Fetching the paper…
Reading the bibliography…
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost.
Levitt, M., Warshel, A.: Computer simulation of protein folding. Nature 253
1975
Earlier work this paper cites.
McCammon, J.A., Gelin, B.R., Karplus, M.: Dynamics of folded proteins. Nature 267
1977
Earlier work this paper cites.
Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22
1983
Earlier work this paper cites.
García, A.E.: Large-amplitude nonlinear motions in proteins. Phys. Rev. Lett. 68
1992
Earlier work this paper cites.
Clarke, N.D., Kissinger, C.R., Desjarlais, J., Gilliland, G.L., Pabo, C.O.: Structural studies of the engrailed homeodomain. Protein Sci. 3
1994
Earlier work this paper cites.
Lindahl, M., Svensson, L.A., Liljas, A., Sedelnikova, S.E., Eliseikina, I.A., Fomenkova, N.P., Nevskaya, N., Nikonov, S.V., Garber, M.B., Muranova, T.A.: Crystal structure of the ribosomal protein s6 from thermus thermophilus. EMBO J. 13
1994
Earlier work this paper cites.
Li, A., Daggett, V.: Characterization of the transition state of protein unfolding by use of molecular dynamics: chymotrypsin inhibitor 2. Proc. Natl. Acad. Sci. U.S.A. 91
1994
Earlier work this paper cites.
Itzhaki, L.S., Otzen, D.E., Fersht, A.R.: The structure of the transition state for folding of chymotrypsin inhibitor 2 analysed by protein engineering methods: evidence for a nucleation-condensation mechanism for protein folding. J. Mol. Biol. 254
1995
Earlier work this paper cites.
Onuchic, J.N., Luthey-Schulten, Z., Wolynes, P.G.: Theory of protein folding: the energy landscape perspective. Annu. Rev. Phys. Chem. 48
1997
Earlier work this paper cites.
Ben-Naim, A.: Statistical potentials extracted from protein structures: Are these meaningful potentials? J. Chem. Phys. 107
1997
Earlier work this paper cites.
Martinez, J.C., Pisabarro, M.T., Serrano, L.: Obligatory steps in protein folding and the conformational diversity of the transition state. Nat. Struct. Biol. 5
1998
Earlier work this paper cites.
Vendruscolo, M., Domany, E.: Pairwise contact potentials are unsuitable for protein folding. J. Chem. Phys. 109
1998
Earlier work this paper cites.
Walsh, S.T.R., Cheng, H., Bryson, J.W., Roder, H., DeGrado, W.F.: Solution structure and dynamics of a de novo designed three-helix bundle protein. Proc. Natl. Acad. Sci. U.S.A. 96
1999
Earlier work this paper cites.
Riddle, D.S., Grantcharova, V.P., Santiago, J.V., Alm, E., Ruczinski, I., Baker, D.: Experiment and theory highlight role of native state topology in sh3 folding. Nat. Struct. Biol. 6
1999
Earlier work this paper cites.
Zemla, A., Venclovas, C., Moult, J., Fidelis, K.: Processing and analysis of casp3 protein structure predictions. Proteins 37
1999
Earlier work this paper cites.
Ferrara, P., Apostolakis, J., Caflisch, A.: Thermodynamics and kinetics of folding of two model peptides investigated by molecular dynamics simulations. J. Phys. Chem. B 104
2000
Earlier work this paper cites.
Clementi, C., Nymeyer, H., Onuchic, J.N.: Topological and energetic factors: what determines the structural details of the transition state ensemble and “en-route” intermediates for protein folding? an investigation for small globular proteins. J. Mol. Biol. 298
2000
Earlier work this paper cites.
Clementi, C., Nymeyer, H., Onuchic, J.N.: Topological and energetic factors: what determines the structural details of the transition state ensemble and “en-route” intermediates for protein folding? an investigation for small globular proteins. J. Mol. Biol. 298
2000
Earlier work this paper cites.
Sarisky, C.A., Mayo, S.L.: The β \beta β \beta α \alpha fold: explorations in sequence space. J. Mol. Biol. 307
2001
Earlier work this paper cites.
Liwo, A., Czaplewski, C., Pillardy, J., Scheraga, H.A.: Cumulant-based expressions for the multibody terms for the correlation between local and electrostatic interactions in the united-residue force field. J. Chem. Phys. 115
2001
Earlier work this paper cites.
Pillardy, J., Czaplewski, C., Liwo, A., Wedemeyer, W.J., Lee, J., Ripoll, D.R., Arlukowicz, P., Oldziej, S., Arnautova, Y.A., Scheraga, H.A.: Development of physics-based energy functions that predict medium-resolution structures for proteins of the α \alpha , β \beta , and α / β \alpha/\beta structural classes. J. Phys. Chem. B 105
2001
Earlier work this paper cites.
Otzen, D.E., Oliveberg, M.: Conformational plasticity in folding of the split β \beta - α \alpha - β \beta protein s6: evidence for burst-phase disruption of the native state. J. Mol. Biol. 317
2002
Earlier work this paper cites.
Sheinerman, F.B., Honig, B.: On the role of electrostatic interactions in the design of protein–protein interfaces. J. Mol. Biol. 318
2002
Earlier work this paper cites.
Hubner, I.A., Oliveberg, M., Shakhnovich, E.I.: Simulation, experiment, and evolution: understanding nucleation in protein s6 folding. Proc. Natl. Acad. Sci. U.S.A. 101
2004
Earlier work this paper cites.
Ejtehadi, M., Avall, S., Plotkin, S.: Three-body interactions improve the prediction of rate and mechanism in protein folding models. Proc. Natl. Acad. Sci. U.S.A. 101
2004
Earlier work this paper cites.
Izvekov, S., Voth, G.A.: A multiscale coarse-graining method for biomolecular systems. J. Phys. Chem. B 109
2005
Earlier work this paper cites.
Izvekov, S., Voth, G.A.: Multiscale coarse graining of liquid-state systems. J. Chem. Phys. 123
2005
Earlier work this paper cites.
Chiu, T.K., Kubelka, J., Herbst-Irmer, R., Eaton, W.A., Hofrichter, J., Davies, D.R.: High-resolution x-ray crystal structures of the villin headpiece subdomain, an ultrafast folding protein. Proc. Natl. Acad. Sci. U.S.A. 102
2005
Earlier work this paper cites.
Izvekov, S., Voth, G.A.: Multiscale coarse-graining of mixed phospholipid/cholesterol bilayers. J. Chem. Theory Comput. 2
2006
Earlier work this paper cites.
Zhou, J., Thorpe, I.F., Izvekov, S., Voth, G.A.: Coarse-grained peptide modeling using a systematic multiscale approach. Biophys. J. 92
2007
Earlier work this paper cites.
Lindberg, M.O., Oliveberg, M.: Malleability of protein folding pathways: a simple reason for complex behaviour. Curr. Opin. Struct. Biol. 17
2007
Earlier work this paper cites.
Freddolino, P.L., Liu, F., Gruebele, M., Schulten, K.: Ten-microsecond molecular dynamics simulation of a fast-folding ww domain. Biophys. J. 94
2008
Earlier work this paper cites.
Noid, W.G., Chu, J.-W., Ayton, G.S., Krishna, V., Izvekov, S., Voth, G.A., Das, A., Andersen, H.C.: The multiscale coarse-graining method. i. a rigorous bridge between atomistic and coarse-grained models. J. Chem. Phys. 128
2008
Earlier work this paper cites.
Thorpe, I.F., Zhou, J., Voth, G.A.: Peptide folding using multiscale coarse-grained models. J. Phys. Chem. B 112
2008
Earlier work this paper cites.
Honda, S., Akiba, T., Kato, Y.S., Sawada, Y., Sekijima, M., Ishimura, M., Ooishi, A., Watanabe, H., Odahara, T., Harata, K.: Crystal structure of a ten-amino acid protein. J. Am. Chem. Soc. 130
2008
Earlier work this paper cites.
Barua, B., Lin, J.C., Williams, V.D., Kummler, P., Neidigh, J.W., Andersen, N.H.: The trp-cage: optimizing the stability of a globular miniprotein. Prot. Eng. Des. Sel. 21
2008
Earlier work this paper cites.
Wang, H., Junghans, C., Kremer, K.: Comparative atomistic and coarse-grained study of water: What do we lose by coarse-graining? Eur. Phys. J. E 28
2009
Earlier work this paper cites.
Molinero, V., Moore, E.B.: Water modeled as an intermediate element between carbon and silicon. J. Phys. Chem. B 113
2009
Earlier work this paper cites.
Krishna, V., Noid, W.G., Voth, G.A.: The multiscale coarse-graining method. iv. transferring coarse-grained potentials between temperatures. J. Chem. Phys. 131
2009
Earlier work this paper cites.
Shaw, D.E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R.O., Eastwood, M.P., Bank, J.A., Jumper, J.M., Salmon, J.K., Shan, Y., Wriggers, W.: Atomic-level characterization of the structural dynamics of proteins. Science 330
2010
Cited alongside, same era.
Voelz, V.A., Bowman, G.R., Beauchamp, K., Pande, V.S.: Molecular simulation of ab initio protein folding for a millisecond folder ntl9 (1- 39). J. Am. Chem. Soc. 132
2010
Cited alongside, same era.
Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J.L., Dror, R.O., Shaw, D.E.: Improved side-chain torsion potentials for the amber ff99sb protein force field. Proteins 78
2010
Cited alongside, same era.
Jr, R.D.H., Lu, L., Voth, G.A.: Multiscale coarse-graining of the protein energy landscape. PLOS Comput. Bio. 6
2010
Cited alongside, same era.
Jin, J., Pak, A.J., Voth, G.A.: Understanding missing entropy in coarse-grained systems: Addressing issues of representability and transferability. J. Phys. Chem. Lett. 10
2019
Later among the works it cites.
Lebold, K.M., Noid, W.: Dual approach for effective potentials that accurately model structure and energetics. J. Chem. Phys. 150
2019
Later among the works it cites.
Wang, W., Gómez-Bombarelli, R.: Coarse-graining auto-encoders for molecular dynamics. npj Comput. Mater. 5
2019
Later among the works it cites.
Husic, B.E., Charron, N.E., Lemm, D., Wang, J., Pérez, A., Krämer, A., Chen, Y., Olsson, S., Fabritiis, G., Noé, F., Clementi, C.: Coarse graining molecular dynamics with graph neural networks. J. Chem. Phys. 153
2020
Later among the works it cites.
Ruza, J., Wang, W., Schwalbe-Koda, D., Axelrod, S., Harris, W.H., Gómez-Bombarelli, R.: Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks. J. Chem. Phys. 153
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2010
Cited alongside, same era.
Tozzini, V.: Minimalist models for proteins: a comparative analysis. Q. Rev. Biophys. 43
2010
Cited alongside, same era.
Izvekov, S., Chung, P.W., Rice, B.M.: The multiscale coarse-graining method: Assessing its accuracy and introducing density dependent coarse-grain potentials. J. Chem. Phys. 133
2010
Cited alongside, same era.
Das, A., Andersen, H.C.: The multiscale coarse-graining method. v. isothermal-isobaric ensemble. J. Chem. Phys. 132
2010
Cited alongside, same era.
Lindorff-Larsen, K., Piana, S., Dror, R.O., Shaw, D.E.: How fast-folding proteins fold. Science 334
2011
Cited alongside, same era.
Thorpe, I.F., Goldenberg, D.P., Voth, G.A.: Exploration of transferability in multiscale coarse-grained peptide models. J. Phys. Chem. B 115
2011
Cited alongside, same era.
Prinz, J.-H., Wu, H., Sarich, M., Keller, B., Senne, M., Held, M., Chodera, J.D., Schütte, C., Noé, F.: Markov models of molecular kinetics: Generation and validation. J. Chem. Phys. 134
2011
Cited alongside, same era.
Zimmermann, M.T., Leelananda, S.P., Gniewek, P., Feng, Y., Jernigan, R.L., Kloczkowski, A.: Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models. J. Struct. Funct. Genomics 12
2011
Cited alongside, same era.
2020
Later among the works it cites.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S.A.A., Ballard, A.J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A.W., Kavukcuoglu, K., Kohli, P., Hassabis, D.: Highly accurate protein structure prediction with AlphaFold. Nature 596
2021
Later among the works it cites.
Souza, P.C.T., Alessandri, R., Barnoud, J., Thallmair, S., Faustino, I., Grünewald, F., Patmanidis, I., Abdizadeh, H., Bruininks, B.M.H., Wassenaar, T.A., Kroon, P.C., Melcr, J., Nieto, V., Corradi, V., Khan, H.M., Domański, J., Javanainen, M., Martinez-Seara, H., Reuter, N., Best, R.B., Vattulainen, I., Monticelli, L., Periole, X., Tieleman, D.P., Vries, A.H., Marrink, S.J.: Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18
2021
Later among the works it cites.
Joshi, S.Y., Deshmukh, S.A.: A review of advancements in coarse-grained molecular dynamics simulations. Mol. Simul. 47
2021
Later among the works it cites.
Giulini, M., Rigoli, M., Mattiotti, G., Menichetti, R., Tarenzi, T., Fiorentini, R., Potestio, R.: From system modeling to system analysis: The impact of resolution level and resolution distribution in the computer-aided investigation of biomolecules. Front. Mol. Biosci. 8
2021
Later among the works it cites.
Wang, J., Charron, N., Husic, B., Olsson, S., Noé, F., Clementi, C.: Multi-body effects in a coarse-grained protein force field. J. Chem. Phys. 154
2021
Later among the works it cites.
Chen, Y., Krämer, A., Charron, N.E., Husic, B.E., Clementi, C., Noé, F.: Machine learning implicit solvation for molecular dynamics. J. Chem. Phys. 155
2021
Later among the works it cites.
Ko, T.W., Finkler, J.A., Goedecker, S., Behler, J.: A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer. Nat. Commun. 12
2021
Later among the works it cites.
Unke, O.T., Chmiela, S., Gastegger, M., Schütt, K.T., Sauceda, H.E., Müller, K.-R.: Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects. Nat. Commun. 12
2021
Later among the works it cites.
Kidder, K.M., Szukalo, R.J., Noid, W.: Energetic and entropic considerations for coarse-graining. Eur. Phys. J. B 94
2021
Later among the works it cites.
Pretti, E., Shell, M.S.: A microcanonical approach to temperature-transferable coarse-grained models using the relative entropy. J. Chem. Phys. 155
2021
Later among the works it cites.
Robustelli, P., Ibanez-de-Opakua, A., Campbell-Bezat, C., Giordanetto, F., Becker, S., Zweckstetter, M., Pan, A.C., Shaw, D.E.: Molecular basis of small-molecule binding to α \alpha -synuclein. J. Am. Chem. Soc. 144
2022
Later among the works it cites.
2022
Later among the works it cites.
Oliveira Jr, A.B., Contessoto, V.G., Hassan, A., Byju, S., Wang, A., Wang, Y., Dodero-Rojas, E., Mohanty, U., Noel, J.K., Onuchic, J.N., Whitford, P.C.: Smog 2 and opensmog: Extending the limits of structure-based models. Protein Sci. 31
2022
Later among the works it cites.
Jin, J., Pak, A.J., Durumeric, A.E.P., Loose, T.D., Voth, G.A.: Bottom-up coarse-graining: Principles and perspectives. J. Chem. Theory Comput. 18
2022
Later among the works it cites.
Ding, X., Zhang, B.: Contrastive learning of coarse-grained force fields. J. Chem. Theory Comput. 18
2022
Later among the works it cites.
Frank, T., Unke, O., Müller, K.-R.: So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems. Adv. Neural Inf. Process. Syst. 35
2022
Later among the works it cites.
Thölke, P., Fabritiis, G.D.: Equivariant transformers for neural network based molecular potentials. In: International Conference on Learning Representations (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J.P., Kornbluth, M., Molinari, N., Smidt, T.E., Kozinsky, B.: E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13
2022
Later among the works it cites.
Batatia, I., Kovacs, D.P., Simm, G., Ortner, C., Csányi, G.: Mace: Higher order equivariant message passing neural networks for fast and accurate force fields. Adv. Neur. In. 35
2022
Later among the works it cites.
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., Rives, A.: Language models of protein sequences at the scale of evolution enable accurate structure prediction. Science 379
2023
Closest in time.
2023
Closest in time.
Noid, W.G.: Perspective: Advances, challenges, and insight for predictive coarse-grained models. J. Phys. Chem. B (2023)
2023
Closest in time.
Majewski, M., Pérez, A., Thölke, P., Doerr, S., Charron, N.E., Giorgino, T., Husic, B.E., Clementi, C., Noé, F., De Fabritiis, G.: Machine learning coarse-grained potentials of protein thermodynamics. Nat. Commun. 14
2023
Closest in time.
Köhler, J., Chen, Y., Krämer, A., Clementi, C., Noé, F.: Flow-matching: Efficient coarse-graining of molecular dynamics without forces. J. Chem. Theory Comput. 19
2023
Closest in time.
Chennakesavalu, S., Toomer, D.J., Rotskoff, G.M.: Ensuring thermodynamic consistency with invertible coarse-graining. J. Chem. Phys. 158
2023
Closest in time.
Krämer, A., Durumeric, A.E.P., Charron, N.E., Chen, Y., Clementi, C., Noé, F.: Statistically optimal force aggregation for coarse-graining molecular dynamics. J. Phys. Chem. Lett. 14
2023
Closest in time.
Wellawatte, G.P., Hocky, G.M., White, A.D.: Neural potentials of proteins extrapolate beyond training data. J. Chem. Phys. 159
2023
Closest in time.
Airas, J., Ding, X., Zhang, B.: Transferable coarse graining via contrastive learning of graph neural networks. bioRxiv, 2023–09 (2023)
2023
Closest in time.
Marloes Arts, V.G.S., Huang, C.-W., Zügner, D., Federici, M., Clementi, C., Noé, F., Pinsler, R., Berg, R.: Two for one: Diffusion models and force fields for coarse-grained molecular dynamics. J. Chem. Theory Comput. 19
2023
Closest in time.
Durumeric, A.E.P., Charron, N.E., Templeton, C., Musil, F., Bonneau, K., Pasos-Trejo, A.S., Chen, Y., Kelkar, A., Noé, F., Clementi, C.: Machine learned coarse-grained protein force-fields: Are we there yet? Curr. Opin. Struc. Biol. 79
2023
Closest in time.
Zaporozhets, I., Clementi, C.: Multibody terms in protein coarse-grained models: A top-down perspective. J. Phys. Chem. B 127
2023
Closest in time.