Can Peptide Folding Simulations Provide Predictive Information for Aggregation Propensity?

Lin, Edmund I.; Shell, M. Scott

doi:10.1021/jp104114n

Cited by 12Protein Structure and DynamicsAlzheimer's disease research and treatmentsProtein purification and stability

Can Peptide Folding Simulations Provide Predictive Information for Aggregation Propensity?

Lin, Edmund I., Shell, M. Scott·2010·The Journal of Physical Chemistry B

DOI 10.1021/jp104114n

Open this paper in CoChat — free account

Abstract

Nonnative peptide aggregation underlies many diseases and is a major problem in the development of peptide-based therapeutics. Efforts in the past decade have revealed remarkable correlations between aggregation rates or propensities and very simple sequence metrics like hydrophobicity and charge. Here, we investigate the extent to which a molecular picture of peptide folding bears out similar relationships. Using replica exchange molecular dynamics folding simulations, we compute equilibrium conformational ensembles of 142 hexa- and decapeptide systems, of which about half readily form amyloid fibrils and half do not. The simulations are used to compute a variety of ensemble-based properties, and we investigate the extent to which these metrics provide molecular clues about fibril formation. To assess whether multiple metrics together are useful in understanding aggregation, we also develop a number of logistic regression models, some of which predict fibril formers with 70-80% accuracy and identify aggregation-prone regions in larger proteins. Importantly, these models quantify the importance of different molecular properties in aggregation driving forces; notably, they suggest that hydrophobic interactions play a dominant role.

Integrity signals

Registry

Crossref · 10.1021/jp104114n

Cites

85 of 85 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The present work used the AMBER ff96 force field in combination with the OBC2 implicit solvent model, which has been successfully applied to study the association of proteins and peptides by others.
“The present work used the AMBER ff96 force field in combination with the OBC2 implicit solvent model, which has been successfully applied to study the association of proteins and peptides by others.123−125”
From Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints (2025) · cites this paper as [123]

Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535 · see citing works
Protein Misfolding, Functional Amyloid, and Human Disease
Chiti, F., & Dobson, C. M. (2006) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.75.101304.123901 · Cited by 6,435 · see citing works
Protein folding and misfolding
Dobson, C. M. (2003) · Nature · doi:10.1038/nature02261 · Cited by 4,791 · see citing works
Replica-exchange molecular dynamics method for protein folding
Sugita, Y., & Okamoto, Y. (1999) · Chemical Physics Letters · doi:10.1016/s0009-2614(99)01123-9 · Cited by 4,499 · see citing works
Fabrication of novel biomaterials through molecular self-assembly
Zhang, S. (2003) · Nature Biotechnology · doi:10.1038/nbt874 · Cited by 3,353 · see citing works
AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules
Pearlman, D. A., Case, D. A., Caldwell, J. W., Ross, W. S., Cheatham, T. E., DeBolt, S., Ferguson, D. M., Seibel, G., & Kollman, P. A. (1995) · Computer Physics Communications · doi:10.1016/0010-4655(95)00041-d · Cited by 3,129 · see citing works
Structure of the cross-β spine of amyloid-like fibrils
Nelson, R. A., Sawaya, M. R., Balbirnie, M., Madsen, A. Ø., Riekel, C., Grothe, R., & Eisenberg, D. S. (2005) · Nature · doi:10.1038/nature03680 · Cited by 2,205 · see citing works
Protein misfolding, evolution and disease
Dobson, C. M. (1999) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(99)01445-0 · Cited by 1,960 · see citing works
A structural model for Alzheimer's β-amyloid fibrils based on experimental constraints from solid state NMR
Petkova, A. T., Ishii, Y., Balbach, J. J., Antzutkin, O. N., Leapman, R. D., Delaglio, F., & Tycko, R. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.262663499 · Cited by 1,847 · see citing works
Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins
Fernández‐Escamilla, A. M., Rousseau, F., Schymkowitz, J. W. H., & Serrano, L. G. (2004) · Nature Biotechnology · doi:10.1038/nbt1012 · Cited by 1,663 · see citing works
Environment and exposure to solvent of protein atoms. Lysozyme and insulin
Shrake, A., & Rupley, J. A. (1973) · Journal of Molecular Biology · doi:10.1016/0022-2836(73)90011-9 · Cited by 1,387 · see citing works
Modification of the Generalized Born Model Suitable for Macromolecules
Onufriev, A. V., Bashford, D., & Case, D. A. (2000) · The Journal of Physical Chemistry B · doi:10.1021/jp994072s · Cited by 1,093 · see citing works
Rationalization of the effects of mutations on peptide andprotein aggregation rates
Chiti, F., Stefani, M., Taddei, N., Ramponi, G., & Dobson, C. M. (2003) · Nature · doi:10.1038/nature01891 · Cited by 1,072 · see citing works
AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides
Conchillo‐Solé, O., de Groot, N. S., Avilés, F. X., Vendrell, J., Daura, X., & Ventura, S. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-65 · Cited by 1,058 · see citing works
Assembly of τ protein into Alzheimer paired helical filaments depends on a local sequence motif ( <sup>306</sup> VQIVYK <sup>311</sup> ) forming β structure
von Bergen, M., Friedhoff, P., Biernat, J., Heberle, J., Mandelkow, E.-M., & Mandelkow, E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.10.5129 · Cited by 1,021 · see citing works
Protein aggregation and its inhibition in biopharmaceutics
Wang, W. (2005) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2004.11.014 · Cited by 990 · see citing works
Prediction of “Aggregation-prone” and “Aggregation-susceptible” Regions in Proteins Associated with Neurodegenerative Diseases
Pawar, A. P., DuBay, K. F., Zurdo, J., Chiti, F., Vendruscolo, M., & Dobson, C. M. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.04.016 · Cited by 585 · see citing works
Molecular structure of amyloid fibrils: insights from solid-state NMR
Tycko, R. (2006) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583506004173 · Cited by 529 · see citing works
Self-assembling peptides and proteins for nanotechnological applications
Rajagopal, K., & Schneider, J. P. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2004.06.006 · Cited by 476 · see citing works
α-Helical stabilization by side chain shielding of backbone hydrogen bonds
Garcı́a, A. E., & Sanbonmatsu, K. Y. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.042496899 · Cited by 442 · see citing works
Prediction of Aggregation-Prone Regions in Structured Proteins
Tartaglia, G. G., Pawar, A. P., Campioni, S., Dobson, C. M., Chiti, F., & Vendruscolo, M. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.05.013 · Cited by 437 · see citing works
The 3D profile method for identifying fibril-forming segments of proteins
Thompson, M. J., Sievers, S. A., Karanicolas, J., Ivanova, M. I., Baker, D. A., & Eisenberg, D. S. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0511295103 · Cited by 396 · see citing works
De novo designed peptide-based amyloid fibrils
de la Paz, M. L., Goldie, K. N., Zurdo, J., Lacroix, E., Dobson, C. M., Hoenger, A., & Serrano, L. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.252340199 · Cited by 379 · see citing works
Mapping Aβ Amyloid Fibril Secondary Structure Using Scanning Proline Mutagenesis
Williams, A. D., Portelius, E., Kheterpal, I., Guo, J., Cook, K. D., Xu, Y., & Wetzel, R. B. (2003) · Journal of Molecular Biology · doi:10.1016/j.jmb.2003.11.008 · Cited by 378 · see citing works
Progress towards a molecular-level structural understanding of amyloid fibrils
Tycko, R. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2003.12.002 · Cited by 374 · see citing works
Molecular dynamics simulations of spontaneous fibril formation by random-coil peptides
Nguyen, H. D., & Hall, C. K. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0407273101 · Cited by 368 · see citing works
A Molecular Model of Alzheimer Amyloid β-Peptide Fibril Formation
Tjernberg, L. O., Callaway, D. J. E., Tjernberg, A., Hahne, S., Lilliehöök, C., Terenius, L. Y., Thyberg, J., & Nordstedt, C. (1999) · Journal of Biological Chemistry · doi:10.1074/jbc.274.18.12619 · Cited by 360 · see citing works
Characterizing hydrophobicity of interfaces by using cavity formation, solute binding, and water correlations
Godawat, R., Jamadagni, S. N., & Garde, S. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0902778106 · Cited by 357 · see citing works
Prediction of the Absolute Aggregation Rates of Amyloidogenic Polypeptide Chains
DuBay, K. F., Pawar, A. P., Chiti, F., Zurdo, J., Dobson, C. M., & Vendruscolo, M. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.06.043 · Cited by 319 · see citing works
Emerging ideas on the molecular basis of protein and peptide aggregation
Thirumalai, D. (2003) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(03)00032-0 · Cited by 315 · see citing works
Prolines and Aamyloidogenicity in Fragments of the Alzheimer's Peptide .beta./A4
Wood, S. J., Wetzel, R. B., Martin, J. D., & Hurle, M. R. (1995) · Biochemistry · doi:10.1021/bi00003a003 · Cited by 313 · see citing works
Prediction of Amyloid Fibril-forming Proteins
Kallberg, Y., Gustafsson, M., Persson, B., Thyberg, J., & Johansson, J. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.m010402200 · Cited by 303 · see citing works
Principles, approaches, and challenges for predicting protein aggregation rates and shelf life.
Weiss, W. F., Young, T. M., & Roberts, C. (2009) · Journal of Pharmacy and Science · doi:10.1002/jps.21521 · Cited by 298 · see citing works
The role of side-chain interactions in the early steps of aggregation: Molecular dynamics simulations of an amyloid-forming peptide from the yeast prion Sup35
Gsponer, J. A., Haberthür, U., & Caflisch, A. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0835307100 · Cited by 264 · see citing works
The PASTA server for protein aggregation prediction
Trovato, A., Seno, F., & Tosatto, S. C. E. (2007) · Protein Engineering Design and Selection · doi:10.1093/protein/gzm042 · Cited by 244 · see citing works
The development/application of a ‘minimalist’ organic/biochemical molecular mechanic force field using a combination of ab initio calculations and experimental data
Kollman, P., Dixon, R., Cornell, W., Fox, T., Chipot, C., & Pohorille, A. (1997) · Computer Simulation of Biomolecular Systems · doi:10.1007/978-94-017-1120-3_2 · Cited by 242 · see citing works
Peptides as novel smart materials
Fairman, R., & Åkerfeldt, K. S. (2005) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2005.07.005 · Cited by 232 · see citing works
Replica exchange molecular dynamics simulations of amyloid peptide aggregation
Cecchini, M., Rao, F. V., Seeber, M., & Caflisch, A. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1809588 · Cited by 225 · see citing works
Simulations as analytical tools to understand protein aggregation and predict amyloid conformation
Ma, B., & Nussinov, R. (2006) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2006.08.018 · Cited by 223 · see citing works
Prediction of aggregation rate and aggregation‐prone segments in polypeptide sequences
Tartaglia, G. G., Cavalli, A., Pellarin, R., & Caflisch, A. (2005) · Protein Science · doi:10.1110/ps.051471205 · Cited by 214 · see citing works
Conformational Dynamics of Trialanine in Water. 2. Comparison of AMBER, CHARMM, GROMOS, and OPLS Force Fields to NMR and Infrared Experiments
Mu, Kosov, D. S., & Stock, G. (2003) · The Journal of Physical Chemistry B · doi:10.1021/jp022445a · Cited by 200 · see citing works
The role of aromaticity, exposed surface, and dipole moment in determining protein aggregation rates
Tartaglia, G. G., Cavalli, A., Pellarin, R., & Caflisch, A. (2004) · Protein Science · doi:10.1110/ps.04663504 · Cited by 181 · see citing works
The Structural Biology of Protein Aggregation Diseases: Fundamental Questions and Some Answers
Eisenberg, D. S., Nelson, R. A., Sawaya, M. R., Balbirnie, M., Sambashivan, S., Ivanova, M. I., Madsen, A. Ø., & Riekel, C. (2006) · Accounts of Chemical Research · doi:10.1021/ar0500618 · Cited by 179 · see citing works
Exploring protein aggregation and self‐propagation using lattice models: Phase diagram and kinetics
Dima, R. I., & Thirumalai, D. (2002) · Protein Science · doi:10.1110/ps.4220102 · Cited by 159 · see citing works
Protein folding by zipping and assembly
Ozkan, S. B., Wu, G. A., Chodera, J. D., & Dill, K. A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0703700104 · Cited by 147 · see citing works
A Test on Peptide Stability of AMBER Force Fields with Implicit Solvation
Shell, M. S., Ritterson, R. S., & Dill, K. A. (2008) · The Journal of Physical Chemistry B · doi:10.1021/jp800282x · Cited by 145 · see citing works
Self-citation
Self-assembly of ionic-complementary peptides: a physicochemical viewpoint
Chen, P. (2005) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2004.12.048 · Cited by 142 · see citing works
Peptide self-assembly at the nanoscale: a challenging target for computational and experimental biotechnology
Colombo, G., Soto, P., & Gazit, E. (2007) · Trends in biotechnology · doi:10.1016/j.tibtech.2007.03.004 · Cited by 142 · see citing works
Simulations of nucleation and elongation of amyloid fibrils
Zhang, J., & Muthukumar, M. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3050295 · Cited by 130 · see citing works
Computational models for the prediction of polypeptide aggregation propensity
Caflisch, A. (2006) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2006.07.009 · Cited by 129 · see citing works
Short Peptide Amyloid Organization: Stabilities and Conformations of the Islet Amyloid Peptide NFGAIL
Zanuy, D., Ma, B., & Nussinov, R. (2003) · Biophysical Journal · doi:10.1016/s0006-3495(03)74996-0 · Cited by 120 · see citing works
Investigation of Salt Bridge Stability in a Generalized Born Solvent Model
Geney, R., Layten, M., Gomperts, R., Horn̆ák, V., & Simmerling, C. L. (2005) · Journal of Chemical Theory and Computation · doi:10.1021/ct050183l · Cited by 117 · see citing works
Formation and Growth of Oligomers: A Monte Carlo Study of an Amyloid Tau Fragment
Li, D., Mohanty, S., Irbäck, A., & Huo, S. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000238 · Cited by 116 · see citing works
Energy landscape of amyloidogenic peptide oligomerization by parallel-tempering molecular dynamics simulation: Significant role of Asn ladder
Tsai, H. G., Reches, M., Tsai, C., Gunasekaran, K., Gazit, E., & Nussinov, R. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408653102 · Cited by 115 · see citing works
Protein refolding Versus aggregation: computer simulations on an intermediate-resolution protein model
Smith, A. V., & Hall, C. K. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.4845 · Cited by 110 · see citing works
Stability and Structure of Oligomers of the Alzheimer Peptide Aβ16–22: From the Dimer to the 32-Mer
Röhrig, U. F., Laio, A., Tantalo, N., Parrinello, M., & Roberto Petronzio, A. (2006) · Biophysical Journal · doi:10.1529/biophysj.106.088542 · Cited by 106 · see citing works
Self-assembly of β-sheet forming peptides into chiral fibrillar aggregates
Bellesia, G., & Shea, J. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2739547 · Cited by 101 · see citing works
Design and application of stimulus-responsive peptide systems
Chockalingam, K., Blenner, M., & Banta, S. A. (2007) · Protein Engineering Design and Selection · doi:10.1093/protein/gzm008 · Cited by 98 · see citing works
Folding Very Short Peptides Using Molecular Dynamics
Ho, B. K., & Dill, K. A. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020027 · Cited by 97 · see citing works
Discrete molecular dynamics simulations of peptide aggregation
Peng, S., Ding, F., Urbanc, B., Buldyrev, S. V., Cruz, L., Stanley, E., & Dokholyan, N. V. (2004) · Physical Review E · doi:10.1103/physreve.69.041908 · Cited by 97 · see citing works
Replica Exchange Simulations of the Thermodynamics of Aβ Fibril Growth
Takeda, T., & Klimov, D. K. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.10.008 · Cited by 95 · see citing works
A Molecular Dynamics Approach to the Structural Characterization of Amyloid Aggregation
Cecchini, M., Curcio, R., Pappalardo, M., Melki, R., & Caflisch, A. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.01.009 · Cited by 93 · see citing works
Sequence Dependence of Amyloid Fibril Formation: Insights from Molecular Dynamics Simulations
de la Paz, M. L., Mori, G., Serrano, L. G., & Colombo, G. (2005) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.03.081 · Cited by 81 · see citing works
Sampling the Self-Assembly Pathways of KFFE Hexamers
Wei, G., Mousseau, N., & Derreumaux, P. (2004) · Biophysical Journal · doi:10.1529/biophysj.104.047688 · Cited by 78 · see citing works
Engineering peptides and proteins that undergo α-to-β transitions
Mihara, H., & Takahashi, Y. (1997) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(97)80113-3 · Cited by 75 · see citing works
Protein aggregation in silico
Cellmer, T., Bratko, D., Prausnitz, J. M., & Blanch, H. W. (2007) · Trends in biotechnology · doi:10.1016/j.tibtech.2007.03.011 · Cited by 69 · see citing works
What Determines the Structure and Stability of KFFE Monomers, Dimers, and Protofibrils?
Bellesia, G., & Shea, J. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.10.040 · Cited by 62 · see citing works
Molecular modeling of the core of Aβ amyloid fibrils
Guo, J., Wetzel, R. B., & Xu, Y. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20222 · Cited by 62 · see citing works
Recent Structural and Computational Insights into Conformational Diseases
Fernàndez‐Busquets, X., de Groot, N. S., Fernández, D., & Ventura, S. (2008) · Current Medicinal Chemistry · doi:10.2174/092986708784534938 · Cited by 60 · see citing works
Peptide free-energy profile is strongly dependent on the force field: Comparison of C96 and AMBER95
Ono, S., Nakajima, N., Higo, J., & Nakamura, H. (2000) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(20000715)21:9 · Cited by 59 · see citing works
Free Energy Landscapes for Amyloidogenic Tetrapeptides Dimerization
Baumketner, A., & Shea, J. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.059196 · Cited by 57 · see citing works
The Alzheimer β-amyloid (Aβ1–39) dimer in an implicit solvent
Anand, P., Nandel, F. S., & Hansmann, U. H. E. (2008) · The Journal of Chemical Physics · doi:10.1063/1.3021062 · Cited by 50 · see citing works
Sequence and structural determinants of Cu, Zn superoxide dismutase aggregation
Khare, S. D., Wilcox, K. C., Gong, P., & Dokholyan, N. V. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20629 · Cited by 47 · see citing works
Blind Test of Physics-Based Prediction of Protein Structures
Shell, M. S., Ozkan, S. B., Voelz, V. A., Wu, G.-H., & Dill, K. A. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.11.009 · Cited by 45 · see citing works
Self-citation
Coarse-Grained Strategy for Modeling Protein Stability in Concentrated Solutions
Cheung, J. K., & Truskett, T. M. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.062067 · Cited by 44 · see citing works
Compactness, aggregation, and prionlike behavior of protein: A lattice model study
GIUGLIARELLI, G., Micheletti, C., Banavar, J. R., & Maritan, A. (2000) · The Journal of Chemical Physics · doi:10.1063/1.1289463 · Cited by 42 · see citing works
Convergence and Heterogeneity in Peptide Folding with Replica Exchange Molecular Dynamics
Lin, E. I., & Shell, M. S. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct900119n · Cited by 41 · see citing works
Self-citation
Molecular Dynamics Simulation Suggests Possible Interaction Patterns at Early Steps of β2-Microglobulin Aggregation
Fogolari, F., Corazza, A., Viglino, P., Zuccato, P., Pieri, L., Faccioli, P., Bellotti, V., & Esposito, G. (2006) · Biophysical Journal · doi:10.1529/biophysj.106.098483 · Cited by 37 · see citing works
Predicting Peptide Structures in Native Proteins from Physical Simulations of Fragments
Voelz, V. A., Shell, M. S., & Dill, K. A. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000281 · Cited by 36 · see citing works
Self-citation
Coarse-Grained Strategy for Modeling Protein Stability in Concentrated Solutions. II: Phase Behavior
Shen, V. K., Cheung, J. K., Errington, J. R., & Truskett, T. M. (2005) · Biophysical Journal · doi:10.1529/biophysj.105.076497 · Cited by 36 · see citing works
Self-assembly of peptides into a β-barrel motif
Friedel, M., & Shea, J. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1649934 · Cited by 31 · see citing works
Folding of a Highly Conserved Diverging Turn Motif from the SH3 Domain
Gnanakaran, S., & Garcı́a, A. E. (2003) · Biophysical Journal · doi:10.1016/s0006-3495(03)74966-2 · Cited by 26 · see citing works
Rapid Free Energy Calculation of Peptide Self-Assembly by REMD Umbrella Sampling
Wolf, M. G., Jongejan, J. A., Laman, J. D., & de Leeuw, S. W. (2008) · The Journal of Physical Chemistry B · doi:10.1021/jp804285e · Cited by 24 · see citing works
Ion-Enhanced Etching of Si(100) with Molecular Chlorine: Neutral and Ionic Product Yields as a Function of Ion Kinetic Energy and Molecular Chlorine Flux
Materer, N. F., Goodman, R. S., & Leone, S. R. (2000) · The Journal of Physical Chemistry B · doi:10.1021/jp993278q · Cited by 6 · see citing works

Flagged first, then by citations

Sorted by citations

Hydrophobicity of Proteins and Interfaces: Insights from Density Fluctuations
Jamadagni, S. N., Godawat, R., & Garde, S. (2011) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-061010-114156 · Cited by 231 · see citing works
Nanomaterials design and tests for neural tissue engineering
Saracino, G. A. A., Cigognini, D., Silva, D., Caprini, A., & Gelain, F. (2012) · Chemical Society Reviews · doi:10.1039/c2cs35065c · Cited by 170 · see citing works
Molecular Simulations Indicate Marked Differences in the Structure of Amylin Mutants, Correlated with Known Aggregation Propensity
Miller, C. M., Zerze, G. H., & Mittal, J. (2013) · The Journal of Physical Chemistry B · doi:10.1021/jp409755y · Cited by 48 · see citing works
Characterizing the Structural Behavior of Selected Aβ-42 Monomers with Different Solubilities
Velez‐Vega, C., & Escobedo, F. A. (2011) · The Journal of Physical Chemistry B · doi:10.1021/jp1086575 · Cited by 46 · see citing works
Factors That Drive Peptide Assembly from Native to Amyloid Structures: Experimental and Theoretical Analysis of [Leu-5]-Enkephalin Mutants
Do, T. D., LaPointe, N. E., Sangwan, S., Teplow, D. B., Feinstein, S. C., Sawaya, M. R., Eisenberg, D. S., & Bowers, M. T. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp502473s · Cited by 29 · see citing works
Zn2+ Effect on Structure and Residual Hydrophobicity of Amyloid β-Peptide Monomers
Shi, H., Kang, B., & Lee, J. Y. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp504779m · Cited by 29 · see citing works
Are AMBER Force Fields and Implicit Solvation Models Additive? A Folding Study with a Balanced Peptide Test Set
Robinson, M. K., Monroe, J. I., & Shell, M. S. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00788 · Cited by 28 · see citing works
Self-citation
Fibrillization Propensity for Short Designed Hexapeptides Predicted by Computer Simulation
Wagoner, V. A., Cheon, M., Chang, I., & Hall, C. K. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2011.12.038 · Cited by 27 · see citing works
Predicting unfolding thermodynamics and stable intermediates for alanine-rich helical peptides with the aid of coarse-grained molecular simulation
Calero‐Rubio, C., Paik, B. A., Jia, X., Kiick, K. L., & Roberts, C. J. (2016) · Biophysical Chemistry · doi:10.1016/j.bpc.2016.07.002 · Cited by 14 · see citing works
Helix-coil transition and conformational deformity in Aβ42-monomer: a case study using the Zn2+ cation
Chowdhury, U. D., & Bhargava, B. L. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1927190 · Cited by 4 · see citing works
Assessment of the Topology and Oligomerisation States of Coiled Coils Using Metadynamics with Conformational Restraints
Notari, E., Wood, C. W., & Michel, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01695 · Cited by 4 · see citing works
Can Simple Interaction Models Explain Sequence-Dependent Effects in Peptide Homodimerization?
SMITH, D. J. H., & Shell, M. S. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b03186 · Cited by 3 · see citing works
Self-citation

Sequence dependent aggregation of peptides and fibril formation
Hung, N. B., Le, D.-M., & Hoàng, T. X. (2017) · The Journal of Chemical Physics · doi:10.1063/1.5001517 · Cited by 14 · see citing works
Prediction of Aggregation-Prone Regions in Structured Proteins
Tartaglia, G. G., Pawar, A. P., Campioni, S., Dobson, C. M., Chiti, F., & Vendruscolo, M. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.05.013 · Cited by 437 · see citing works
Available online
Tartaglia, G. G., Pawar, A. P., Campioni, S., Dobson, C. M., Chiti, F., Vendruscolo, M., Scienze, D. D., & Morgagni, V. (2008)
Computational models for the prediction of polypeptide aggregation propensity
Caflisch, A. (2006) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2006.07.009 · Cited by 129 · see citing works
Aggregation of peptides in the tube model with correlated sidechain orientations
Hung, N. B., & Hoàng, T. X. (2015) · Journal of Physics Conference Series · doi:10.1088/1742-6596/627/1/012028 · Cited by 1 · see citing works

Authors: Lin, E. I., & Shell, M. S.
Affiliations: University of California, Santa Barbara (US)
Corresponding author: Shell, M. Scott
ORCID: Shell, M. Scott
Venue: The Journal of Physical Chemistry B
Publisher: American Chemical Society (ACS)
Published: 2010-08-23
Volume: 114
Issue: 36
Pages: 11899-11908
ISSN: 1520-5207 (ISSN-L)
1520-6106
DOI: 10.1021/jp104114n
OpenAlex: W2003101786
Cited by: 12
References: 85
Citations by year: 2025: 1 · 2021: 1 · 2017: 1 · 2016: 2 · 2014: 2 · 2013: 1 · 2012: 1
FWCI: 0.65
Type: journal article
Language: en
Open access: closed
Locations: The Journal of Physical Chemistry B (publishedVersion)
PubMed (publishedVersion)
Funders: Division of Chemical, Bioengineering, Environmental, and Transport Systems
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
Keywords: peptide folding, peptide aggregation, amyloid fibril formation, aggregation propensity, hydrophobic interactions, conformational ensembles, hydrophobicity, peptide therapeutics, aggregation-prone regions, logistic regression prediction
MeSH terms: Peptides, Protein Conformation, Protein Folding, Databases, Protein, Static Electricity, Molecular Dynamics Simulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Can Peptide Folding Simulations Provide Predictive Information for Aggregation Propensity?

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks