ΑΙhub.org
 

Using AlphaFold to find complex protein knots


by
11 August 2022



share this:

complex protein knot with seven crossings (left) predicted by AlphaFold and a simplified representation (right)A complex protein knot with seven crossings (left) predicted by AlphaFold and a simplified representation (right). Image credit: Maarten Brems.

The question of how the chemical composition of a protein, the amino acid sequence, determines its 3D structure has been one of the biggest challenges in biophysics for more than half a century. This knowledge about the so-called “folding” of proteins is in great demand, as it contributes significantly to the understanding of various diseases and their treatment, among other things. For these reasons, Google’s DeepMind research team has developed AlphaFold, an artificial intelligence that predicts 3D structures.

A team consisting of researchers from Johannes Gutenberg University Mainz (JGU) and the University of California, Los Angeles, has now taken a closer look at these structures and examined them with respect to knots. We know knots primarily from shoelaces and cables, but they also occur on the nanoscale in our cells. Knotted proteins can not only be used to assess the quality of structure predictions but also raise important questions about folding mechanisms and the evolution of proteins.

The most complex knots as a test for AlphaFold

“We investigated numerically all – that is some 100,000 – predictions of AlphaFold for new protein knots,” said Maarten A. Brems, a PhD student in the group of Dr Peter Virnau at Mainz University. The goal was to identify rare, high-quality structures containing complex and previously unknown protein knots to provide a basis for experimental verification of AlphaFold’s predictions. The study not only discovered the most complex knotted protein to date but also the first composite knots in proteins. The latter can be thought of as two separate knots on the same string. “These new discoveries also provide insight into the evolutionary mechanisms behind such rare proteins”, added Robert Runkel, a theoretical physicist also involved in the project. The results of this study were recently published in Protein Science.

Dr Peter Virnau is pleased with the results: “We have already established a collaboration with our colleague Dr Todd Yeates from UCLA to confirm these structures experimentally. This line of research will shape the biophysics community’s view of artificial intelligence – and we are fortunate to have an expert like Todd Yeates involved.”

Read the research paper

AlphaFold predicts the most complex protein knot and composite protein knots
Maarten A. Brems, Robert Runkel, Todd O. Yeates, Peter Virnau




Johannes Gutenberg Universität Mainz

            AUAI is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Interview with Minji Park: humans in the loop to keep language models up to date

  06 Nov 2026
Human-AI co-inference through a shared editable state.

AI plus chemistry can expand battery electrolyte design

  05 Nov 2026
Researchers are using artificial intelligence to speed the search for better battery materials.
coffee corner

AIhub coffee corner: Are humanoids the future of embodied AI?

  04 Nov 2026
The AIhub coffee corner captures the musings of AI experts over a short conversation.

“With AI, we need to reconsider what it means to learn”

  03 Nov 2026
Artificial intelligence is forcing universities to reinvent how they teach and assess students’ knowledge.

Forthcoming machine learning and AI seminars: November 2026 edition

  02 Nov 2026
A list of free-to-attend AI-related seminars that are scheduled to take place in the next couple of months.
monthly digest

AIhub monthly digest: October 2026 – neurosymbolic planning, time-series forecasting, and robots for the home

  30 Oct 2026
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
AI pioneers

From teaching machines to teaching people: an interview with Michael Littman

  29 Oct 2026
AI Pioneer Michael Littman reflects on the evolution of reinforcement learning, the power of science communication, and how generative AI is changing the way we teach and learn.

From lab to life: Why translating AI advances to the real world is a major challenge

  28 Oct 2026
Systems that perform well in controlled lab settings often fall short when deployed in homes, in hospitals or on city streets.


↑


AUAI is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence