Biology:Conformational ensembles
File:Modeling-Conformational-Ensembles-of-Slow-Functional-Motions-in-Pin1-WW-pcbi.1001015.s016.ogv
In computational chemistry, conformational ensembles, also known as structural ensembles, are experimentally constrained computational models describing the structure of intrinsically unstructured proteins.[1][2] Such proteins are flexible in nature, lacking a stable tertiary structure, and therefore cannot be described with a single structural representation.[3] The techniques of ensemble calculation are relatively new on the field of structural biology, and are still facing certain limitations that need to be addressed before it will become comparable to classical structural description methods such as biological macromolecular crystallography.[4]
Purpose
Ensembles are models consisting of a set of conformations that together attempt to describe the structure of a flexible protein. Even though the degree of conformational freedom is extremely high, flexible/disordered protein generally differ from fully random coil structures.[5][6] The main purpose of these models is to gain insights regarding the function of the flexible protein, extending the structure-function paradigm from folded proteins to intrinsically disordered proteins.
Calculation techniques
The calculation of ensembles rely on experimental measurements, mostly by Nuclear Magnetic Resonance spectroscopy and Small-angle X-ray scattering. These measurements yield short and long-range structural information.
Short-range
- Chemical Shifts (CS)
- Residual Dipolar Couplings (RDCs)
- J-couplings
- Hydrogen-exchange
- Solvent-accessibility.
Long-range
- Paramagnetic Relaxation Enhancements (PREs)
- Nuclear Overhauser effects (NOEs)
- SAXS topological restraints.
Constrained molecular dynamics simulations
The structure of disordered proteins may be approximated by running constrained molecular dynamics (MD) simulations where the conformational sampling is being influenced by experimentally derived constraints.[7]
Fitting experimental data
Another approach uses selection algorithms such as ENSEMBLE and ASTEROIDS.[8][9] Calculation procedures first generate a pool of random conformers (initial pool) so that they sufficiently sample the conformation space. The selection algorithms start by choosing a smaller set of conformers (an ensemble) from the initial pool. Experimental parameters (NMR/SAXS) are calculated (usually by some theoretical prediction methods) for each conformer of chosen ensemble and averaged over ensemble. The difference between these calculated parameters and true experimental parameters is used to make an error function and the algorithm selects the final ensemble so that the error function is minimised.
Limitations
The determination of a structural ensemble for an IDP from NMR/SAXS experimental parameters involves generation of structures that agree with the parameters and their respective weights in the ensemble. Usually, the available experimental data is less compared to the number of variables required to determine making it an under-determined system. Due to this reason, several structurally very different ensembles may describe the experimental data equally well, and currently there are no exact methods to discriminate between ensembles of equally good fit. This problem has to be solved either by bringing in more experimental data or by improving the prediction methods by introducing rigorous computational methods.
References
- ↑ "Constructing ensembles for intrinsically disordered proteins". Current Opinion in Structural Biology. (3) 21 (3): 426–31. June 2011. doi:10.1016/j.sbi.2011.04.001. PMID 21530234. PMC 3112268. http://dspace.mit.edu/bitstream/1721.1/99137/1/Stultz_Constructing%20ensembles.pdf.
- ↑ "pE-DB: a database of structural ensembles of intrinsically disordered and of unfolded proteins". Nucleic Acids Research 42 (Database issue): D326-35. January 2014. doi:10.1093/nar/gkt960. PMID 24174539.
- ↑ "Intrinsically unstructured proteins and their functions". Nature Reviews. Molecular Cell Biology 6 (3): 197–208. March 2005. doi:10.1038/nrm1589. PMID 15738986.
- ↑ "Unstructural biology coming of age". Current Opinion in Structural Biology 21 (3): 419–25. June 2011. doi:10.1016/j.sbi.2011.03.012. PMID 21514142.
- ↑ "Atomic resolution description of the interaction between the nucleoprotein and phosphoprotein of Hendra virus". PLOS Pathogens 9 (9): e1003631. 2013. doi:10.1371/journal.ppat.1003631. PMID 24086133.
- ↑ "Cooperative unfolding of compact conformations of the intrinsically disordered protein osteopontin". Biochemistry 52 (31): 5167–75. August 2013. doi:10.1021/bi400502c. PMID 23848319.
- ↑ "Determination of the free energy landscape of alpha-synuclein using spin label nuclear magnetic resonance measurements". Journal of the American Chemical Society 131 (51): 18314–26. December 2009. doi:10.1021/ja904716h. PMID 20028147.
- ↑ "Characterization of disordered proteins with ENSEMBLE". Bioinformatics 29 (3): 398–9. February 2013. doi:10.1093/bioinformatics/bts701. PMID 23233655.
- ↑ "Defining conformational ensembles of intrinsically disordered and partially folded proteins directly from chemical shifts". Journal of the American Chemical Society 132 (4): 1270–2. February 2010. doi:10.1021/ja909973n. PMID 20063887.
External links
- Varadi, Mihaly. "PED3: Protein Ensemble Database". Peter Tompa Lab. Vrije Universiteit Brussel. http://pedb.vib.be. "Database of conformational ensembles describing flexible proteins"
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