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Holy Peptide Model! BATMAN Outperforms Peptide-TCR Binding Prediction Standard

Imagine your immune cells could be modified to attack any kind of cancer. T cell receptor (TCR) therapy has the potential to one day become a universal cancer treatment. But there are risks. Cells announce their state by displaying peptides on their surface. T

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Imagine your immune cells could be modified to attack any kind of cancer. T cell receptor (TCR) therapy has the potential to one day become a universal cancer treatment. But there are risks. Cells announce their state by displaying peptides on their surface. These peptides are used by T cells to distinguish cancerous and healthy cells. While TCR therapy needs laser focus to prevent friendly fire, T cell receptors can recognize more than one peptide, and this cross-reactivity can lead to T cells attacking the wrong targets.

The numbers of peptides and TCRs in the human body is also enormous, making it nearly impossible and costly to determine to which peptides a given TCR can bind. Researchers at Cold Spring Harbor Laboratory (CSHL) have now developed a database, BATCAVE (benchmark for activation of T cells with cross-reactive avidity for epitopes), containing over 22,000 TCR-peptide interactions, together with an AI model, BATMAN (Bayesian inference of activation of TCR by mutant antigens), trained on BATCAVE, which can predict to which peptides a TCR will bind. During testing, BATMAN outperformed competing models in accurately predicting which peptides bind to a given TCR.

To develop BATCAVE and BATMAN, CSHL Assistant Professor Hannah Meyer, PhD, teamed with Associate Professor Saket Navlakha, PhD, and postdoc Amitava Banerjee, PhD. Senior and co-corresponding author Meyer and colleagues described their developments in Cell Systems, in a paper titled “T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.”

A single T cell receptor can recognize a variety of peptides, a property known as TCR cross-reactivity, the authors explained. “Predicting which peptides a TCR cross-reacts to is critical for numerous applications, including predicting viral escape, cancer neoantigen immunogenicity, autoimmunity, and off-target toxicity of T cell-based therapies.” And while mapping all the targets of a T cell receptor is important for predicting pathogenic escape and off-target effects of TCR therapies, “… this mapping has been challenging due to lack of unbiased benchmarking datasets and computational methods sensitive to small-peptide mutations,” the team commented. “… predicting interactions among TCRs, peptides, and major histocompatibility complexes (TCR-pMHCs) remains challenging …”.

To address this challenge the team developed BATCAVE, a T cell receptor cross-reactivity database of 22,000 TCR-pMHC pairs. “… we curated the benchmark for activation of T cells with cross-reactive avidity for epitopes (BATCAVE) database, encompassing near-complete single-amino-acid mutational assays, centered around 25 immunogenic epitopes, across both major histocompatibility complex classes, against 151 human and mouse TCRs, containing 22,000+ TCR-peptide pairs in total,” they explained.

The investigators also created BATMAN, an interpretable Bayesian model, trained on BATCAVE, for predicting the peptides that activate a TCR. “Using this database, we then developed a computational method, called BATMAN, that predicts TCR activation of peptides based on their distances to the TCR’s index peptide,” they continued.

In addition, the team developed an active learning (AL) extension to BATMAN that efficiently maps targets of a novel TCR by selecting a few peptides to assay. “This version provides an efficient way to sample from the prohibitively large antigenic space by iteratively selecting peptides to assay that provide the best improvement of novel TCR activation prediction accuracy.”

Navlakha further explained, “We trained [BATMAN] on a bunch of TCRs and what they recognize. But give me a new TCR that is not in my database, and I need to figure out what it binds to. So, we ask, which are the best peptides I should select to make predictions?”

During testing, BATMAN outperformed competing models in accurately predicting which peptides bind to a given TCR. The AI also revealed why seemingly unrelated peptides get caught in the crossfire. “We show that BATMAN outperforms existing methods, reveals structural and biochemical predictors of TCR-peptide interactions, and can predict polyclonal T cell responses and TCR targets with high sequence dissimilarity,” the investigators commented. “Overall, the interpretable parameters learned by BATMAN capture a host of biological features that reveal the nature of TCR-pMHC interactions.”

Meyer further explained, “It’s not enough to just count differences between potential targets. It matters where the difference is and what type of difference it is. Our model is already good enough to tell us if there are peptides we should be concerned about for targeted [cancer] therapies.”

Despite the promise, there’s more to be done before BATMAN can venture from the BATCAVE for potential clinical use, the authors suggest. As large as the database is, it houses a fraction of all possible TCR-peptide pairs. More data could enhance BATMAN’s performance and potentially help scientists answer fundamental questions about the immune system.

“BATMAN could be improved by incorporating TCR sequence information into the model and by training on datasets from other types of experimental TCR cross-reactivity assays, (e.g., yeast display library enrichment, T-Scan, and SABR), which sample more comprehensively outside the one aa mutational scan space,” the investigators pointed out. Insights from their database and methods could in addition be used to predict how TCR sequences may determine the relationship between TCR-binding affinity and cross-reactivity. The data could potentially aid in the design of high-affinity TCRs with limited off-target cross-reactivity, which would have multiple clinical applications. “There’s a lot of variation in the body’s T-cell response,” Banerjee says. “If we can accurately predict how these cells and peptides interact, that will be very helpful for designing future therapies not only for cancer, but all human illnesses.”

In summary, the investigators wrote, “Overall, BATMAN fills a hitherto unoccupied niche of TCR-pMHC prediction methods by accurately discriminating between small differences in peptide sequences, which we show existing methods fail to predict, but which are essential for understanding neoantigen immunogenicity and off-target effects of TCR-based therapies.”

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The endogenous opioids and their receptors are widely distributed throughout the central and peripheral nervous systems, particularly the parts of these systems that regulate pain, emotion, reward, stress responses, motivation, drug addiction, and autonomic control. The differential expression and location of the various receptor subtypes across different neurons account for the wide range of opioid-related behaviors. The activation of µ-opioid receptors is mainly known for playing a role in pain relief. Still, research has also indicated it may be involved in behaviors related to survival, such as appetite and reproduction. The activity of µ-opioid receptors is also known to play a critical role in responses to social stimuli by modulating responses to social rejection or social acceptance, for example. Activation of the δ-opioid receptors and κ-opioid receptors is also known to be involved in pain modulation. Also, studies have shown that NOP activation is involved in pain mechanisms and several behaviors related to psychological stress. Alterations in the endogenous opioid system are suspected to be involved in Parkinson's disease, seizures, neuroprotective mechanisms, and depression.

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02How do these peptides act?

These peptides, like the parent compound AC253, acted as antagonists at the AMY receptor. They were also resistant to protein breakdown, and crossed the blood-brain barrier easily when injected into the abdominal cavity, to localize in the hippocampus, which is crucial in memory. These peptides protected the brain against beta-amyloid injury, and normalized the AD-associated impairment of the memory-associated long-term potentiation of nerve impulses in the hippocampus. They improved memory testing results, and reduced the level of inflammation in the brain. These effects appear to be mediated via the blockade of AMY receptors. For instance, inhibition of microglial AMY receptors reduce the activation of the inflammasome NLRP3. This reduces the secretion of inflammatory chemicals in the surrounding brain tissue, which offers another mechanism for lower amyloid production. In addition, these peptides increase the rate of outflow of amyloid beta from the brain, which also contributes to a lower level of amyloid after treatment. These marked changes all occurred within a relatively short span of treatment. A very important additional finding was that treatment with these peptides brought about improvement in mice which were showing signs of well-established AD in the brain as well as in their behavior. This is unique in that most therapies fail to affect the progress of AD once it has begun to manifest clinically. Peptides also have fewer off-target effects. Small molecules are easy to administer, inexpensive to make and cross the blood-brain barrier more rapidly. For this reason, the team resorted to computational tools and artificial intelligence to come up with a new small molecular drug based on these peptides. This can be taken orally, and is similar in size and structure to the medications used for medical conditions like high blood pressure. An optimized version is being developed to enable human trials to be conducted. The work so far has taken about two decades, building step upon painstaking step to come up with the right solution. However, says Jhamandas, “Occasionally you come across a discovery that has the potential to change the game in a very fundamental way, like hitting a home run, and I'm very excited that we are really on to something here.” Short amylin receptor antagonist peptides improve memory deficits in Alzheimer’s disease mouse model. Rania Soudy, Ryoichi Kimura, Aarti Patel, Wen Fu, Kamaljit Kaur, David Westaway, Jing Yang & Jack Jhamandas. Scientific Reports, volume 9, Article number: 10942 (2019). https://doi.org/10.1038/s41598-019-47255-9. https://www.nature.com/articles/s41598-019-47255-9

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03What is nisin?

Some bacterial species produce antimicrobial peptides known as bacteriocins that have been used in the food industry as preservatives. For example, nisin, which is produced by Lactococcus lactis, has broad-spectrum bactericidal activity and has been used as a food preservative throughout the world. Nisin is effective in controlling Gram-positive bacteria such as Clostridioides difficile. In combination with other compounds like ethylene diamine tetra-acetic acid and cinnamaldehyde, nisin has been effective in controlling enterotoxigenic Gram-negative bacteria such as Escherichia coli. Previous studies have used chicken and mouse models to demonstrate the in vivo efficacy of nisin on the microbiome, whereas nisin efficacy has been proven in ex vivo experiments on the human microbiome. To date, no studies have assessed the in vivo effects of nisin in large mammals.

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04A peptide identified in a fungus found in northern European pine forests possesses as much power as penicillin as well as vancomycin, according to an international team of researchers.

Reporting in the October 13 issue of Nature, a team from Denmark-based biotech company Novozymes, and researchers from Georgetown University Medical Center and the David Geffen School of Medicine at UCLA, say they have isolated "plectasin," the first defensin ever found in fungi. The research was performed at Novozymes laboratories in Denmark. Defensins are peptides, miniature protein molecules that are produced by a wide range of animals to protect themselves against infection. Humans have defensins in their white blood cells and in their skin, for example, but it is believed that this new fungal defensin, plectasin, is more potent and targets certain bacteria more specifically. Indeed, when plectasin was tested in the laboratory and in animals, it proved to be highly effective against the bacteria Streptococcus pneumoniae, and Streptococcus pyogenes, including strains that are now resistant to conventional antibiotics. These bacteria are responsible for such diseases as meningitis, community-acquired pneumonia, strep throat, life-threatening sepsis, and flesh destroying skin infections. The discovery of plectasin has implications for the development of defensins as a treatment against many common, and deadly, infections, and may initiate a new era of antibiotic discovery and development, said study co-author Michael Zasloff, M.D., Ph.D., Professor in the Departments of Surgery and Pediatrics at Georgetown University Medical Center. Zasloff says that the field of antibiotic development has not changed much since 1929 when Alexander Fleming realized that the fungal "bread mold" Penicillium, which had landed by chance in a Petri dish produced a substance that eliminated colonies of staphylococcal bacteria. "Most antibiotics used by humans are produced by fungi and certain soil bacteria," he said. "Using our existing tools of discovery, we have failed to uncover any new classes of antibiotics from these sources over the past decade. However, by utilizing a new genetic approach that allowed the team to discover plectasin, we now know that a whole class of antibiotics has been overlooked." "This finding (plectasin), and the existence of about 200,000 additional species of fungi, opens up a vast universe to explore for novel peptide antibiotics," said co-author Robert Lehrer, M.D., Distinguished Professor of Medicine at the David Geffen School of Medicine at UCLA. Plectasin, if proven safe and effective in humans, could be on the market by 2012, said Lehrer. Zasloff and Lehrer are known internationally as experts in antimicrobial peptides - the class of antibiotics that plectasin falls within - and in this study they collaborated with Novozymes, a Danish biotech company that led the research. Zasloff and Lehrer are the only two scientists from U.S. universities on the team of 20 researchers who co-authored the research paper. All life forms have to defend themselves against microbial invaders - bacteria, fungi, viruses - and to do this, they produce antimicrobial defensin peptides. In humans, defensins are made by specific white blood cells and immune cells that later engulf foreign invaders, and by the skin and mucous membranes, in order to kill microbes before they invade protective barriers. Researchers believe that fungi have a similar system of defense, especially since these plant-like organisms live off rotting matter, said Zasloff. "They must compete with other organisms, like bacteria and viruses, which also want to consume the same meal. In addition, they need to defend themselves from being eaten by the microbes which surround them." But he said no one had been able to find defensins in fungi using traditional research techniques, which involved growing fungi in liquid cultures and then testing the culture to see if it contained any antibiotic molecule. The research team instead used the latest genetic science to search for the defensins they thought fungi must have. Selecting the Pseudoplectania nigrella species of fungus may have been serendipitous, Lehrer said, but the Novozymes team used state-of-the-art biotechnology to intercept ,and interpret its genetic messages and exhibited tremendous skill in producing plectasin efficiently, economically, and in large amounts." "I started working on antimicrobial peptides over three decades ago, said Lehrer, and my laboratory first described human defensins in 1985. So, the discovery of plectasin makes me feel like a grandfather." Further examination revealed that this defensin, plectasin, resembles defensins found in spiders, scorpions, dragonflies and mussels - thus suggesting that the defensins found in insects, molluscs and fungi arose from a common ancestral gene, the researchers say. Based on this information, the scientists now believe that defensins appeared in living things more than a billion years ago. The investigators then turned to the National Center for Antimicrobials and Infection Control, the Danish equivalent of the U.S. Centers for Disease Control, to test plectasin in the laboratory for antimicrobial activity against a broad spectrum of bacteria. It showed potent activity against several species of Gram-positive bacteria, and was especially active against S. pneumoniae (the leading cause of pneumonia), including all known clinical strains and those that are now resistant to conventional antibiotics. "That is important because increasing bacterial resistance to conventional antibiotics threatens the future of many antibiotics in current use," Zasloff said. "In mouse studies, plectasin showed extremely low toxicity, and was as effective as vancomycin and penicillin in curing the animals of experimental peritonitis (inflammation of the lining of the abdominal cavity, which can be deadly) and pneumonia caused by S. pneumoniae, the researchers report. "Although the precise mechanism by which plectasin exerts its antimicrobial activity is still under investigation, it may work by a mechanism that is very different from traditional antibiotics, Zasloff said. "As a group, defensins exhibit activity against many types of bacteria, fungi, protozoa, and even viruses. It is entirely possible that fungal defensins will be discovered that could be developed against all of these human pathogens," Zasloff added.

Source: www.news-medical.net ↗
05So, how can this definition challenge be overcome?

To precisely define self and non-self peptides and, in turn, self-similarity, we must first improve our understanding of the adaptive immune cascade and its constituent components. In brief, the fundamental unit of adaptive immune recognition comprises the major histocompatibility complex (MHC) molecules (called the human leukocyte antigen [HLA] in humans), the peptide being presented (and, in turn, identified as self or non-self), and the T cell receptor.

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