Exploring amyloid aggregates with coarse-grained protein simulations
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Exploring amyloid aggregates with coarse-grained protein simulations Philippe Derreumaux1,2 1 Laboratoire de Biochimie Théorique, UPR 9080 CNRS, Université Paris Diderot, Sorbonne Paris Cité, IBPC, 13 rue Pierre et Marie Curie, 75005, Paris, France 2 Institut Universitaire de France, 103 Boulevard Saint-Michel, 75005, Paris. ABSTRACT Proteins are complex, yet elegant, machines fine-tuned by evolution to properly fulfill a variety of tasks in the crowded cellular environment. These are, however, very challenging numerically due to their dimension, number of degrees of freedom and the wide range of relevant time scales. With aging, some proteins misfold and form harmful amyloid aggregates associated with multiple neurodegenerative diseases, and in particular Alzheimer’s, which challenge our society today. Here, I present the coarse-grained OPEP (Optimized Potential for Efficient peptide structure Prediction) force field and what we can learn from OPEP simulations to get insights into the self-assembly of amyloid peptides. INTRODUCTION Classical molecular dynamics (MD) simulations can contribute significantly to a better understanding of how proteins find their three-dimensional native structures or how protein/protein or protein/ligand interacts. By inventing innovative computers, such as Anton which can only do one thing, MD, but 100 faster than the best supercomputer, it has been recently possible to elucidate the atomic detail of how 15 structurally diverse proteins with 10-80 amino acids fold into their native states in explicit solvent. These long 1-ms simulations also provide insights into quantitative thermodynamic and kinetic data.1,2 Anton is also able to locate the correct binding location of drugs and may prove particularly useful in the development of allosteric inhibitors that target previously undiscovered binding sites.3 Though continuous progress in computer science and algorithmic development to enhance conformational sampling such as simulated tempering, replica exchange molecular dynamics (REMD),4 and metadynamics5, repeating these atomistic simulations in explicit solvent at a genomic scale is still out of reach. In addition, because the formation of amyloid aggregates associated with neurodegenerative diseases includes too many degrees of freedom and spans time scales of days in vitro, scientists are developing and improving coarse-grained (CG) models for membranes and soluble proteins. The basic idea of CG is to replace groups of atoms by a single bead, thus reducing the number of degrees of freedom. This coarse-graining accelerates conformational sampling and allows larger systems to be studied, but poses the problem of how to derive potentials that maintain the all-atom physical behavior. Overall, despite the loss of atomistic details in the side-chains and the backbone of amino acids, protein CG models have proven useful in the description of many long time processes bridging the gap between the atomistic and mesoscopic scales and have allowed the exploration of protein folding, dynamics, mech
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