyme known as the active site.

The active site of the enzyme precisely orients amino acids to interact with the target molecule when it enters. This makes it easier for the molecule to undergo a chemical reaction to turn into a different one, making the process go faster. After the chemical reaction is done, the new molecule is released and the enzyme is ready to process another.

How do you design an enzyme?

Scientists have spent decades trying to design their own enzymes to make new molecules, materials or therapeutics. But making enzymes that look like and go as fast as those found in nature is incredibly difficult.

Enzymes have complex, irregular shapes that are made up of hundreds of amino acids. Each of these building blocks needs to be placed perfectly or else the enzyme will slow down or completely shut off. The difference between a speed racer and slowpoke enzyme can be a distance of less than the width of a single atom.

Initially, scientists focused on modifying the amino acid sequences of existing enzymes to improve their speed or stability. Early successes with this approach primarily improved the stability of enzymes, enabling them to catalyze chemical reactions at a higher range of temperatures. But this approach was less useful for improving the speed of enzymes. To this day, designing new enzymes by modifying individual amino acids is generally not an effective way to improve natural enzymes.

Researchers found that using a process called directed evolution, in which the amino acid sequence of an enzyme is randomly changed until it can perform a desired function, proved much more fruitful. For example, studies have shown that directed evolution can improve chemical reaction speed, thermostability, and even generate enzymes with properties that aren’t seen in nature. However, this approach is typically labor-intensive: You have to screen many mutants to find one that does what you want. In some cases, if there’s no good enzyme to start from, this method can fail to work at all.

Both of these approaches are limited by their reliance on natural enzymes. That is, restricting your design to the shapes of natural proteins likely limits the kinds of chemistry that enzymes can facilitate. Remember, you can’t eat soup with a knife.

Is it possible to make enzymes from scratch, rather than modify nature’s recipe? Yes, with computers.

Designing enzymes with computers

The first attempts to computationally design enzymes still largely relied on natural enzymes as a starting point, focusing on placing enzyme active sites into natural proteins.

This approach is akin to trying to find a suit at a thrift store: It is unlikely you will find a perfect fit because the geometry of an enzyme’s active site (your body in this analogy) is highly specific, so a random protein with a rigidly fixed structure (a suit with random measurements) is unlikely to perfectly accommodate it. The resulting enzymes from these efforts performed much more slowly than those found in nature, requiring further optimization with directed evolution to reach speeds common among natural enzymes.

Recent advances in deep learning have dramatically changed the landscape of designing enzymes with computers. Enzymes can now be generated in much the same way that AI models such as ChatGPT and DALL-E generate text or images, and you don’t need to use native protein structures to support your active site.

Our team showed that when we prompt an AI model, called RFdiffusion, with the structure and amino acid sequence of an active site, it can generate the rest of the enzyme structure that would perfectly support it. This is equivalent to prompting ChatGPT to write an entire short story based on a prompt that only says to include the line “And sadly, the eggs never showed up.”

We used this AI model specifically to generate enzymes called serine hydrolases, a group of proteins that have potential applications in medicine and plastic recycling. After designing the enzymes, we mixed them with their intended molecular target to see whether they could catalyze its breakdown. Encouragingly, many of the designs we tested were able to break down the molecule, and better than previously designed enzymes for the same reaction.

To see how accurate our computational designs were, we used a method called X-ray crystallography to determine the shapes of these enzymes. We found that many of them were a nearly perfect match to what we digitally designed.

Our findings mark a key advance in enzyme design, highlighting how AI can help scientists start to tackle complex problems. Machine learning tools could help more researchers access enzyme design and tap into the full potential of enzymes to solve modern-day problems.

A team of microbiologists, chemists and pharmaceutical specialists at Shandong University, Guangzhou Medical University, Second Military Medical University and Qingdao University, all in China, has developed an AI model that generates antimicrobial peptide structures for screening against treatment-resistant microbes.

In their study published in the journal Science Advances, the group developed a compression method to reduce the number of elements needed in training data for an AI system, which helped to reduce diversification issues with current AI models.

Prior research has suggested that drug-resistant microbes are one of the most pressing problems in medical science. Researchers around the world have been looking for new ways to treat people infected with such microbes—one approach involves developing antimicrobial peptides, which work by targeting bacterial membranes.

Unfortunately, developing or finding peptides has proven to be too slow to address the crisis. So researchers have turned to AI-based approaches to aid in finding such peptides. But that approach has encountered problems, as well, the biggest being the lack of a large training base, which leads to peptide discovery that lacks diversity.

In this new study, the researchers in China found a way around this problem by developing a compression technique that reduces the number of elements needed to train their AI system.

The researchers call their system a two-stage AI pipeline leverage diffusion model. The first stage works by compressing data describing 2.8 million known peptides into a numerical form by amplifying signal noise randomly. The second stage then pulls new peptides from the simplified data, removes the noise, and decompresses the data used to describe its peptide sequence.

In testing their new system, the research team found that it was able to filter peptides listed in a training database down to a reasonable number of those most likely to have antimicrobial properties. In looking at 600,000 of them, the team experimentally tested 40 peptides and found 25 that showed promise in combating bacterial and fungal pathogens.