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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.For 50 years, the “protein folding problem” has been a major mystery. How does a miniature string-like chemical — the protein molecule – encode the functions of living organisms: how our muscles exert force, how our immune systems reject pathogens, how our eyes see our surroundings, how plants convert solar energy, and all the rest. Huge progress is being made. Moreover, these amazing nano-machines could play important roles in health and disease and commerce in the future, in 2013, in the video “The protein folding problem: a major conundrum of science: Ken Dill at TEDxSBU“, below:
These tiny molecular machines underpin every biological process in every living thing and each one has a unique 3D shape that determines how it works and what it does. But figuring out the exact structure of a protein is an expensive and often time-consuming process, meaning we only know the exact 3D structure of a tiny fraction of the 200m proteins known to science. Being able to accurately predict the shape of proteins could accelerate research in every field of biology. That could lead to important breakthroughs like finding new medicines or finding proteins and enzymes that break down industrial and plastic waste or efficiently capture carbon from the atmosphere. Join Kathryn as she explains what protein folding is, why it’s important and how our Artificial Intelligence system AlphaFold offers a solution to this grand scientific challenge, in the video “Protein folding explained“, below:
In a major scientific advance, the latest version of our AI system AlphaFold has been recognised as a solution to this grand challenge by the organisers 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. A protein’s shape is closely linked with its function, and the ability to predict this structure unlocks a greater understanding of what it does and how it works. Many of the world’s greatest challenges, like developing treatments for diseases or finding enzymes that break down industrial waste, are fundamentally tied to proteins and the role they play. The ‘protein folding problem’ In his acceptance speech for the 1972 Nobel Prize in Chemistry, Christian Anfinsen famously postulated that, in theory, a protein’s amino acid sequence should fully determine its structure. This hypothesis sparked a five decade quest to be able to computationally predict a protein’s 3D structure based solely on its 1D amino acid sequence as a complementary alternative to these expensive and time consuming experimental methods. A major challenge, however, is that the number of ways a protein could theoretically fold before settling into its final 3D structure is astronomical. In 1969 Cyrus Levinthal noted that it would take longer than the age of the known universe to enumerate all possible configurations of a typical protein by brute force calculation – Levinthal estimated 10^300 possible conformations for a typical protein. Yet in nature, proteins fold spontaneously, some within milliseconds – a dichotomy sometimes referred to as Levinthal’s paradox. Results from the CASP14 assessment In 1994, Professor John Moult and Professor Krzysztof Fidelis founded CASP as a biennial blind assessment to catalyse research, monitor progress, and establish the state of the art in protein structure prediction. It is both the gold standard for assessing predictive techniques and a unique global community built on shared endeavour. Crucially, CASP chooses protein structures that have only very recently been experimentally determined (some were still awaiting determination at the time of the assessment) to be targets for teams to test their structure prediction methods against; they are not published in advance. Participants must blindly predict the structure of the proteins, and these predictions are subsequently compared to the ground truth experimental data when they become available. We’re indebted to CASP’s organisers and the whole community, not least the experimentalists whose structures enable this kind of rigorous assessment. The main metric used by CASP to measure the accuracy of predictions is the Global Distance Test (GDT) which ranges from 0-100. In simple terms, GDT can be approximately thought of as the percentage of amino acid residues (beads in the protein chain) within a threshold distance from the correct position. According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods. In the results from the 14th CASP assessment, released today, our latest AlphaFold system achieves a median score of 92.4 GDT overall across all targets. This means that our 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), in the video “AlphaFold: a solution to a 50-year-old grand challenge in biology“, below:
To better understand AlphaFold, please refer to the excerpt from wikipedia, in italics, below:
AlphaFold is an artificial intelligence program developed byGoogle’sDeepMind which performs predictions of protein structure. The program is designed as a deep learning system that is built to predict evolving protein structures to the width of an atom.In November 2020, a version of the program titled AlphaFold 2 took part in the 14th edition of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) competition, in which it predicted the target folded structure of proteins within a margin of an error of the size of an atom or ~0.16 nanometers. This level of accuracy was higher than any of the other computational programs in the competition and matches the levels of accuracy from lab based experimental techniques. The program scored above 90 for around two-thirds of the proteins in CASP’s global distance test (GDT), a test that measures the degree to which a computational program predicted structure is similar to the lab experiment determined structure, with 100 being an exact match. This barrier is considered a significant one in computational biology and a “50 year old problem.”
The inside story of the DeepMind team of scientists and engineers who created AlphaFold, an AI system that is recognised as a solution to “protein folding”, a grand scientific challenge for more than 50 years, in the video “AlphaFold: The making of a scientific breakthrough”
– A deep learning approach to protein folding – Takes the protein sequence data as input – Generates the distance metric between residues in the folded protein – Proposed by DeepMind, in the video “Protein Folding: AlphaFold“, below:
AlphaFold: Improved protein structure prediction using potentials from deep learning | Andrew Senior – Research Scientist, DeepMind, in the video “AlphaFold: Improved protein structure prediction […], AI & Molecular World, Andrew Senior“, below:
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