Difference between revisions of "Task 3 - Normal Mode Analysis"
Line 1: | Line 1: | ||
Several experimental techniques, such as X-ray crystallography, NMR and spectroscopy, can provide information on the structure and dynamics of biological macromolecules, in our case proteins. However, experimental methods are often time-consuming and do not provide a complete picture of the dynamic properties of proteins. Structural bioinformatics can complement experimental methods. |
Several experimental techniques, such as X-ray crystallography, NMR and spectroscopy, can provide information on the structure and dynamics of biological macromolecules, in our case proteins. However, experimental methods are often time-consuming and do not provide a complete picture of the dynamic properties of proteins. Structural bioinformatics can complement experimental methods. |
||
− | Molecular dynamics (MD) simulations provide invaluable insight into protein dynamics considering the full range of harmonic and anharmonic motions at the atomic level. However, as you have found out by now, MD simulations are computational expensive. Typical simulations sample conformational motions on the nanosecond timescale. |
+ | '''Molecular dynamics''' (MD) simulations provide invaluable insight into protein dynamics considering the full range of harmonic and anharmonic motions at the atomic level. However, as you have found out by now, MD simulations are computational expensive. Typical simulations sample conformational motions on the nanosecond timescale. |
− | Normal mode analysis (NMA), on the other hand, has been used successfully to determine and investigate large global motions of proteins. In NMA, the protein is modeled as a harmonic system oscillating around a stable equilibrium. The low-frequency modes correspond to collective motions of the complete protein. <br /> |
+ | '''Normal mode analysis''' (NMA), on the other hand, has been used successfully to determine and investigate large global motions of proteins. In NMA, the protein is modeled as a harmonic system oscillating around a stable equilibrium. The low-frequency modes correspond to collective motions of the complete protein. <br /> |
The first NMA of a protein used an all-atom representation of bovine pancreatic trypsin inhibitor (BPTI). You can have a look at the papers here: <br /> |
The first NMA of a protein used an all-atom representation of bovine pancreatic trypsin inhibitor (BPTI). You can have a look at the papers here: <br /> |
||
[[Media:Brooks1983_PNAS.pdf|Brooks & Karplus 1983]] <br /> |
[[Media:Brooks1983_PNAS.pdf|Brooks & Karplus 1983]] <br /> |
Revision as of 22:51, 11 July 2011
Several experimental techniques, such as X-ray crystallography, NMR and spectroscopy, can provide information on the structure and dynamics of biological macromolecules, in our case proteins. However, experimental methods are often time-consuming and do not provide a complete picture of the dynamic properties of proteins. Structural bioinformatics can complement experimental methods.
Molecular dynamics (MD) simulations provide invaluable insight into protein dynamics considering the full range of harmonic and anharmonic motions at the atomic level. However, as you have found out by now, MD simulations are computational expensive. Typical simulations sample conformational motions on the nanosecond timescale.
Normal mode analysis (NMA), on the other hand, has been used successfully to determine and investigate large global motions of proteins. In NMA, the protein is modeled as a harmonic system oscillating around a stable equilibrium. The low-frequency modes correspond to collective motions of the complete protein.
The first NMA of a protein used an all-atom representation of bovine pancreatic trypsin inhibitor (BPTI). You can have a look at the papers here:
Brooks & Karplus 1983
Go, Noguti & Nishikawa 1983
Elastic network models (among them Gaussian and anisotropic network models) greatly reduce the memory requirements for NMA. In 1996, Monique Tirion introduced this simplified model that was further developed by several others during the next years.
You can find a short overview here:
http://mmb.pcb.ub.es/FlexServ/help/NMA.php
Two original papers can be viewed here:
Tirion 1996
Hinsen 1998
Tasks and questions
In this task you will analyze your protein structure using elastic and Gaussian network models. Furthermore, using a small protein as example you will calculate normal modes based on all atoms. Some servers provide a concise introduction to the theory behind NMA. This information may be very helpful.
For each server, analyze at least the lowest five normal modes.
- What information do the different servers provide?
- Which regions of your protein are most flexible, most stable?
- When you visualize the modes (provided by server or using for example PyMol or VMD), try to describe what movements you observe? Hinge-movement, “breathing”…
- Can you observe notable differences between the normal modes calculated by different servers?
- Out of the servers, chose one or two favorites and discuss the results of these in more detail. Why do you like these?
- When your MD simulations are finished, compare the lowest-frequency normal modes with your MD simulation using visualization software, e.g. PyMol or VMD. Can you observe different movements or similar dynamics? If possible, compare an overlay of the lowest-frequency modes to your MD simulation. You can superimpose the normal modes for example in VMD.
- What are the advantages and disadvantages of NMA compared to MD?
Parameters
- If possible, use a cutoff for C_alpha atom pairs of 15 Angstroem.
- Calculate the 10 lowest-frequency normal modes (the six zero modes have to be considered for a few applications).
- In most cases you can upload the original .pdb file from the Protein Data Bank. In some cases, however, you can upload only the structure itself (ATOM lines of the .pdb file).
Servers
1. WEBnm@
http://apps.cbu.uib.no/webnma/home
- try the amplitude scaling and vectors option
2. ElNemo
http://www.igs.cnrs-mrs.fr/elnemo/start.html
3. Anisotropic Network Model web server
http://ignmtest.ccbb.pitt.edu/cgi-bin/anm/anm1.cgi
- set the distance weight to and 3.0
- try the amplitude scaling and vectors option
- have a look at the different ANM model cutoffs
4. oGNM – Gaussian network model
http://ignm.ccbb.pitt.edu/GNM_Online_Calculation-t.htm
- set the cutoff to 15 Angstroem
5. NOMAD-Ref
http://lorentz.dynstr.pasteur.fr/nma/submission.php
- set the distance weight to 3.0
- set the cutoff to 15 Angstroem
6. All-atom NMA analysis using Gromacs on the NOMAD-Ref server
http://lorentz.dynstr.pasteur.fr/gromacs/nma_submission.php
- All-atom calculations are only supported for small proteins of up to 2,000 atoms. Use for example BPTI, PDB entry: 1BPTI. Upload a .pdb file that contains only the ATOM lines of the original .pdb file. You can also choose another small protein for the all-atom NMA.
- set the temperature to 600K and 2000K
- you can visualize the modes with PyMol or VMD.
- Compare the all-atom NMA of BPTI (or your chosen protein) with an elastic network calculation, e.g. NOMAD-Ref.