AI Model LucaProt Uncovers Over 160,000 New RNA Viruses, Revolutionizing Viral Discovery

AI model LucaProt uncovers 251,000 new RNA viruses, revealing hidden diversity worldwide

By Dr. Sushama R. Chaphalkar, PhD.Reviewed by Susha Cheriyedath, M.Sc.Oct 13 2024 A new deep learning model, LucaProt, has detected over 251,000 new RNA virus species from global ecosystems, revealing unprecedented viral diversity in places like Antarctic sediment and extreme aquatic environments. Discover how this breakthrough could reshape our understanding of viral evolution. Study: Using artificial intelligence to document the hidden RNA virosphere In a recent study published in the journal Cell, researchers developed a deep learning model, "LucaProt," a transformer-based AI model to detect highly divergent ribonucleic acid (RNA)-dependent RNA polymerase (RdRP) sequences in meta-transcriptomes from diverse ecosystems. They identified 180 RNA virus supergroups and 161,979 putative RNA virus species, showing that RNA viruses are widespread and present even in extreme environments. Background RNA viruses are widespread and infect a variety of species, yet their role in global ecosystems has only recently been recognized due to large-scale virus discovery efforts. These studies, primarily using RdRP sequences, have expanded the known virosphere by identifying thousands of new virus species. However, current tools often miss highly divergent RNA viruses, prompting the need for improved identification strategies. Deep learning, particularly algorithms like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, has revolutionized many areas of life sciences by offering more accurate and flexible approaches to identifying viruses. While CNNs and RNNs have been effective, they face limitations in processing long or complex sequences. Transformer architectures, which excel at capturing both short- and long-range relationships, present a promising alternative for discovering highly divergent RNA viruses. Therefore, researchers in the present study developed a transformer-based artificial intelligence (AI) tool named LucaProt, which was rigorously benchmarked against several other virus discovery tools such as Diamond, HMMscan, HH-suite, and PalmScan. LucaProt achieved the highest recall rate (98.22%) and outperformed these methods in terms of recall and long-sequence processing. Additionally, LucaProt maintained a lower false positive rate compared to these tools to detect highly divergent RNA viruses from meta-transcriptomes to potentially reveal hidden viral diversity. About the study (A) Geographical distribution of the samples analyzed at an ecosystem level. Pie size is positively correlated to the number of samples (log10). The DBSCAN clustering algorithm was applied to group 1,837 latitude and longitude points from all metatranscriptomes into 70 clustered points. (B) Total number of samples at different ecosystems. The embedded bar chart represents the samples used for dual RNA and DNA sequencing in this study. A total of 10,487 meta-transcriptomes, comprising 51 terabytes of sequencing data, were analyzed, of which 10,437 were obtained from the Sequence Read Archive of the National Center for Biotechnology Information database, covering diverse environments such as aquatic, soil, host-related, and extreme habitats. Additionally, 50 datasets were generated from Antarctica and China, covering marine, freshwater, soil, and sediment samples. Sequencing and DNA/RNA extraction were performed. Sequence reads were assembled into contigs, and potential proteins were predicted using ORFfinder. Two strategies were employed to identify potential viral RdRPs: LucaProt and ClstrSearch (a traditional approach that clusters proteins based on sequence similarity). The LucaProt model was trained on 235,413 samples, including 5,979 positive and 229,434 negative sequences, ensuring a comprehensive and well-validated dataset. The results were compared to another method based on homologous protein clustering. A benchmarking test compared LucaProt with Diamond, HMMscan, HH-suite, and PalmScan tools. LucaProt outperformed these traditional tools, revealing significantly more new RNA viruses. Reverse transcription polymerase chain reaction-based assays validated the presence of RNA organisms from viral supergroups. In addition, AlphaFold2 was utilized to predict the three-dimensional (3D) structures of viral RdRPs, with their structural similarities to known viral and eukaryotic polymerases thoroughly evaluated. Results and discussion LucaProt showed high accuracy (0.014% false positives) and specificity (1.72% false negatives). A total of 513,134 RNA viral contigs were identified using the two methods, representing 161,979 potential viral species (with over 90% RdRP identity) and 180 RNA viral supergroups, comparable to existing viral classifications by the International Committee on Taxonomy of Viruses. Notably, LucaProt identified 70,458 putative unique viruses, including 60 previously unidentified supergroups, with the highest recall rate among all tested methods. Of these, 99.9% of viral contigs and 87.2% of supergroups were identified by both methods, while LucaProt identified an additional 444 contigs and 23 supergroups exclusively. LucaProt achieved the highest recall rate of 98.22% among the tools. Other tools identified less than 42% of the new viruses exclusive to LucaProt. Notably, LucaProt recalled over 98% of RdRPs from other studies. Validation confirmed that the 180 new viral supergroups were RNA viruses based on RdRP motifs and sequence similarity. Further analysis using AlphaFold2 revealed structural similarities between newly identified viral RdRPs and existing viral polymerases, enhancing confidence in the identification of novel RNA viruses. The study also uncovered some of the most complex RNA virus genomes ever identified, including one genome that was 47.3 kilobases long, among the longest RNA viruses discovered to date. Most RNA virus genomes were around 2,131 nucleotides. Additional proteins were identified in new genomes, reinforcing their classification as RNA viruses. The RNA virosphere expanded significantly, with a 55.9-fold increase in species compared to previous classifications. High phylogenetic diversity was found in newly discovered supergroups, indicating potential for more divergent RNA viruses. Widespread virus presence was revealed across 32 ecosystem subtypes and 1,612 locations, with 33.3% of groups identified by LucaProt being previously unreported. Alpha diversity, a measure of species diversity within an ecosystem, was highest in environments like leaf litter, while viral abundance peaked in Antarctic sediment and marine environments. Many new viral supergroups were predominantly aquatic or sediment-based, with a few associated with specific host ecosystems. However, systemic biases in data generation may affect comparisons across ecosystems. The study's limitations include challenges in classifying highly divergent viruses, lack of matching DNA data for some virus groups, and the identification of only partial viral genomes focused on RdRP segments. Conclusion This study enhances our understanding of the RNA virosphere by identifying over 251,000 new viral species and 180 novel supergroups using deep learning and large-scale meta-transcriptomic analysis. These findings highlight the vast genetic diversity of viruses in environmental samples, emphasizing the importance of ongoing research in ecology and public health related to viral pathogens and ecosystem dynamics. Journal reference: Using artificial intelligence to document the hidden RNA virosphere. Hou, Xin, et al., Cell (2024), DOI: 10.1016/j.cell.2024.09.027, https://www.cell.com/cell/fulltext/S0092-8674(24)01085-7

AI Discovers 161,000 New Viruses - Neuroscience News

Summary: A novel study study using AI has uncovered 161,979 new RNA viruses, significantly expanding our understanding of Earth's viral diversity. These discoveries were made by analyzing genetic data using a machine learning model, which identified previously unrecognized viruses hidden in public databases. The findings reveal a vast array of viruses in extreme environments worldwide, showing the resilience and adaptability of RNA viruses. This research paves the way for further exploration of viral and microbial diversity, potentially reshaping how scientists study Earth's ecosystems. Artificial intelligence (AI) has been used to reveal details of a diverse and fundamental branch of life living right under our feet and in every corner of the globe. 161,979 new species of RNA virus have been discovered using a machine learning tool that researchers believe will vastly improve the mapping of life on Earth and could aid in the identification of many millions more viruses yet to be characterised. Published in Cell and conducted by an international team of researchers, the study is the largest virus species discovery paper ever published. "We have been offered a window into an otherwise hidden part of life on earth, revealing remarkable biodiversity," said senior author Professor Edwards Holmes from the School of Medical Sciences in the Faculty of Medicine and Health at the University of Sydney. "This is the largest number of new virus species discovered in a single study, massively expanding our knowledge of the viruses that live among us," Professor Holmes said. "To find this many new viruses in one fell swoop is mind-blowing, and it just scratches the surface, opening up a world of discovery. There are millions more to be discovered, and we can apply this same approach to identifying bacteria and parasites." Although RNA viruses are commonly associated with human disease, they are also found in extreme environments around the world and may even play key roles in global ecosystems. In this study they were found living in the atmosphere, hot springs and hydrothermal vents. "That extreme environments carry so many types of viruses is just another example of their phenomenal diversity and tenacity to live in the harshest settings, potentially giving us clues on how viruses and other elemental life-forms came to be," Professor Holmes said. HOW THE AI TOOL WORKED The researchers built a deep learning algorithm, LucaProt, to compute vast troves of genetic sequence data, including lengthy virus genomes of up to 47,250 nucleotides and genomically complex information to discover more than 160,000 viruses. "The vast majority of these viruses had been sequenced already and were on public databases, but they were so divergent that no one knew what they were," Professor Holmes said. "They comprised what is often referred to as sequence 'dark matter'. Our AI method was able to organise and categorise all this disparate information, shedding light on the meaning of this dark matter for the first time. The AI tool was trained to compute the dark matter and identify viruses based on sequences and the secondary structures of the protein that all RNA viruses use for replication. It was able to significantly fast track virus discovery, which, if using traditional methods, would be time intensive. Co-author from Sun Yat-sen University, the study's institutional lead, Professor Mang Shi said: "We used to rely on tedious bioinformatics pipelines for virus discovery, which limited the diversity we could explore. "Now, we have a much more effective AI-based model that offers exceptional sensitivity and specificity, and at the same time allows us to delve much deeper into viral diversity. We plan to apply this model across various applications." Co-author Dr Zhao-Rong Li, who researches in the Apsara Lab of Alibaba Cloud Intelligence, said: "LucaProt represents a significant integration of cutting-edge AI technology and virology, demonstrating that AI can effectively accomplish tasks in biological exploration. "This integration provides valuable insights and encouragement for further decoding of biological sequences and the deconstruction of biological systems from a new perspective. We will also continue our research in the field of AI for virology." Professor Holmes said: "The obvious next step is to train our method to find even more of this amazing diversity, and who knows what extra surprises are in store." Funding: The researchers declare no competing interests. The research was supported by the National Natural Science Foundation of China, the Shenzhen Science and Technology Program, the Natural Science Foundation of Guangdong Province, the Guangdong Province "Pearl River Talent Plan" Innovation and Entrepreneurship Team Project, the Hong Kong Innovation and Technology Fund (ITF) and the Health and Medical Research Fund. Professor Holmes is funded by a National Health and Medical Research Council of Australia Investigator grant and by AIR@InnoHK administered by the Innovation and Technology Commission, Hong Kong Special Administrative Region, China. Using artificial intelligence to document the hidden virosphere Current metagenomic tools can fail to identify highly divergent RNA viruses. We developed a deep learning algorithm, termed LucaProt, to discover highly divergent RNA-dependent RNA polymerase (RdRP) sequences in 10,487 metatranscriptomes generated from diverse global ecosystems. LucaProt integrates both sequence and predicted structural information, enabling the accurate detection of RdRP sequences. Using this approach, we identified 161,979 potential RNA virus species and 180 RNA virus supergroups, including many previously poorly studied groups, as well as RNA virus genomes of exceptional length (up to 47,250 nucleotides) and genomic complexity. A subset of these novel RNA viruses was confirmed by RT-PCR and RNA/DNA sequencing. Newly discovered RNA viruses were present in diverse environments, including air, hot springs, and hydrothermal vents, with virus diversity and abundance varying substantially among ecosystems. This study advances virus discovery, highlights the scale of the virosphere, and provides computational tools to better document the global RNA virome.

AI scans RNA 'dark matter' and uncovers 70,000 new viruses

Researchers have used artificial intelligence (AI) to uncover 70,500 viruses previously unknown to science, many of them weird and nothing like known species. The RNA viruses were identified using metagenomics, in which scientists sample all the genomes present in the environment without having to culture individual viruses. The method shows the potential of AI to explore the 'dark matter' of the RNA virus universe. Viruses are ubiquitous microorganisms that infect animals, plants and even bacteria, yet only a small fraction have been identified and described. There is "essentially a bottomless pit" of viruses to discover, says Artem Babaian, a computational virologist at the University of Toronto in Canada. Some of these viruses could cause diseases in people, which means that characterizing them could help to explain mystery illnesses, he says. Previous studies have used machine learning to find new viruses in sequencing data. The latest study, published in Cell this week, takes that work a step further and uses it to look at predicted protein structures. The AI model incorporates a protein-prediction tool, called ESMFold, that was developed by researchers at Meta (formerly Facebook, headquartered in Menlo Park, California). A similar AI system, AlphaFold, was developed by researchers at Google DeepMind in London, who won the Nobel Prize in Chemistry this week. In 2022, Babaian and his colleagues searched 5.7 million genomic samples archived in publicly available databases and identified almost 132,000 new RNA viruses. Other groups have led similar efforts. But RNA viruses evolve quickly, so existing methods for identifying RNA viruses in genomic sequence data probably miss many. A common method is to look for a section of the genome that encodes a key protein used in RNA replication, called RNA-dependent RNA polymerase (RdRp). But if the sequence that encodes this protein in a virus is vastly different from any known sequence, researchers won't recognize it. Shi Mang, an evolutionary biologist at Sun Yat-sen University in Shenzhen, China, and a co-author of the Cell study, and his colleagues went looking for previously unrecognized viruses in publicly available genomic samples. They developed a model, called LucaProt, using the 'transformer' architecture that underpins ChatGPT, and fed it sequencing and ESMFold protein-prediction data. They then trained their model to recognize viral RdRps and used it to find sequences that encoded these enzymes -- evidence that those sequences belonged to a virus -- in the large tranche of genomic data. Using this method, they identified some 160,000 RNA viruses, including some that were exceptionally long and found in extreme environments such as hot springs, salt lakes and air. Just under half of them had not been described before. They found "little pockets of RNA virus biodiversity that are really far off in the boonies of evolutionary space", says Babaian. "It's a really promising approach for expanding the virosphere," says Jackie Mahar, an evolutionary virologist at the CSIRO Australian Centre for Disease Preparedness in Geelong. Characterizing viruses will help researchers to understand the microbes' origins and how they evolved in different hosts, she says. And expanding the pool of known viruses makes it easier to find more viruses that are similar, says Babaian. "All of a sudden you can see things that you just weren't seeing before." The team wasn't able to determine the hosts of the viruses they identified, which should be investigated further, says Mahar. Researchers are particularly interested in knowing whether any of the new viruses infect archaea, an entire branch of the tree of life for which no RNA viruses have been clearly shown to infect. Shi is now developing a model to predict the hosts of these newly identified RNA viruses. He hopes this will help researchers to understand the roles that viruses have in their environmental niches.

The hidden virosphere: AI helps discover more than 160,000 new virus species

Artificial intelligence (AI) has been used to reveal details of a diverse and fundamental branch of life living right under our feet and in every corner of the globe. A total of 161,979 new species of RNA virus have been discovered using a machine learning tool that researchers believe will vastly improve the mapping of life on Earth and could aid in the identification of many millions more viruses yet to be characterized. Published in Cell and conducted by an international team of researchers, the study is the largest virus species discovery paper ever published. "We have been offered a window into an otherwise hidden part of life on earth, revealing remarkable biodiversity," said senior author Professor Edwards Holmes from the School of Medical Sciences in the Faculty of Medicine and Health at the University of Sydney. "This is the largest number of new virus species discovered in a single study, massively expanding our knowledge of the viruses that live among us," Professor Holmes said. "To find this many new viruses in one fell swoop is mind-blowing, and it just scratches the surface, opening up a world of discovery. There are millions more to be discovered, and we can apply this same approach to identifying bacteria and parasites." Although RNA viruses are commonly associated with human disease, they are also found in extreme environments around the world and may even play key roles in global ecosystems. In this study they were found living in the atmosphere, hot springs and hydrothermal vents. "That extreme environments carry so many types of viruses is just another example of their phenomenal diversity and tenacity to live in the harshest settings, potentially giving us clues on how viruses and other elemental life-forms came to be," Professor Holmes said. How the AI tool worked The researchers built a deep learning algorithm, LucaProt, to compute vast troves of genetic sequence data, including lengthy virus genomes of up to 47,250 nucleotides and genomically complex information to discover more than 160,000 viruses. "The vast majority of these viruses had been sequenced already and were on public databases, but they were so divergent that no one knew what they were," Professor Holmes said. "They comprise what is often referred to as sequence 'dark matter.' Our AI method was able to organize and categorize all this disparate information, shedding light on the meaning of this dark matter for the first time." The AI tool was trained to compute the dark matter and identify viruses based on sequences and the secondary structures of the protein that all RNA viruses use for replication. It was able to significantly fast track virus discovery, which, if using traditional methods, would be time intensive. Co-author from Sun Yat-sen University, the study's institutional lead, Professor Mang Shi said, "We used to rely on tedious bioinformatics pipelines for virus discovery, which limited the diversity we could explore. Now, we have a much more effective AI-based model that offers exceptional sensitivity and specificity, and at the same time allows us to delve much deeper into viral diversity. We plan to apply this model across various applications." Co-author Dr. Zhao-Rong Li, who researches in the Apsara Lab of Alibaba Cloud Intelligence, said, "LucaProt represents a significant integration of cutting-edge AI technology and virology, demonstrating that AI can effectively accomplish tasks in biological exploration. "This integration provides valuable insights and encouragement for further decoding of biological sequences and the deconstruction of biological systems from a new perspective. We will also continue our research in the field of AI for virology." Professor Holmes said, "The obvious next step is to train our method to find even more of this amazing diversity, and who knows what extra surprises are in store."

Over 160,000 new viruses discovered by AI

Artificial intelligence (AI) has been used to reveal details of a diverse and fundamental branch of life living right under our feet and in every corner of the globe. 161,979 new species of RNA virus have been discovered using a machine learning tool that researchers believe will vastly improve the mapping of life on Earth and could aid in the identification of many millions more viruses yet to be characterised. Published in Cell and conducted by an international team of researchers,the study is the largest virus species discovery paper ever published. "We have been offered a window into an otherwise hidden part of life on earth, revealing remarkable biodiversity," said senior author Professor Edwards Holmes from the School of Medical Sciences in the Faculty of Medicine and Health at the University of Sydney. "This is the largest number of new virus species discovered in a single study, massively expanding our knowledge of the viruses that live among us," Professor Holmes said. "To find this many new viruses in one fell swoop is mind-blowing, and it just scratches the surface, opening up a world of discovery. There are millions more to be discovered, and we can apply this same approach to identifying bacteria and parasites." Although RNA viruses are commonly associated with human disease, they are also found in extreme environments around the world and may even play key roles in global ecosystems. In this study they were found living in the atmosphere, hot springs and hydrothermal vents. "That extreme environments carry so many types of viruses is just another example of their phenomenal diversity and tenacity to live in the harshest settings, potentially giving us clues on how viruses and other elemental life-forms came to be," Professor Holmes said. HOW THE AI TOOL WORKED The researchers built a deep learning algorithm, LucaProt, to compute vast troves of genetic sequence data, including lengthy virus genomes of up to 47,250 nucleotides and genomically complex information to discover more than 160,000 viruses. "The vast majority of these viruses had been sequenced already and were on public databases, but they were so divergent that no one knew what they were," Professor Holmes said. "They comprised what is often referred to as sequence 'dark matter'. Our AI method was able to organise and categorise all this disparate information, shedding light on the meaning of this dark matter for the first time. The AI tool was trained to compute the dark matter and identify viruses based on sequences and the secondary structures of the protein that all RNA viruses use for replication. It was able to significantly fast track virus discovery, which, if using traditional methods, would be time intensive. Co-author from Sun Yat-sen University, the study's institutional lead, Professor Mang Shi said: "We used to rely on tedious bioinformatics pipelines for virus discovery, which limited the diversity we could explore. Now, we have a much more effective AI-based model that offers exceptional sensitivity and specificity, and at the same time allows us to delve much deeper into viral diversity. We plan to apply this model across various applications." Co-author Dr Zhao-Rong Li, who researches in the Apsara Lab of Alibaba Cloud Intelligence, said: "LucaProt represents a significant integration of cutting-edge AI technology and virology, demonstrating that AI can effectively accomplish tasks in biological exploration. This integration provides valuable insights and encouragement for further decoding of biological sequences and the deconstruction of biological systems from a new perspective. We will also continue our research in the field of AI for virology." Professor Holmes said: "The obvious next step is to train our method to find even more of this amazing diversity, and who knows what extra surprises are in store."

AI helped discover 160,000 new viruses -- here's why that is good news

While many are (understandably) skeptical of the potential of AI, it has its very specific strengths that could be a huge boon for humanity not just now, but in the future. One of those, it appears, is detecting new species of virus. A new machine-learning model at the University of Sydney, Australia, has crunched an absurd amount of data and uncovered no fewer than 161,979 new RNA (ribonucleic) viruses by analyzing genetic data and cross-referencing it with unrecognized ones. It all sounds a little like something only Batman could pull off, relying on a degree of technology and intelligence that's far beyond comprehension for the rest of us. For anyone keen to understand more the full results are available in a research paper. The algorithm, called LucaProt, computes huge swathes of data including virus genomes. It then matched that to protein structures used by RNA viruses for replication. That kind of output would traditionally have taken much longer, but it's not just about finding viruses responsible for diseases. Some of the RNA viruses will exist in extreme conditions across the globe and form a key part of an ecosystem. This study helped find them in hot springs, hydrothermal vents, and even Earth's atmosphere. "We have been offered a window into an otherwise hidden part of life on earth, revealing remarkable biodiversity," said senior author Professor Edwards Holmes from the School of Medical Sciences in the Faculty of Medicine and Health at the University of Sydney told Cell. "This is the largest number of new virus species discovered in a single study, massively expanding our knowledge of the viruses that live among us," Professor Holmes added. "To find this many new viruses in one fell swoop is mind-blowing, and it just scratches the surface, opening up a world of discovery. There are millions more to be discovered, and we can apply this same approach to identifying bacteria and parasites." This is the latest in a series of examples showing how AI is helping and shaping the world beyond making pretty pictures or writing jokes for a wedding speech.