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Other ORP type . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Other ORP type . 2025
License: CC BY
Data sources: Datacite
ZENODO
Other ORP type . 2025
License: CC BY
Data sources: Datacite
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WEBINAR: Using AI protein design to design binding proteins to challenging bacterial transporters

Authors: Grinter, Rhys;

WEBINAR: Using AI protein design to design binding proteins to challenging bacterial transporters

Abstract

This record includes training materials associated with the Australian BioCommons webinar ‘Using AI protein design to design binding proteins to challenging bacterial transporters’. This webinar took place on 15 July 2025 and is part of the series “Leveraging deep learning to design custom protein-binding proteins”. Series description Deep learning methods are speeding up the process of designing proteins with desirable biophysical properties. This fast moving field leverages computational workflows that integrate deep learning models like RFdiffusion, ProteinMPNN, Bindcraft with protein structural prediction methods (Alphafold, Chai-1, Boltz-2) and traditional structural biology methods to improve protein design success rates. This webinar series features case studies from leaders in the field and is designed to inspire and help you recognise potential applications of this new approach to the design of protein-binding-proteins. Join us to hear how software such as Bindcraft is being applied to different research questions and gain hints and tips on using them in your own work. This series is brought to you by the Community for Structural Biology Computing in Australia. Speaker: Dr Rhys Grinter, University of Melbourne Host: Dr Melissa Burke, Australian BioCommons Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Grinter_slides_2025 (PDF): A PDF copy of the slides presented during the webinar. Materials shared elsewhere: A recording of this webinar is available on the Australian BIoCommons YouTube channel: https://youtu.be/3Ad2gUjeSL8

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Keywords

FOS: Computer and information sciences, Bioinformatics, Deep learning, Protein design, Structural biology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average