WEBINAR SERIES: Leveraging deep learning to design custom protein-binding-proteins

This record collates training materials associated with the Australian BioCommons webinar series 'Leveraging deep learning to design custom protein-binding proteins' that took place between July - November 2025.

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.

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:

  • Series metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc.

Materials shared elsewhere:

Recordings of all webinars in this series are available Australian BioCommons YouTube channel.

The slides from these webinars are shared in Zenodo.

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

Dr Rhys Grinter, University of Melbourne

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Recording: https://youtu.be/3Ad2gUjeSL8

Slides: https://zenodo.org/records/16511653

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AIcrs: AI-Designed Anti-CRISPRs as Programmable CRISPR Inhibitors

Dr Cyntia Taveneau, Monash University

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Recording: https://youtu.be/GSoOfyJUYSA

Slides: https://zenodo.org/records/17033917

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Using in silico design methods to create de novo proteins that selectively modulate apoptosis

Dr Richard Birkinshaw, WEHI

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Recording: https://youtu.be/9-3sHy1ybpE

Slides: https://zenodo.org/records/17148914

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Introducing ProteinDJ: A modular and open-source framework for protein design workflows

Dr Josh Hardy, WEHI

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Recording: https://youtu.be/xwvF62HxaF0

Slides: https://zenodo.org/uploads/17337232

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Baby steps in the AI-guided design of proteins to modulate gene transcription

Professor Joel Mackay, University of Sydney

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Recording: https://youtu.be/tKqH8WlkIX4

Slides: https://zenodo.org/records/17605782

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DOI: 10.5281/zenodo.17626499

Licence: Creative Commons Attribution 4.0 International

Keywords: Structural Biology, Deep learning, Life science, AI, Bioinformatics

Status: Active

Authors: Grinter, Rhys (orcid: 0000-0002-8195-5348), Taveneau, Cyntia (orcid: 0000-0002-3395-4957), Birkinshaw, Richard (orcid: 0000-0003-1825-0182), Hardy, Joshua (orcid: 0000-0002-8014-8552), Mackay, Joel (orcid: 0000-0001-7508-8033)


Activity log