The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Discovery Through the Computational Microscope
Open accessAbstract
Summary All-atom molecular dynamics simulations have become increasingly popular as a tool to investigate protein function and dynamics. However, researchers are concerned about the short time scales covered by simulations, the apparent impossibility to model large and integral biomolecular systems, and the actual predictive power of the molecular dynamics methodology. Here we review simulations that were in the past both hotly disputed and considered key successes, namely of proteins with mainly mechanical functions (titin, fibrinogen, ankyrin, and cadherin). The simulation work covered shows how state-of-the-art modeling alleviates some of the prior concerns, and how unrefuted discoveries are made through the “computational microscope".
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Citations
Cites
1 retracted
RETRACTED: Solvent molecules bridge the mechanical unfolding transition state of a protein
Dougan, L., Feng, G., Lu, H. J., & Fernández, J. M. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706075105 · Cited by 74
RetractedVMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952
Self-citationScalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914
Self-citationReversible Unfolding of Individual Titin Immunoglobulin Domains by AFM
Rief, M., Gautel, M., Oesterhelt, F., Fernandez, J. M., & Gaub, H. E. (1997) · Science · doi:10.1126/science.276.5315.1109 · Cited by 3,077
Smith, S. B., Cui, Y., & Bustamante, C. (1996) · Science · doi:10.1126/science.271.5250.795 · Cited by 2,770
Dynamic strength of molecular adhesion bonds
Evans, E., & Ritchie, K. (1997) · Biophysical Journal · doi:10.1016/s0006-3495(97)78802-7 · Cited by 2,549
Direct observation of kinesin stepping by optical trapping interferometry
Svoboda, K., Schmidt, C. F., Schnapp, B. J., & Block, S. M. (1993) · Nature · doi:10.1038/365721a0 · Cited by 1,910
Adhesion Forces Between Individual Ligand-Receptor Pairs
Florin, E.-L., Moy, V. T., & Gaub, H. E. (1994) · Science · doi:10.1126/science.8153628 · Cited by 1,903
Regulation of cadherin-mediated adhesion in morphogenesis
Gumbiner, B. M. (2005) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1699 · Cited by 1,523
CADHERINS: A MOLECULAR FAMILY IMPORTANT IN SELECTIVE CELL-CELL ADHESION
Takeichi, M. (1990) · Annual Review of Biochemistry · doi:10.1146/annurev.bi.59.070190.001321 · Cited by 1,306
Cited by
CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field
Qi, Y., Ingólfsson, H. I., Cheng, X., Lee, J., Marrink, S. J., & Im, W. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00513 · Cited by 494
Molecular dynamics simulations of large macromolecular complexes
Perilla, J. R., Goh, B. C., Cassidy, C. K., Liu, B., Bernardi, R. C., Rudack, T., Yu, H., Wu, Z., & Schulten, K. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.03.007 · Cited by 420
Self-citationMuller, M. P., Jiang, T., Sun, C., Lihan, M., Pant, S., Mahinthichaichan, P., Trifan, A., & Tajkhorshid, E. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00608 · Cited by 282
TorchMD: A Deep Learning Framework for Molecular Simulations
Doerr, S., Majewski, M., Pérez, A., Krämer, A., Clementi, C., Noe, F., Giorgino, T., & De Fabritiis, G. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01343 · Cited by 230
Molecular dynamics simulation of an entire cell
Stevens, J. A., Grünewald, F., van Tilburg, P. A. M., König, M., Gilbert, B. R., Brier, T. A., Thornburg, Z. R., Luthey-Schulten, Z., & Marrink, S. J. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1106495 · Cited by 156
“Over the past decades, MD has matured into a powerful tool that functions as a computational microscope (Lee et al., 2009; Dror et al., 2012).”
Background
Metadata
- Authors
- Lee, E. H., Hsin, J., Sotomayor, M., Comellas, G., & Schulten, K.
- Venue
- Structure
- Publisher
- Elsevier BV
- Published
- 2009-10
- Volume
- 17
- Issue
- 10
- Pages
- 1295-1306
- ISSN
- 0969-2126 (ISSN-L)
- OpenAlex
- W2143848552
- Cited by
- 334
- References
- 108
- FWCI
- 6.96
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Lee, E. H., Hsin, J., Sotomayor, M., Comellas, G., & Schulten, K. (2009). Discovery Through the Computational Microscope. Structure. https://doi.org/10.1016/j.str.2009.09.001
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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