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- 01GYC6CWETECE4W8H4VE0AT313 classification C3.
- 01GYC6CWETECE4W8H4VE0AT313 date "2022".
- 01GYC6CWETECE4W8H4VE0AT313 language "eng".
- 01GYC6CWETECE4W8H4VE0AT313 type conference.
- 01GYC6CWETECE4W8H4VE0AT313 hasPart 01GYC7K21GH4QE0FFJHE2FFQGK.pdf.
- 01GYC6CWETECE4W8H4VE0AT313 subject "Medicine and Health Sciences".
- 01GYC6CWETECE4W8H4VE0AT313 presentedAt urn:uuid:5f794c56-70ab-45eb-b002-8100ae24af49.
- 01GYC6CWETECE4W8H4VE0AT313 abstract "The rapid emergence and spread of antibiotic resistance mechanisms is an increasing health care problem due to bacterial infections with resistant strains. There is an urgent need for alternative antibiotics. Enzybiotics are a promising alternative to conventional antibiotics. They are derived from enzymes used by bacteriophages to degrade the bacterial cell wall, also called phage lytic proteins. These enzymes have a modular architecture, allowing protein engineers to recombine protein domains of native phage lytic proteins and create new modular variants with improved properties. We have created PhaLP, a database of phage lytic proteins, to provide easy access to the natural diversity of phage lytic proteins. The rich diversity of their domains, provides the building blocks to create new modular enzybiotics, tailored for a specific application. Here, we engineer an enzybiotic against vancomycin-resistant Enterococcus faecalis causing urinary tract infections. Given a set of building blocks, one can imagine a design space of all possible modular variants, each with a different fitness upon evaluation in a suitable assay. With an increasing number of building blocks, the size of this design space rapidly explodes, making it impossible to evaluate all variants. However, machine learning can be adopted to model the fitness landscape and, in an iterative approach, identify the top variant by screening a fraction of the complete design space.".
- 01GYC6CWETECE4W8H4VE0AT313 author 7E3E1484-50F5-11E5-B4A0-F149B5D1D7B1.
- 01GYC6CWETECE4W8H4VE0AT313 author C157F57C-AA66-11E4-986E-18D2B4D1D7B1.
- 01GYC6CWETECE4W8H4VE0AT313 author F4D5440C-F0ED-11E1-A9DE-61C894A0A6B4.
- 01GYC6CWETECE4W8H4VE0AT313 dateCreated "2023-04-19T07:24:23Z".
- 01GYC6CWETECE4W8H4VE0AT313 dateModified "2024-10-29T16:01:58Z".
- 01GYC6CWETECE4W8H4VE0AT313 name "Machine learning-guided engineering of modular enzybiotics to target vancomycin-resistant Enterococcus faecalis infections of the urinary tract".
- 01GYC6CWETECE4W8H4VE0AT313 sameAs LU-01GYC6CWETECE4W8H4VE0AT313.
- 01GYC6CWETECE4W8H4VE0AT313 sourceOrganization urn:uuid:32a0ee0f-ae95-454f-801c-7d3cf8e9141e.
- 01GYC6CWETECE4W8H4VE0AT313 sourceOrganization urn:uuid:4e36bbbd-7882-4801-98b2-00fd9f7b705f.
- 01GYC6CWETECE4W8H4VE0AT313 type C3.