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AWSEM-MD: Protein Structure Prediction Using Coarse-Grained Physical Potentials and Bioinformatically Based Local Structure Biasing
Open accessAbstract
The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained protein force field. AWSEM contains physically motivated terms, such as hydrogen bonding, as well as a bioinformatically based local structure biasing term, which efficiently takes into account many-body effects that are modulated by the local sequence. When combined with appropriate local or global alignments to choose memories, AWSEM can be used to perform de novo protein structure prediction. Herein we present structure prediction results for a particular choice of local sequence alignment method based on short residue sequences called fragments. We demonstrate the model's structure prediction capabilities for three levels of global homology between the target sequence and those proteins used for local structure biasing, all of which assume that the structure of the target sequence is not known. When there are no homologues in the database of structures used for local structure biasing, AWSEM calculations produce structural predictions that are somewhat improved compared with prior works using related approaches. The inclusion of a small number of structures from homologous sequences improves structure prediction only marginally, but when the fragment search is restricted to only homologous sequences, AWSEM can perform high resolution structure prediction and can be used for kinetics and dynamics studies.
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Cites
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Comparative Protein Modelling by Satisfaction of Spatial Restraints
Šali, A., & Blundell, T. L. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1626 · Cited by 12,977
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Some Factors in the Interpretation of Protein Denaturation
Kauzmann, W. (1959) · Advances in Protein Chemistry · doi:10.1016/s0065-3233(08)60608-7 · Cited by 4,870
Comparative Protein Structure Modeling of Genes and Genomes
Martí-Renom, M. A., Stuart, A. C., Fiser, A., Sánchez, R., Melo, F., & Šali, A. (2000) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.29.1.291 · Cited by 3,169
The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain
Pauling, L., Corey, R. B., & Branson, H. R. (1951) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.37.4.205 · Cited by 2,915
Funnels, pathways, and the energy landscape of protein folding: A synthesis
Bryngelson, J. D., Onuchic, J. N., Socci, N. D., & Wolynes, P. G. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340210302 · Cited by 2,648
Self-citationModeling of loops in protein structures
Fiser, A., Do, R. K. G., & Šali, A. (2000) · Protein Science · doi:10.1110/ps.9.9.1753 · Cited by 2,116
Protein structure alignment by incremental combinatorial extension (CE) of the optimal path
Shindyalov, I. N., & Bourne, P. E. (1998) · Protein Engineering Design and Selection · doi:10.1093/protein/11.9.739 · Cited by 2,041
Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding
Fersht, A. (2017) · Series in Structural Biology · doi:10.1142/10574 · Cited by 2,039
Atomic-level characterization of the structural dynamics of proteins
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., et al. (2010) · Science · doi:10.1126/science.1187409 · Cited by 1,923
Structure and Mechanism in Protein Science. A guide to Enzyme Catalysis and Protein Folding
Howland, J. L. (2001) · Biochemistry and Molecular Biology Education · Cited by 1,823
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Kuhlman, B., Dantas, G., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,670
PISCES: a protein sequence culling server
Wang, G., & Dunbrack, R. L. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg224 · Cited by 1,662
Noid, W. G., Chu, J.-W., Ayton, G. S., Krishna, V., Izvekov, S., Voth, G. A., Das, A., & Andersen, H. C. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2938860 · Cited by 875
Pauling, L., & Niemann, C. (1939) · Journal of the American Chemical Society · doi:10.1021/ja01876a065 · Cited by 362
Optimal protein-folding codes from spin-glass theory.
Goldstein, R. A., Luthey-Schulten, Z. A., & Wolynes, P. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.11.4918 · Cited by 344
Self-citationWater in protein structure prediction
Papoian, G. A., Ulander, J., Eastwood, M. P., Luthey-Schulten, Z., & Wolynes, P. G. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307851100 · Cited by 313
Self-citationHopfield, J. J. (1988) · National Academy of Sciences eBooks · Cited by 282
Protein tertiary structure recognition using optimized Hamiltonians with local interactions.
Goldstein, R. A., Luthey-Schulten, Z. A., & Wolynes, P. G. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.19.9029 · Cited by 220
Self-citationRole of Water Mediated Interactions in Protein−Protein Recognition Landscapes
Papoian, G. A., Ulander, J., & Wolynes, P. G. (2003) · Journal of the American Chemical Society · doi:10.1021/ja034729u · Cited by 191
Self-citationEnergy landscapes and solved protein–folding problems
Wolynes, P. G. (2005) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2004.1502 · Cited by 173
Self-citationChemically accurate coarse graining of double-stranded DNA
Savelyev, A., & Papoian, G. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1001163107 · Cited by 163
Self-citationToward Protein Tertiary Structure Recognition by Means of Associative Memory Hamiltonians
Friedrichs, M. S., & Wolynes, P. G. (1989) · Science · doi:10.1126/science.246.4928.371 · Cited by 146
Self-citationInvestigation of Protein Folding by Coarse-Grained Molecular Dynamics with the UNRES Force Field
Maisuradze, G. G., Senet, P., Czaplewski, C. R., Liwo, A., & Scheraga, H. A. (2010) · The Journal of Physical Chemistry A · doi:10.1021/jp9117776 · Cited by 111
Savelyev, A., & Papoian, G. A. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.02.067 · Cited by 88
Self-citationMolecular theory of associative memory Hamiltonian models of protein folding
Sasai, M., & Wolynes, P. G. (1990) · Physical Review Letters · doi:10.1103/physrevlett.65.2740 · Cited by 80
Self-citationAssociative memory Hamiltonians for structure prediction without homology: α/β proteins
Hardin, C., Eastwood, M. P., Prentiss, M. C., Luthey-Schulten, Z. A., & Wolynes, P. G. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.252753899 · Cited by 79
Self-citationSavelyev, A., & Papoian, G. A. (2009) · The Journal of Physical Chemistry B · doi:10.1021/jp9005058 · Cited by 78
Self-citationGeneralized protein tertiary structure recognition using associative memory hamiltonians
Friedrichs, M. S., Goldstein, R. A., & Wolynes, P. G. (1991) · Journal of Molecular Biology · doi:10.1016/0022-2836(91)90591-s · Cited by 75
Self-citationLocal Conformational Signals and the Statistical Thermodynamics of Collapsed Helical Proteins
Saven, J. G., & Wolynes, P. G. (1996) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0156 · Cited by 61
Self-citationAssociative memory Hamiltonians for structure prediction without homology: Alpha-helical proteins
Hardin, C., Eastwood, M. P., Luthey-Schulten, Z. A., & Wolynes, P. G. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.230432197 · Cited by 54
Self-citationRefolding dynamics of stretched biopolymers upon force quench
Hyeon, C., Morrison, G., Pincus, D. L., & Thirumalai, D. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905764106 · Cited by 49
Service, R. F. (2008) · Science · doi:10.1126/science.321.5890.784 · Cited by 49
Zong, C. C., Papoian, G. A., Ulander, J., & Wolynes, P. G. (2006) · Journal of the American Chemical Society · doi:10.1021/ja058589v · Cited by 36
Self-citationRestriction versus guidance in protein structure prediction
Hegler, J. A., Lätzer, J., Shehu, A., Clementi, C., & Wolynes, P. G. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907002106 · Cited by 34
Self-citationSasai, M., & Wolynes, P. G. (1992) · Physical Review A · doi:10.1103/physreva.46.7979 · Cited by 29
Self-citationMolecular dynamics of associative memory hamiltonians for protein tertiary structure recognition
Friedrichs, M. S., & Wolynes, P. G. (1990) · Tetrahedron Computer Methodology · doi:10.1016/0898-5529(90)90051-9 · Cited by 21
Self-citationProtein structure prediction: Do hydrogen bonding and water-mediated interactions suffice?
Oklejas, V., Zong, C. C., Papoian, G. A., & Wolynes, P. G. (2010) · Methods · doi:10.1016/j.ymeth.2010.05.006 · Cited by 13
Self-citationKwac, K. (2008) · Bulletin of the Korean Chemical Society · doi:10.5012/bkcs.2008.29.11.2172 · Cited by 4
Cited by
Coarse-Grained Protein Models and Their Applications
Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E., & Kolinski, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,071
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 893
Nguyen, P. H., Ramamoorthy, A., Sahoo, B. R., Zheng, J., Faller, P., Straub, J. E., Dominguez, L., Shea, J.-E., Dokholyan, N. V., De Simone, A., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01122 · Cited by 678
The power of coarse graining in biomolecular simulations
Ingólfsson, H. I., Lopez, C. A., Uusitalo, J. J., de Jong, D. H., Gopal, S. M., Periole, X., & Marrink, S. J. (2014) · WIREs Computational Molecular Science · doi:10.1002/wcms.1169 · Cited by 572
Wei, G., Xi, W., Nussinov, R., & Ma, B. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00562 · Cited by 436
Metadata
- Authors
- Davtyan, A., Schafer, N. P., Zheng, W., Clementi, C., Wolynes, P. G., & Papoian, G. A.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-05-10
- Volume
- 116
- Issue
- 29
- Pages
- 8494-8503
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W1992898397
- Cited by
- 324
- References
- 43
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- green
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