Welcome back to Voices of Tomorrow, the podcast where we explore the cutting edge of science, technology, and artificial intelligence.
In recent years, AI has been nothing short of revolutionary, reshaping entire fields of study.
In fact, AI's contributions to science have been so impactful that recently, two Nobel Prizes, one in physics and another in chemistry,
have been awarded to researchers leveraging AI in their discoveries.
We've discussed the profound advancements in AI-enabled protein prediction and design that led to these Nobel wins.
But today, we dive into an area that, until recently, has remained hidden from our view, the RNA virus sphere.
This is the world of RNA viruses, an incredibly diverse and fast-evolving group of viruses that, despite their ubiquity, have remained largely unexplored.
Why? Because traditional methods have struggled to keep pace with their rapid evolution and diversity.
But now, thanks to artificial intelligence, we are witnessing a monumental shift.
AI is not only accelerating the discovery of RNA viruses but also revealing entirely new species and viral ecosystems we never knew existed.
Today's episode will be focused on a groundbreaking study recently published in Cell,
which used AI to document the vast, hidden diversity of RNA viruses in the environment.
To understand the significance of this study, let's first set the stage.
RNA viruses are everywhere. They infect nearly every form of life, plants, animals, fungi, bacteria and even other viruses.
Some RNA viruses are well known to us, like those that cause influenza, HIV or COVID-19,
but the vast majority of RNA viruses remain unknown and uncharacterized.
Scientists refer to this uncharted territory as the RNA dark matter.
So why have these viruses been so elusive? It's because RNA viruses are masters of evolution.
They mutate and evolve much faster than their DNA counterparts, making it difficult for traditional tools to recognize and categorize them.
Moreover, the key protein used to identify RNA viruses, RNA-dependent RNA polymerase, or RDRP,
often evolves so rapidly that it becomes almost unrecognizable to standard sequence-based search tools.
Without a reliable way to detect this polymerase, vast sections of the RNA virus sphere remain undiscovered.
Now, imagine the challenge this presents to researchers.
It's like trying to solve a jigsaw puzzle where many of the pieces are not just missing but constantly changing shape.
This is where artificial intelligence comes into play.
AI offers a way to make sense of this puzzle, even as the pieces shift before our eyes.
Enter LucaProt, the AI-powered tool at the heart of this study, which is revolutionizing the way we explore the RNA virus sphere.
LucaProt is built using deep learning, specifically leveraging the transformer architecture,
the same architecture behind language models like GPT-3 and GPT-4.
But instead of generating text, LucaProt is tasked with finding hidden RNA viruses in massive datasets of metagenomic sequences.
The researchers behind LucaProt, as detailed in the cell paper,
aim to tackle one of the most fundamental problems in viral discovery,
identifying highly divergent RDRP sequences in vast amounts of metagenomic data.
Traditional tools had been limited by their reliance on close-sequence similarity to known viruses.
In contrast, LucaProt doesn't just match sequences to known examples.
It uses AI to predict the structure and function of highly divergent sequences that have no close-known relatives.
LucaProt integrates sequence data with structural predictions from ESMfold,
a powerful AI tool developed by Meta that predicts the three-dimensional structures of proteins.
By combining both the sequence and structural information,
LucaProt is able to detect viruses that traditional models would miss,
even when their RDRP sequences have diverged dramatically from known viruses.
This approach allowed LucaProt to scan through 10,487 metatranscriptomes,
datasets representing genetic material collected from diverse ecosystems around the world.
In total, LucaProt analyzed 51 terabytes of sequencing data,
ultimately identifying a staggering 161,979 putative RNA virus species and 180 RNA virus supergroups.
To put this in perspective, this discovery represents one of the largest expansions of the known RNA virus sphere in history.
Now, let's break down how LucaProt actually works.
At its core, LucaProt uses a deep learning model based on the transformer architecture.
What makes the transformer architecture so powerful is its ability to process sequences of varying lengths
while capturing both local and long-range dependencies within the data.
This makes it ideal for biological sequence analysis,
where relationships between distant parts of a sequence can be crucial for understanding function.
LucaProt doesn't stop at analyzing the sequence alone.
It integrates the predicted 3D structure of proteins, using the AI model ESMfold.
ESMfold predicts how a protein will fold into its three-dimensional shape,
which is critical for understanding how that protein functions in the real world.
This structural prediction is key because even if two protein sequences are highly divergent,
their three-dimensional structures might still perform similar functions.
For example, let's say you have two RNA viruses whose RDRP sequences look almost nothing alike.
Traditional models would miss the connection, but LucaProt,
thanks to its deep learning model and structural predictions,
can recognize that both sequences fold into similar structures and therefore likely perform the same function.
This allows LucaProt to identify highly divergent viruses that have evolved to the point
where their sequences are barely recognizable compared to known examples.
In fact, LucaProt was able to identify 70,000 novel viruses in just this way.
Many of these viruses were found in extreme environments,
such as salt lakes, hot springs, and hydrothermal vents,
places where viral life has adapted to thrive under extreme conditions.
Some of the viruses LucaProt discovered were unlike anything seen before,
with genomes that stretched up to 47,250 nucleotides in length,
far exceeding the typical size of known RNA viruses.
To give you a better sense of the power of LucaProt's eye-driven approach,
imagine trying to identify distant relatives at a family reunion.
Traditional viral discovery methods would be like searching for familiar faces in the crowd.
If someone doesn't closely resemble a family member you already know, you'd miss them completely.
LucaProt, however, is like having a tool that can identify even the most distantly related family members
by looking at more than just surface-level features.
It can find commonalities at a deeper level,
whether they share a similar bone structure or genetic markers,
even if their appearance has changed dramatically.
This ability to detect hidden patterns and make connections
that humans and traditional tools might miss is exactly what makes LucaProt so revolutionary.
It's a reminder of the power of AI not just to analyze data,
but to discover new realms of knowledge that were previously inaccessible.
LucaProt's discoveries don't just expand our understanding of the RNA virus sphere.
They point to a future where AI is at the forefront of biological discovery.
The implications of this work are profound.
We're now able to explore viral diversity at a scale never before possible,
opening up new avenues for research into viral ecology, evolution, and host-virus interactions.
One of the most exciting applications of this research is in understanding the role
these newly discovered viruses play in ecosystems.
Are they infecting plants, animals, or even microorganisms we haven't yet identified?
How do they influence the environments they inhabit?
These are questions that LucaProt's discoveries are just beginning to address.
And as AI models like LucaProt continue to improve,
we will gain more detailed insights into the complex web of life shaped by RNA viruses.
However, this work also highlights a number of open questions.
For instance, while LucaProt identified tens of thousands of novel viruses,
there remains a significant gap in our understanding of the hosts these viruses infect.
No clear RNA virus has been shown to infect archaea, for example, leaving us with the question,
are there RNA viruses infecting these organisms, or are they immune?
These are the kinds of questions that future research will need to answer.
While LucaProt has made incredible strides, we are still only scratching the surface
of what AI can uncover in the biological world.
Let's take a moment to step back and see where this work fits within the larger context of eye-driven discoveries.
Earlier this year, we celebrated AI's contribution to science
when the Nobel Prize in Chemistry was awarded for AlphaFold,
a deep learning model that predicts the structure of proteins with unprecedented accuracy.
LucaProt represents the next phase of this AI evolution.
While AlphaFold focuses on known proteins,
LucaProt pushes the boundaries even further by discovering completely unknown RNA viruses,
many of which have never been seen before.
In a way, LucaProt builds on AlphaFold's legacy,
using AI to explore the hidden corners of biology that have long eluded scientists.
Both LucaProt and AlphaFold highlight the incredible power of AI not just to solve existing problems
but to uncover entire realms of life that were previously beyond our reach.
AI is no longer just an analytical tool,
it's becoming an indispensable partner in biological discovery.
The RNA viruses uncovered by LucaProt are not simply an addition to our database of known life forms.
They represent a new frontier in virology,
one that could reshape how we think about viral evolution, ecology, and even human health.
So, what's next?
LucaProt's discovery of tens of thousands of new RNA viruses is only the beginning.
AI is poised to play an even bigger role in biology as these tools become more refined and more powerful.
With AI's help, we're not just uncovering hidden viruses,
we're gaining new insights into how ecosystems function, how life evolved,
and even how viruses might be harnessed for future biomedical applications.
The sheer scale and speed at which AI can process and analyze biological data is unlike anything we've seen before.
As models like LucaProt continue to improve,
we will unlock even more secrets hidden in the biological, dark matter,
transforming our understanding of life on Earth.
Thank you for tuning into this episode of Voices of Tomorrow,
where we explored the vast, hidden virus sphere and how AI is unlocking secrets that have eluded humanity for centuries.
As we've seen, the boundaries of science are expanding faster than ever before,
and with the help of AI, we are rewriting the very code of life itself.
But this is just the beginning.
The discoveries we've discussed today are a glimpse into a future where AI will lead us to answers we didn't even know to ask.
So, whether you're a researcher on the front lines of innovation, or someone fascinated by the potential of technology to transform our world,
I invite you to stay connected.
Be sure to subscribe, share your thoughts, and let us know what excites you most about the future of AI.
In the episodes to come, we'll continue our deep dive into the cutting-edge intersections of technology, biology, and beyond.
Will we explore the mysterious depths of quantum computing?
Or perhaps investigate the next wave of breakthroughs in eye-driven healthcare?
Whatever it may be, I can promise you this.
The discoveries we will uncover together will challenge what we thought we knew and inspire new visions of what's possible.
So, join us again next time on Voices of Tomorrow, where today's voices of innovation are shaping the future, one breakthrough at a time.