Proteins are essential to life, supporting practically all its functions. They are large complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure. Figuring out what shapes proteins fold into is known as the “protein folding problem”, and has stood as a grand challenge in biology for the past 50 years. In a major scientific advance, the latest version of the AI system AlphaFold (deepmind.com) has been recognized as a solution to this grand challenge by the organizers of the biennial Critical Assessment of protein Structure Prediction (CASP). This breakthrough demonstrates the impact AI can have on scientific discovery and its potential to dramatically accelerate progress in some of the most fundamental fields that explain and shape our world.
In the results from the 14th CASP assessment, released on December 4, 2020, the latest AlphaFold system achieves a median score of 92.4 GDT overall across all targets. This means that its predictions have an average error (RMSD) of approximately 1.6 Angstroms, which is comparable to the width of an atom (or 0.1 of a nanometer). Even for the very hardest protein targets, those in the most challenging free-modelling category, AlphaFold achieves a median score of 87.0 GDT (data available here).
This is a reminder to apply for the Instruct-ERIC R&D Pilot Call - get funding of up to €15,000!
Closing 14:00 CEST on 15 April, the Instruct-ERIC R&D Funding Call invites early career researchers to submit proposals to access Instruct technology, equipment, and expertise from our 27 facilities across Europe for small scale pilot research projects in integrated structural biology.
Register now for the 32nd edition of the Instruct-ERIC Structure Meets Function webinar series, taking place on Zoom, 17 April at 11:00 CEST.
The latest webinar will have two speakers who have accessed the Instruct-ERIC facilities, expertise, and machinery via the ISIDORe project.
Talk 1: Nikitas Georgiou - National and Kapodistrian University of Athens
Title: Potential anti-inflammatory drugs
Talk 2: Séverine Padiolleau - Université de Technologie de Compiègne
Title: Identification of aptamers interacting with a protein target involved in lyme disease