For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
6
views
14
references
 Top references
cited by
0
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      106
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning

      discussion
      Author(s): 1 , 2 , 2
      Publication date (Electronic):
      Journal: PLOS Biology
      Publisher: Public Library of Science

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and state-of-the-art of antibiotic discovery based on ecological principles, genome mining and artificial intelligence.

          Abstract

          A major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. This Perspective discusses the challenges and state of the art of antibiotic discovery.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: found
          • Article: not found

          Cistrome and Epicistrome Features Shape the Regulatory DNA Landscape.

          Ronan O’Malley, Shao-shan Carol Huang, Liang Song … (2016)
          The cistrome is the complete set of transcription factor (TF) binding sites (cis-elements) in an organism, while an epicistrome incorporates tissue-specific DNA chemical modifications and TF-specific chemical sensitivities into these binding profiles. Robust methods to construct comprehensive cistrome and epicistrome maps are critical for elucidating complex transcriptional networks that underlie growth, behavior, and disease. Here, we describe DNA affinity purification sequencing (DAP-seq), a high-throughput TF binding site discovery method that interrogates genomic DNA with in-vitro-expressed TFs. Using DAP-seq, we defined the Arabidopsis cistrome by resolving motifs and peaks for 529 TFs. Because genomic DNA used in DAP-seq retains 5-methylcytosines, we determined that >75% (248/327) of Arabidopsis TFs surveyed were methylation sensitive, a property that strongly impacts the epicistrome landscape. DAP-seq datasets also yielded insight into the biology and binding site architecture of numerous TFs, demonstrating the value of DAP-seq for cost-effective cistromic and epicistromic annotation in any organism.
          Article 0 comments Cited 398 times – based on 0 reviews     Review now
            • Record: found
            • Abstract: not found
            • Article: not found

            Genomic basis for natural product biosynthetic diversity in the actinomycetes.

            Markus Nett, Haruo Ikeda, Bradley S Moore (2009)
            Article 0 comments Cited 240 times – based on 0 reviews     Review now
              • Record: found
              • Abstract: found
              • Article: not found

              Chemical ecology of antibiotic production by actinomycetes.

              Anne van der Meij, Sarah Worsley, Matthew Hutchings … (2017)
              Actinomycetes are a diverse family of filamentous bacteria that produce a plethora of natural products relevant for agriculture, biotechnology and medicine, including the majority of the antibiotics we use in the clinic. Rather than as free-living bacteria, many actinomycetes have evolved to live in symbiosis with among others plants, fungi, insects and sponges. As a common theme, these organisms profit from the natural products and enzymes produced by the actinomycetes, for example, for protection against pathogenic microbes, for growth promotion or for the degradation of complex natural polymers such as lignocellulose. At the same time, the actinomycetes benefit from the resources of the hosts they interact with. Evidence is accumulating that these interactions control the expression of biosynthetic gene clusters and have played a major role in the evolution of the high chemical diversity of actinomycete-produced secondary metabolites. Many of the biosynthetic gene clusters for antibiotics are poorly expressed under laboratory conditions, but they are likely expressed in response to host-specific demands. Here, we review the environmental triggers and cues that control natural product formation by actinomycetes and provide pointers as to how these insights may be harnessed for drug discovery.
              Article 0 comments Cited 170 times – based on 0 reviews     Review now
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): PLoS Biol
                Journal ID (iso-abbrev): PLoS Biol
                Journal ID (publisher-id): plos
                Title: PLOS Biology
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Print): 1544-9173
                ISSN (Electronic): 1545-7885
                Publication date (Electronic): 28 February 2025
                Publication date Collection: February 2025
                Publication date PMC-release: 28 February 2025
                Volume: 23
                Issue: 2
                Electronic Location Identifier: e3003058
                Affiliations
                [1 ] Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
                [2 ] Institute of Biology, Leiden University, Leiden, The Netherlands
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-0341-1561
                Article
                Publisher ID: PBIOLOGY-D-25-00231
                DOI: 10.1371/journal.pbio.3003058
                PMC ID: 11878895
                PubMed ID: 40019875
                SO-VID: 0124f95a-4212-42c8-8c53-ec101db57c49
                Copyright © © 2025 Medema, van Wezel
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Page count
                Figures: 0, Tables: 0, Pages: 3
                Funding
                The authors received no specific funding for this work.
                Categories
                Subject: Perspective
                Subject: Medicine and Health Sciences
                Subject: Pharmacology
                Subject: Drugs
                Subject: Antimicrobials
                Subject: Antibiotics
                Subject: Biology and Life Sciences
                Subject: Microbiology
                Subject: Microbial Control
                Subject: Antimicrobials
                Subject: Antibiotics
                Subject: Medicine and Health Sciences
                Subject: Pharmacology
                Subject: Drug Research and Development
                Subject: Drug Discovery
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Applied Mathematics
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Algorithms
                Subject: Machine Learning Algorithms
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Machine Learning Algorithms
                Subject: Computer and Information Sciences
                Subject: Artificial Intelligence
                Subject: Machine Learning
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Genomics
                Subject: Biology and Life Sciences
                Subject: Microbiology
                Subject: Microbial Control
                Subject: Antimicrobial Resistance
                Subject: Medicine and Health Sciences
                Subject: Pharmacology
                Subject: Antimicrobial Resistance
                Subject: Biology and Life Sciences
                Subject: Ecology
                Subject: Chemical Ecology
                Subject: Ecology and Environmental Sciences
                Subject: Ecology
                Subject: Chemical Ecology
                Custom metadata
                PLOS Publication Stage vor-update-to-uncorrected-proof
                Publication Update 2025-03-04

                ScienceOpen disciplines: Life sciences
                Data availability:
                ScienceOpen disciplines: Life sciences

                Comments

                Comment on this article

                Related Documents Log