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  1. Article ; Online: AI in infectious diseases: The role of datasets.

    de la Fuente-Nunez, Cesar

    Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy

    2024  Volume 73, Page(s) 101067

    MeSH term(s) Humans ; Communicable Diseases
    Language English
    Publishing date 2024-02-10
    Publishing country Scotland
    Document type Editorial
    ZDB-ID 1474513-6
    ISSN 1532-2084 ; 1368-7646
    ISSN (online) 1532-2084
    ISSN 1368-7646
    DOI 10.1016/j.drup.2024.101067
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Antibiotic discovery with machine learning.

    de la Fuente-Nunez, Cesar

    Nature biotechnology

    2022  Volume 40, Issue 6, Page(s) 833–834

    MeSH term(s) Anti-Bacterial Agents/therapeutic use ; Drug Discovery ; Machine Learning
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2022-05-09
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 1311932-1
    ISSN 1546-1696 ; 1087-0156
    ISSN (online) 1546-1696
    ISSN 1087-0156
    DOI 10.1038/s41587-022-01327-w
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Peptides from non-immune proteins target infections through antimicrobial and immunomodulatory properties.

    Torres, Marcelo Der Torossian / Cesaro, Angela / de la Fuente-Nunez, Cesar

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Encrypted peptides have been recently described as a new class of antimicrobial molecules. They have been proposed to play a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these peptides are found embedded in ...

    Abstract Encrypted peptides have been recently described as a new class of antimicrobial molecules. They have been proposed to play a role in host immunity and as alternatives to conventional antibiotics. Intriguingly, many of these peptides are found embedded in proteins unrelated to the immune system, suggesting that immunological responses may extend beyond traditional host immunity proteins. To test this idea, here we synthesized and tested representative peptides derived from non-immune proteins for their ability to exert antimicrobial and immunomodulatory properties. Our experiments revealed that most of the tested peptides from non-immune proteins, derived from structural proteins as well as proteins from the nervous and visual systems, displayed potent in vitro antimicrobial activity. These molecules killed bacterial pathogens by targeting their membrane, and those originating from the same region of the body exhibited synergistic effects when combined. Beyond their antimicrobial properties, nearly 90% of the peptides tested exhibited immunomodulatory effects, modulating inflammatory mediators such as IL-6, TNF-α, and MCP-1. Moreover, eight of the peptides identified, collagenin 3 and 4, zipperin-1 and 2, and immunosin-2, 3, 12, and 13, displayed anti-infective efficacy in two different preclinical mouse models, reducing bacterial infections by up to four orders of magnitude. Altogether, our results support the hypothesis that peptides from non-immune proteins may play a role in host immunity. These results potentially expand our notion of the immune system to include previously unrecognized proteins and peptides that may be activated upon infection to confer protection to the host.
    Language English
    Publishing date 2024-04-08
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.25.586636
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Antibiotic identified by AI.

    Cesaro, Angela / de la Fuente-Nunez, Cesar

    Nature chemical biology

    2023  Volume 19, Issue 11, Page(s) 1296–1298

    MeSH term(s) Anti-Bacterial Agents/pharmacology ; Artificial Intelligence
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2023-10-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2202962-X
    ISSN 1552-4469 ; 1552-4450
    ISSN (online) 1552-4469
    ISSN 1552-4450
    DOI 10.1038/s41589-023-01448-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Mining for antimicrobial peptides in sequence space.

    Wan, Fangping / de la Fuente-Nunez, Cesar

    Nature biomedical engineering

    2023  Volume 7, Issue 6, Page(s) 707–708

    MeSH term(s) Antimicrobial Peptides ; Antimicrobial Cationic Peptides ; Amino Acid Sequence
    Chemical Substances Antimicrobial Peptides ; Antimicrobial Cationic Peptides
    Language English
    Publishing date 2023-06-06
    Publishing country England
    Document type Journal Article ; Comment
    ISSN 2157-846X
    ISSN (online) 2157-846X
    DOI 10.1038/s41551-023-01027-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Challenges in computational discovery of bioactive peptides in 'omics data.

    Coelho, Luis Pedro / Santos-Júnior, Célio Dias / de la Fuente-Nunez, Cesar

    Proteomics

    2024  , Page(s) e2300105

    Abstract: Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a ...

    Abstract Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.
    Language English
    Publishing date 2024-03-08
    Publishing country Germany
    Document type Journal Article ; Review
    ZDB-ID 2032093-0
    ISSN 1615-9861 ; 1615-9853
    ISSN (online) 1615-9861
    ISSN 1615-9853
    DOI 10.1002/pmic.202300105
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Leveraging artificial intelligence in the fight against infectious diseases.

    Wong, Felix / de la Fuente-Nunez, Cesar / Collins, James J

    Science (New York, N.Y.)

    2023  Volume 381, Issue 6654, Page(s) 164–170

    Abstract: Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will ... ...

    Abstract Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.
    MeSH term(s) Humans ; Artificial Intelligence ; Communicable Diseases/drug therapy ; Drug Discovery/methods ; Pandemics/prevention & control ; Public Health ; Anti-Infective Agents/chemistry ; Anti-Infective Agents/pharmacology ; Communicable Disease Control
    Chemical Substances Anti-Infective Agents
    Language English
    Publishing date 2023-07-13
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.adh1114
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Antibiotic failure: Beyond antimicrobial resistance.

    de la Fuente-Nunez, Cesar / Cesaro, Angela / Hancock, Robert E W

    Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy

    2023  Volume 71, Page(s) 101012

    Abstract: Despite significant progress in antibiotic discovery, millions of lives are lost annually to infections. Surprisingly, the failure of antimicrobial treatments to effectively eliminate pathogens frequently cannot be attributed to genetically-encoded ... ...

    Abstract Despite significant progress in antibiotic discovery, millions of lives are lost annually to infections. Surprisingly, the failure of antimicrobial treatments to effectively eliminate pathogens frequently cannot be attributed to genetically-encoded antibiotic resistance. This review aims to shed light on the fundamental mechanisms contributing to clinical scenarios where antimicrobial therapies are ineffective (i.e., antibiotic failure), emphasizing critical factors impacting this under-recognized issue. Explored aspects include biofilm formation and sepsis, as well as the underlying microbiome. Therapeutic strategies beyond antibiotics, are examined to address the dimensions and resolution of antibiotic failure, actively contributing to this persistent but escalating crisis. We discuss the clinical relevance of antibiotic failure beyond resistance, limited availability of therapies, potential of new antibiotics to be ineffective, and the urgent need for novel anti-infectives or host-directed therapies directly addressing antibiotic failure. Particularly noteworthy is multidrug adaptive resistance in biofilms that represent 65 % of infections, due to the lack of approved therapies. Sepsis, responsible for 19.7 % of all deaths (as well as severe COVID-19 deaths), is a further manifestation of this issue, since antibiotics are the primary frontline therapy, and yet 23 % of patients succumb to this condition.
    MeSH term(s) Humans ; Anti-Bacterial Agents/pharmacology ; Anti-Bacterial Agents/therapeutic use ; Drug Resistance, Bacterial ; Biofilms ; Sepsis/drug therapy
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2023-10-18
    Publishing country Scotland
    Document type Journal Article ; Review
    ZDB-ID 1474513-6
    ISSN 1532-2084 ; 1368-7646
    ISSN (online) 1532-2084
    ISSN 1368-7646
    DOI 10.1016/j.drup.2023.101012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Molecular tools for probing the microbiome.

    Torres, Marcelo Der Torossian / de la Fuente-Nunez, Cesar

    Current opinion in structural biology

    2022  Volume 76, Page(s) 102415

    Abstract: The microbiome plays essential roles in health and disease. Our understanding of the imbalances that can arise in the microbiome and their consequences is held back by a lack of technologies that selectively knock out members of these microbial ... ...

    Abstract The microbiome plays essential roles in health and disease. Our understanding of the imbalances that can arise in the microbiome and their consequences is held back by a lack of technologies that selectively knock out members of these microbial communities. Antibiotics and fecal transplants, the existing methods for manipulating the microbiota of the gastrointestinal tract, are not sufficiently pinpointed to reveal how particular microbial genes, strains, or species affect human health. A toolset for the precise manipulation of the microbiome could significantly advance disease diagnosis and treatment. Here, we provide an overview of current and future strategies for the development of molecular tools that can be used to probe the microbiome without producing off-target effects.
    MeSH term(s) Anti-Bacterial Agents ; Humans ; Microbiota
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2022-08-16
    Publishing country England
    Document type Journal Article ; Review ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 1068353-7
    ISSN 1879-033X ; 0959-440X
    ISSN (online) 1879-033X
    ISSN 0959-440X
    DOI 10.1016/j.sbi.2022.102415
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Toward Autonomous Antibiotic Discovery.

    de la Fuente-Nunez, Cesar

    mSystems

    2019  Volume 4, Issue 3

    Abstract: Machines hold the potential to replace humans in many societal endeavors, and drug discovery is no exception. Antibiotic innovation has been stalled for decades, which has coincided with an alarming increase in multidrug-resistant bacteria. Since the ... ...

    Abstract Machines hold the potential to replace humans in many societal endeavors, and drug discovery is no exception. Antibiotic innovation has been stalled for decades, which has coincided with an alarming increase in multidrug-resistant bacteria. Since the beginning of the antibiotic era, the natural world has been our greatest innovator, giving rise to nearly all antibiotics available today. As mere observers of the vast molecular diversity produced by Earth's organisms, we have perfected the art of isolating novel chemistries with life-saving antimicrobial properties. However, today we are at a crossroads, as no new molecular scaffolds have been discovered for decades. We may need to look beyond the natural world into the virtual dimension for solutions and harness present-day computational power to help solve the grand global health challenge of antibiotic resistance. Computer-made drugs may enable the discovery of unprecedented functions in biological systems and help replenish our arsenal of effective antibiotics.
    Language English
    Publishing date 2019-06-11
    Publishing country United States
    Document type Journal Article
    ISSN 2379-5077
    ISSN 2379-5077
    DOI 10.1128/mSystems.00151-19
    Database MEDical Literature Analysis and Retrieval System OnLINE

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