Structure: Large-scale Studies of Macromolecular Motions

We were amongst the first groups to study macromolecular motions statistically, on a large-scale. In particular, we have set up a publicly accessible Database of Macromolecular Motions and coupled it with simulation tools to interpolate between structural conformations; the database also has associated tools to predict likely motions based on simple models, such as normal modes and localized hinges connecting rigid domains. These allowed us to classify motions based on the interdigitating packing at internal interfaces: motions are identified as shear or hinge, based on whether or not a well-packed interface is maintained between the dynamic elements throughout the motion. This classification scheme is motivated by the fact that protein interiors are packed exceedingly tightly and that tight packing greatly constrains a protein's mobility. We have developed tools for measuring and comparing the packing efficiency at different interfaces and locations throughout macromolecules (e.g. inter-domain, protein surface, helix-helix, protein vs. RNA) using specialized geometric constructions. For this we have generated new protein packing parameters, including self-consistent radii and standard volumes for each atom type.

Identifying Allosteric Hotspots with Dynamics: Application to Inter- and Intra-species Conservation.
D Clarke, A Sethi, S Li, S Kumar, RWF Chang, J Chen, M Gerstein (2016). Structure 24: 826-837.

Predicting protein ligand binding motions with the conformation explorer.
SC Flores, MB Gerstein (2011). BMC Bioinformatics 12: 417.

Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions.
N Bhardwaj, A Abyzov, D Clarke, C Shou, MB Gerstein (2011). Protein Sci 20: 1745-54.

3V: cavity, channel and cleft volume calculator and extractor.
NR Voss, M Gerstein (2010). Nucleic Acids Res 38: W555-62.

RigidFinder: a fast and sensitive method to detect rigid blocks in large macromolecular complexes.
A Abyzov, R Bjornson, M Felipe, M Gerstein (2010). Proteins 78: 309-24.

Bayesian modeling of the yeast SH3 domain interactome predicts spatiotemporal dynamics of endocytosis proteins.
R Tonikian, X Xin, CP Toret, D Gfeller, C Landgraf, S Panni, S Paoluzi, L Castagnoli, B Currell, S Seshagiri, H Yu, B Winsor, M Vidal, MB Gerstein, GD Bader, R Volkmer, G Cesareni, DG Drubin, PM Kim, SS Sidhu, C Boone (2009). PLoS Biol 7: e1000218.

Relating protein conformational changes to packing efficiency and disorder.
N Bhardwaj, M Gerstein (2009). Protein Sci 18: 1230-40.

StoneHinge: hinge prediction by network analysis of individual protein structures.
KS Keating, SC Flores, MB Gerstein, LA Kuhn (2009). Protein Sci 18: 359-71.

HingeMaster: normal mode hinge prediction approach and integration of complementary predictors.
SC Flores, KS Keating, J Painter, F Morcos, K Nguyen, EA Merritt, LA Kuhn, MB Gerstein (2008). Proteins 73: 299-319.

FlexOracle: predicting flexible hinges by identification of stable domains.
SC Flores, MB Gerstein (2007). BMC Bioinformatics 8: 215.

Hinge Atlas: relating protein sequence to sites of structural flexibility.
SC Flores, LJ Lu, J Yang, N Carriero, MB Gerstein (2007). BMC Bioinformatics 8: 167.

The Database of Macromolecular Motions: new features added at the decade mark.
S Flores, N Echols, D Milburn, B Hespenheide, K Keating, J Lu, S Wells, EZ Yu, M Thorpe, M Gerstein (2006). Nucleic Acids Res 34: D296-301.

Normal modes for predicting protein motions: a comprehensive database assessment and associated Web tool.
V Alexandrov, U Lehnert, N Echols, D Milburn, D Engelman, M Gerstein (2005). Protein Sci 14: 633-43.

Conformational changes associated with protein-protein interactions.
CS Goh, D Milburn, M Gerstein (2004). Curr Opin Struct Biol 14: 104-9.

Exploring the range of protein flexibility, from a structural proteomics perspective.
M Gerstein, N Echols (2004). Curr Opin Chem Biol 8: 14-9.

Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures.
V Alexandrov, M Gerstein (2004). BMC Bioinformatics 5: 2.

Tools and databases to analyze protein flexibility; approaches to mapping implied features onto sequences.
WG Krebs, J Tsai, V Alexandrov, J Junker, R Jansen, M Gerstein (2003). Methods Enzymol 374: 544-84.

MolMovDB: analysis and visualization of conformational change and structural flexibility.
N Echols, D Milburn, M Gerstein (2003). Nucleic Acids Res 31: 478-82.

Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic.
WG Krebs, V Alexandrov, CA Wilson, N Echols, H Yu, M Gerstein (2002). Proteins 48: 682-95.

The morph server: a standardized system for analyzing and visualizing macromolecular motions in a database framework.
WG Krebs, M Gerstein (2000). Nucleic Acids Res 28: 1665-75.

Perspectives: signal transduction. Proteins in motion.
M Gerstein, C Chothia (1999). Science 285: 1682-3.

A database of macromolecular motions.
M Gerstein, W Krebs (1998). Nucleic Acids Res 26: 4280-90.

Structural mechanisms for domain movements in proteins.
M Gerstein, AM Lesk, C Chothia (1994). Biochemistry 33: 6739-49.

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