Accounting for protein-solvent contacts facilitates design of nonaggregating lattice proteins.

The folding specificity of proteins can be simulated using simplified structural models and knowledge-based pair-potentials. However, when the same models are used to simulate systems that contain many proteins, large aggregates tend to form. In other words, these models cannot account for the fact that folded, globular proteins are soluble. Here we show that knowledge-based pair-potentials, which include explicitly calculated energy terms between the solvent and each amino acid, enable the simulation of proteins that are much less aggregation-prone in the folded state. Our analysis clarifies why including a solvent term improves the foldability. The aggregation for potentials without water is due to the unrealistically attractive interactions between polar residues, causing artificial clustering. When a water-based potential is used instead, polar residues prefer to interact with water; this leads to designed protein surfaces rich in polar residues and well-defined hydrophobic cores, as observed in real protein structures. We developed a simple knowledge-based method to calculate interactions between the solvent and amino acids. The method provides a starting point for modeling the folding and aggregation of soluble proteins. Analysis of our simple model suggests that inclusion of these solvent terms may also improve off-lattice potentials for protein simulation, design, and structure prediction.

[1]  Joan-Emma Shea,et al.  Molecular Structures of Quiescently Grown and Brain-Derived Polymorphic Fibrils of the Alzheimer Amyloid Aβ9-40 Peptide: A Comparison to Agitated Fibrils , 2010, PLoS Comput. Biol..

[2]  M. Karplus,et al.  Kinetics of protein folding. A lattice model study of the requirements for folding to the native state. , 1994, Journal of molecular biology.

[3]  R. Jernigan,et al.  A new substitution matrix for protein sequence searches based on contact frequencies in protein structures. , 1993, Protein engineering.

[4]  K. Leonhard,et al.  Solvent–amino acid interaction energies in 3-D-lattice MC simulations of model proteins. Aggregation thermodynamics and kinetics , 2003 .

[5]  D Thirumalai,et al.  Probing the mechanisms of fibril formation using lattice models. , 2008, The Journal of chemical physics.

[6]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[7]  F. Cohen,et al.  Thermodynamics of model prions and its implications for the problem of prion protein folding. , 1999, Journal of molecular biology.

[8]  Atomic-scale simulations confirm that soluble beta-sheet-rich peptide self-assemblies provide amyloid mimics presenting similar conformational properties. , 2010, Biophysical journal.

[9]  Tristan Bereau,et al.  Generic coarse-grained model for protein folding and aggregation. , 2009, The Journal of chemical physics.

[10]  E. Shakhnovich,et al.  Proteins with selected sequences fold into unique native conformation. , 1994, Physical review letters.

[11]  D. Thirumalai,et al.  Exploring protein aggregation and self‐propagation using lattice models: Phase diagram and kinetics , 2002, Protein science : a publication of the Protein Society.

[12]  Michele Vendruscolo,et al.  A Generic Mechanism of Emergence of Amyloid Protofilaments from Disordered Oligomeric Aggregates , 2008, PLoS Comput. Biol..

[13]  U. Sauer,et al.  Getting Closer to the Whole Picture , 2007, Science.

[14]  H. Scheraga,et al.  Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. , 1976, Macromolecules.

[15]  Richard W. Clarke,et al.  Direct characterization of amyloidogenic oligomers by single-molecule fluorescence , 2008, Proceedings of the National Academy of Sciences.

[16]  A. Rey,et al.  Topology-based potentials and the study of the competition between protein folding and aggregation. , 2009, The Journal of chemical physics.

[17]  C. Geyer,et al.  Annealing Markov chain Monte Carlo with applications to ancestral inference , 1995 .

[18]  A. Godzik,et al.  Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct? , 1997, Protein science : a publication of the Protein Society.

[19]  J. Thornton,et al.  Ion-pairs in proteins. , 1983, Journal of molecular biology.

[20]  E. Shakhnovich,et al.  Engineering of stable and fast-folding sequences of model proteins. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[21]  K. Heremans,et al.  Pressure effect on the temperature-induced unfolding and tendency to aggregate of myoglobin. , 1999, Biochemistry.

[22]  R. Jernigan,et al.  Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation , 1985 .

[23]  A. Lyubartsev,et al.  New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles , 1992 .

[24]  D. Frenkel,et al.  Simple off-lattice model to study the folding and aggregation of peptides , 2007 .

[25]  A. Maritan,et al.  Lattice tube model of proteins. , 2004, Physical review letters.

[26]  Vijay S. Pande,et al.  How accurate must potentials be for successful modeling of protein folding , 1995, cond-mat/9510123.

[27]  Carol K Hall,et al.  Side-chain interactions determine amyloid formation by model polyglutamine peptides in molecular dynamics simulations. , 2006, Biophysical journal.

[28]  C. Dobson.  Protein folding and misfolding , 2003, Nature.

[29]  D. Covell,et al.  Conformations of folded proteins in restricted spaces. , 1990, Biochemistry.

[30]  Sanne Abeln,et al.  Disordered Flanks Prevent Peptide Aggregation , 2008, PLoS Comput. Biol..

[31]  C. Hall,et al.  Molecular dynamics simulations of spontaneous fibril formation by random-coil peptides. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[32]  G. Parisi,et al.  Simulated tempering: a new Monte Carlo scheme , 1992, hep-lat/9205018.

[33]  U. Hobohm,et al.  Enlarged representative set of protein structures , 1994, Protein science : a publication of the Protein Society.

[34]  K. Dill,et al.  Statistical potentials extracted from protein structures: how accurate are they? , 1996, Journal of molecular biology.

[35]  D. Thirumalai,et al.  Pair potentials for protein folding: Choice of reference states and sensitivity of predicted native states to variations in the interaction schemes , 2008, Protein science : a publication of the Protein Society.

[36]  R. Jernigan,et al.  Structure-derived potentials and protein simulations. , 1996, Current opinion in structural biology.

[37]  C. Dobson,et al.  A toy model for predicting the rate of amyloid formation from unfolded protein. , 2005, Journal of molecular biology.

[38]  D Frenkel,et al.  Designing refoldable model molecules. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[39]  T L Blundell,et al.  FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. , 2001, Journal of molecular biology.