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The Protein Folding Break-Through


Researchers at DeepMind have proudly announced a major break-through in predicting static folded protein structures with a new program known as AlphaFold 2. Protein folding has been an ongoing problem for researchers since 1972. Christian Anfinsen speculated in his Nobel Prize acceptance speech in that year that the three-dimensional structure of a given protein should be algorithm determined by the one-dimensional DNA sequence that describes it. When you hear protein, you might think of muscles and whey powder, but the proteins mentioned here are chains of amino acids that fold into complex shapes. Many of the enzymes, antibodies, and hormones inside your body are folded proteins. We've discussed why protein folding is important as well covered recent advancements in cryo-electron microscopy used to experimentally determine the structure of folded proteins.

AlphaFold makes its mark in predicting protein structures


Players applaud, say words like Whoo, bang plastic knives on the table and enjoy the best weekends with artificial intelligence as the main act, thanks to AI unleashed in games. WIRED UK's science editor, Matt Reynolds, looked at DeepMind's impact on AI milestones: "It has outplayed Go champions, bested professional StarCraft players and turned its attention to chess and shogi." Let the games continue but the serious stuff must seriously shine. In brief, we can admire that unleashing AI for the purpose of scientific discovery has become especially alive and well thanks to research at DeepMind. Tech watchers commented this week on research papers showing the strengths of AI. "As AI matures as a field (and runs out of video games to conquer) probably more of its achievements will look like these: solid improvements in important research domains."

AI Solves 50-Year-Old Biology 'Grand Challenge' Decades Before Experts Predicted


A long-standing and incredibly complex scientific problem concerning the structure and behaviour of proteins has been effectively solved by a new artificial intelligence (AI) system, scientists report. DeepMind, the UK-based AI company, has wowed us for years with its parade of ever-advancing neural networks that continually trounce humans at complex games such as chess and Go. All those incremental advancements were about much more than mastering recreational diversions, however. In the background, DeepMind's researchers were seeking to coax their AIs towards solving much more fundamentally important scientific puzzles – such as finding new ways to fight disease by predicting infinitesimal but vitally important aspects of human biology. Now, with the latest version of their AlphaFold AI engine, they seem to have actually achieved this very ambitious goal – or at least gotten us closer than scientists ever have before. For about 50 years, researchers have strived to predict how proteins achieve their three-dimensional structure, and it's not an easy problem to solve.

Harvard's New Open Source AI Algorithm Simplifies Protein Folding Puzzle - The New Stack


Proteins may be small and unassuming, but these molecules are essential for a variety of biological functions in all living organisms, including digestion, immune response and even intracellular communication. Consisting of long chains of smaller organic compounds called amino acids, the different functions of various proteins are determined by the way they fold up in three-dimensional space. Not surprisingly, the folded structures of these protein chains can get immensely complex, and scientists have yet to fully figure out the mysteries behind how and why certain proteins fold the way they do, and how diseases like Alzheimer's might be caused when they misfold. While using modern technologies like cryo-electron microscopes, nuclear magnetic resonance and X-ray crystallography can help us understand protein folding a little better, it's an unfortunately time-consuming and costly process. Accurately predicting the folded structures of proteins could be the key to unlocking many medical mysteries, and thanks to recent developments in integrating artificial intelligence in the field of computational biology, that slow process may very well be accelerated -- allowing us to discover or even design new and useful proteins.

Artificial Intelligence Accurately Predicts Protein Folding


Posted on July 27th, 2021 by Dr. Francis Collins Proteins are the workhorses of the cell. Mapping the precise shapes of the most important of these workhorses helps to unlock their life-supporting functions or, in the case of disease, potential for dysfunction. While the amino acid sequence of a protein provides the basis for its 3D structure, deducing the atom-by-atom map from principles of quantum mechanics has been beyond the ability of computer programs--until now. In a recent study in the journal Science, researchers reported they have developed artificial intelligence approaches for predicting the three-dimensional structure of proteins in record time, based solely on their one-dimensional amino acid sequences [1]. This groundbreaking approach will not only aid researchers in the lab, but guide drug developers in coming up with safer and more effective ways to treat and prevent disease.