Periodic Reporting for period 1 - SMILE (A hybrid framework to characterize SLiM Mimicry by Leishmania (SMILE) parasites.)
Période du rapport: 2021-08-01 au 2023-07-31
Pathogens are microorganisms that invade and take over their target host's body by manipulating the host's immune system and suppressing its defenses. This results in changes to the host's normal functions and physiology. They interfere with various cellular processes, such as transport, replication of DNA, to ensure their survival and reproduction. We know a lot about how viruses and bacteria do this, which has led to the development of targeted treatments. However, our understanding of invasive eukaryotic pathogens is still limited.
To gain insights into how eukaryotic pathogens hijack host cells, we focused on Leishmania. Leishmaniasis is a parasitic disease caused by Leishmania, affecting millions of people worldwide, with annual deaths estimated to range from 26,000 to 65,000. While it mainly affects impoverished and rural areas, environmental changes could lead to its spread in new regions. Understanding Leishmania's behavior and how it interacts with the host's cells is crucial to develop ways to combat the infection and control the disease.
In the constant battle between the host's immune system and pathogens, pathogenic Short Linear Motifs (SLiMs) play a significant role. These small segments of proteins have the ability to modulate the host cell's functions and evolve rapidly, giving the pathogen an advantage. Studying SLiMs using traditional methods is challenging, but bioinformatics approaches can accelerate the process.
The proposed project aims to systematically examine interactions at the protein level and understand the structural details of these interactions. This information can be used by experimentalists to identify linear motifs, which could lead to new therapeutic strategies for mutation-based diseases. Understanding how Leishmania invades and mimics host cell functions is a critical step toward developing treatments for parasitic diseases.
In conclusion, this research aims to uncover the strategies used by eukaryotic pathogens, specifically Leishmania, to take control of host cells. By understanding these mechanisms, scientists hope to develop targeted treatments and combat parasitic diseases more effectively.
To gain insights into how eukaryotic pathogens hijack host cells, we focused on Leishmania. Leishmaniasis is a parasitic disease caused by Leishmania, affecting millions of people worldwide, with annual deaths estimated to range from 26,000 to 65,000. While it mainly affects impoverished and rural areas, environmental changes could lead to its spread in new regions. Understanding Leishmania's behavior and how it interacts with the host's cells is crucial to develop ways to combat the infection and control the disease.
In the constant battle between the host's immune system and pathogens, pathogenic Short Linear Motifs (SLiMs) play a significant role. These small segments of proteins have the ability to modulate the host cell's functions and evolve rapidly, giving the pathogen an advantage. Studying SLiMs using traditional methods is challenging, but bioinformatics approaches can accelerate the process.
The proposed project aims to systematically examine interactions at the protein level and understand the structural details of these interactions. This information can be used by experimentalists to identify linear motifs, which could lead to new therapeutic strategies for mutation-based diseases. Understanding how Leishmania invades and mimics host cell functions is a critical step toward developing treatments for parasitic diseases.
In conclusion, this research aims to uncover the strategies used by eukaryotic pathogens, specifically Leishmania, to take control of host cells. By understanding these mechanisms, scientists hope to develop targeted treatments and combat parasitic diseases more effectively.
The aim of the project to detect SLiMs in Leishmania mediating host-pathogen PPIs. For this task we first need to identify virulence factors (proteins that invade the host), so then we can explore their details and detect tthose motifs that bind to the host proteins. While SMILE is focused on Leishmania, the developed computational pipeline can later be adapted for other eukaryotic pathogens as well.
We soon realized that Leishmania is an extremely complicated organism for computational biology, as data quality from high throughput experiments were lower quality and we also found a lot of sequence errors. We realized that we don't have reliable data about secreted and surface proteins that helps invading the host, so we started manual annotation.
At the same time AlphaFold2 just came out and it revolutionized computational structural biology. We also started to use AlphaFold2 and realized that it can help us to identify surface proteins.
Traditional methods to predict the structure of transmembrane proteins were complex and prone to errors. The introduction of AlphaFold2 (AF2) has been a tremendous help to the scientific community. It complements the limited experimental data by providing 3D predictions for thousands of new alpha-helical membrane proteins. However, AF2 faces challenges due to the lack of reliable structural templates and not being specifically trained to handle certain structural boundaries. To address this, we developed a new database called Transmembrane AlphaFold database (TmAlphaFold database) and used a simple geometry-based method to visualize the most likely position of the membrane plane.
Having TmAlphaFold helped us to expedite manual annotation to our dataset. Beyond manual annotation we included a lot of experimental and computational to develop a database of Leishmania proteins.
LeishMANIAdb is a database specifically created to study how Leishmania's virulence factors might affect host proteins. Since we don't have complete information about the secretomes of various Leishmania species, we gathered experimental evidence and made computational predictions to identify secreted proteins. This resulted in a user-friendly website where people can access all the available information about these experimental and predicted secretomes. Additionally, we manually analyzed interactions between 211 proteins involved in the host-pathogen relationship, as well as the characteristics and functions of 3764 transmembrane (TM) proteins from different Leishmania species. To further understand how the infection works, we added automatic predictions about the structures and functions of these proteins, providing new insights into the molecular mechanisms of the disease.
Developing LeishMANIAdb enabled us to look into more details about Leishmania proteins. Although by now we had a smaller dataset of surface proteins, we still only have limited information about Leishmania secreted proteins. Therefore we first looked for protein trafficking machineries to have a better overall picture of what proteins get secreted.
We studied three ancient protein targeting systems and their receptors to see how they compare to other living organisms, including their hosts. These systems involve important components of cellular life, like secretory signal peptides, endoplasmic reticulum (ER) retention motifs (KDEL motifs), and autophagy signals (motifs interacting with ATG8 family members). While these systems were expected to be similar across different organisms, we found that they actually vary to some extent from what we see in animals, plants, or fungi. We not only describe how they work but also create predictive models that can tell us where these proteins are located or what functions they serve in Leishmania species. This is the first time such predictions have been made for these proteins in Leishmania.
Finally, using all these results we identified potential proteins that help the entry to the host cells. We selected the most promising one and performed experiments to validate the protein-protein interaction. Although we couldn't collect enough reliable data from the experiments to confirm the presence of the identified linear motif, further experiments may help us to verify and characterize the interaction.
All results are available on preprint servers and online. The results will be published in Q1 quality journals. Early results were presented as a conference talk and a poster presentation.
We soon realized that Leishmania is an extremely complicated organism for computational biology, as data quality from high throughput experiments were lower quality and we also found a lot of sequence errors. We realized that we don't have reliable data about secreted and surface proteins that helps invading the host, so we started manual annotation.
At the same time AlphaFold2 just came out and it revolutionized computational structural biology. We also started to use AlphaFold2 and realized that it can help us to identify surface proteins.
Traditional methods to predict the structure of transmembrane proteins were complex and prone to errors. The introduction of AlphaFold2 (AF2) has been a tremendous help to the scientific community. It complements the limited experimental data by providing 3D predictions for thousands of new alpha-helical membrane proteins. However, AF2 faces challenges due to the lack of reliable structural templates and not being specifically trained to handle certain structural boundaries. To address this, we developed a new database called Transmembrane AlphaFold database (TmAlphaFold database) and used a simple geometry-based method to visualize the most likely position of the membrane plane.
Having TmAlphaFold helped us to expedite manual annotation to our dataset. Beyond manual annotation we included a lot of experimental and computational to develop a database of Leishmania proteins.
LeishMANIAdb is a database specifically created to study how Leishmania's virulence factors might affect host proteins. Since we don't have complete information about the secretomes of various Leishmania species, we gathered experimental evidence and made computational predictions to identify secreted proteins. This resulted in a user-friendly website where people can access all the available information about these experimental and predicted secretomes. Additionally, we manually analyzed interactions between 211 proteins involved in the host-pathogen relationship, as well as the characteristics and functions of 3764 transmembrane (TM) proteins from different Leishmania species. To further understand how the infection works, we added automatic predictions about the structures and functions of these proteins, providing new insights into the molecular mechanisms of the disease.
Developing LeishMANIAdb enabled us to look into more details about Leishmania proteins. Although by now we had a smaller dataset of surface proteins, we still only have limited information about Leishmania secreted proteins. Therefore we first looked for protein trafficking machineries to have a better overall picture of what proteins get secreted.
We studied three ancient protein targeting systems and their receptors to see how they compare to other living organisms, including their hosts. These systems involve important components of cellular life, like secretory signal peptides, endoplasmic reticulum (ER) retention motifs (KDEL motifs), and autophagy signals (motifs interacting with ATG8 family members). While these systems were expected to be similar across different organisms, we found that they actually vary to some extent from what we see in animals, plants, or fungi. We not only describe how they work but also create predictive models that can tell us where these proteins are located or what functions they serve in Leishmania species. This is the first time such predictions have been made for these proteins in Leishmania.
Finally, using all these results we identified potential proteins that help the entry to the host cells. We selected the most promising one and performed experiments to validate the protein-protein interaction. Although we couldn't collect enough reliable data from the experiments to confirm the presence of the identified linear motif, further experiments may help us to verify and characterize the interaction.
All results are available on preprint servers and online. The results will be published in Q1 quality journals. Early results were presented as a conference talk and a poster presentation.
Results can be grouped into three main categories:
1. Developing the TmAlphaFold database to automatically detect membrane proteins using the AlphaFold predicted structure, construct the possible localization of the membrane plane and evaluate the structures
2. Developing LeishMANIAdb that contains experimental and structural information about Leishmania proteins
3. Identifying general eukaryotic systems that highly differ in Leishmania compared to other organisms
1. Developing the TmAlphaFold database to automatically detect membrane proteins using the AlphaFold predicted structure, construct the possible localization of the membrane plane and evaluate the structures
2. Developing LeishMANIAdb that contains experimental and structural information about Leishmania proteins
3. Identifying general eukaryotic systems that highly differ in Leishmania compared to other organisms