The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.

A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice. The objective is to find conformations maximizing H-H contacts. It is known that even in this reduced setting, the problem is intractable (NP-hard). In this work, we apply deep reinforcement learning (DRL) to the two-dimensional HP model. We can obtain the conformations of best known energies for benchmark HP sequences with lengths from 20 to 50. Our DRL is based on a deep Q-network (DQN). We find that a DQN based on long short-term memory (LSTM) architecture greatly enhances the RL learning ability and significantly improves the search process. DRL can sample the state space efficiently, without the need of manual heuristics. Experimentally we show that it can find multiple distinct best-known solutions per trial. This study demonstrates the effectiveness of deep reinforcement learning in the HP model for protein folding.

When DeepMind, an Alphabet subsidiary, started off more than a decade ago, solving some most pressing research questions and problems with AI wasn't at the top of the company's mind. Instead, the company started off AI research with computer games. Every score and win was a measuring stick of success, and pointed to DeepMind's AI going in the right direction. "Five years ago, we conquered the game of Go. This was a great moment," said Colin Murdoch, the chief business officer, during a fireside chat on Tuesday at the AI Hardware Summit being held in Santa Clara, California.

AlphaFold 2 paper and code is finally released. This post aims to inspire new generations of Machine Learning (ML) engineers to focus on foundational biological problems.

"I didn't think we would get to this point in my lifetime." That's how one research leader in structural biology responded to last week's publication of research in which artificial intelligence (AI) was used to predict the structure of more than 20,000 human proteins, as well as that of nearly all the known proteins produced by 20 model organisms such as Escherichia coli, fruit flies and yeast, but also soya bean and Asian rice. That is a combined total of around 365,000 predictions1. The data, publicly accessible for the first time (see https://alphafold.ebi.ac.uk), were released online on 22 July by researchers at DeepMind, a London-based AI company owned by Google's parent company, Alphabet, and the European Bioinformatics Institute, based at the European Molecular Biology Laboratory (EBI-EMBL) near Cambridge, UK. DeepMind's AI predicts structures for a vast trove of proteins The DeepMind team developed a machine-learning tool called AlphaFold.

DeepMind solves a 50-year old problem in Protein Folding Prediction. AlphaFold 2 improves over DeepMind's 2018 AlphaFold system with a new architecture and massively outperforms all competition. In this Video, we take a look at how AlphaFold 1 works and what we can gather about AlphaFold 2 from the little information that's out there. CASP14 Result Bar Chart: https://www.predictioncenter.org/casp14/zscores_final.cgi Paper Title: High Accuracy Protein Structure Prediction Using Deep Learning Abstract: 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.

A "deep learning" software program from Google-owned lab DeepMind showed great progress in solving one of biology's greatest challenges – understanding protein folding. Protein folding is the process by which a protein takes its shape from a string of building blocks to its final three-dimensional structure, which determines its function. By better predicting how proteins take their structure, or "fold," scientists can more quickly develop drugs that, for example, block the action of crucial viral proteins. Solving what biologists call "the protein-folding problem" is a big deal. Proteins are the workhorses of cells and are present in all living organisms.

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.

It's these genetic definitions that circumscribe their three-dimensional structures, which in turn determines their capabilities. But protein "folding," as it's called, is notoriously difficult to figure out from a corresponding genetic sequence alone. DNA contains only information about chains of amino acid residues and not those chains' final form. In December 2018, DeepMind attempted to tackle the challenge of protein folding with a machine learning system called AlphaFold. The product of two years of work, the Alphabet subsidiary said at the time that AlphaFold could predict structures more precisely than prior solutions.

Frank Noé Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany Gianni De Fabritiis Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Doctor Aiguader 88, 08003 Barcelona, Spain, and Institucio Catalana de Recerca i Estudis Avanats (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain Cecilia Clementi Center for Theoretical Biological Physics, and Department of Chemistry, Rice University, 6100 Main Street, Houston, Texas 77005, United StatesAbstract Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way simulations are performed to explore the energy landscape of protein systems is also changing as force-fields are started to be designed by means of machine learning methods. These methods are also used to extract the essential information from large simulation datasets and to enhance the sampling of rare events such as folding/unfolding transitions. While significant challenges still need to be tackled, we expect these methods to play an important role on the study of protein folding and dynamics in the near future. We discuss here the recent advances on all these fronts and the questions that need to be addressed for machine learning approaches to become mainstream in protein simulation.Introduction During the last couple of decades advances in artificial intelligence and machine learning have revolutionized many application areas such as image recognition and language translation. The key of this success has been the design of algorithms that can extract complex patterns and highly nontrivial relationships from large amount of data and abstract this information in the evaluation of new data.