RDC Refinement with XPLOR-NIH: Difference between revisions

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:- Variable tensor tools for floating the principal alignment parameters, Da and R, during refinement  
:- Variable tensor tools for floating the principal alignment parameters, Da and R, during refinement  
:- Database potentials of mean force to refine against:  
:- ?Database potentials of mean force to refine against?:  
::- Multidimensional torsion angles  
::- Multidimensional torsion angles  
::- Backbone hydrogen bonding database (Optional)
::- Backbone hydrogen bonding database (Optional)

Revision as of 19:18, 22 March 2012

Brief Description

The angular dependence of RDCs and RCSAs can provide valuable structural information that complements NOE distance restraints. RDCs and RCSAs can be used to:

- Validate protein structures
- Refine protein structures (current topic)
- Provide constraints as a part of an initial structure determination

Selection of RDC and RCSA Orientational Restraints While the RDCs and RCSAs can be very useful orientational restraints for structural refinement, it can also introduce errors into calculations if not used properly. Local motion can average RDCs and make their use in the search for rigid structural model inappropriate. The current recommendation is to use RDCs and RCSAs from residues that are in well ordered regions of the protein. For initial structure refinement, use RDCs and RCSAs from residues that are part of well ordered alpha-helical or beta-strand regions (PSVS identifies these regions). RDCs from other well ordered regions can be added at the user's discretion. Ideally, the use of residue specific TauCs from T1/T2 or cross-correlation measurements can be used to eliminate the use of RDCs from mobile regions.

Parameter Selection for Use of RDC and RCSA Orientational Restraints in Refinement

Weighting of the RDCs and RCSAs
Error correction of NOE, dihedral angle, RDCs, and RCSAs restraint


Here, we describe the RDCs and RCSAs refinement protocol using XPLOR-NIH. The python version of the refinement script was taken from the example dataset (xplor-nih-2.28/eginput/gb1_rdc/refine.py) provided by the XPLOR-NIH package (http://nmr.cit.nih.gov/xplor-nih/). The features of particular relevance to RDC refinement are as follows:

- Variable tensor tools for floating the principal alignment parameters, Da and R, during refinement
- ?Database potentials of mean force to refine against?:
- Multidimensional torsion angles
- Backbone hydrogen bonding database (Optional)


Getting Started

The following files in XPLOR format are required to run the refinement:

prot_noe.tbl NOE restraint table
prot_dihe.tbl Dihedral angle restraint
prot_rdc.tbl RDC restraint table
prot_rcsa.tbl RCSA restraint table
prot.psf and prot.pdb Startup psf and pdb files were generated using the lowest energy structure from CYANA.

Generate NOE Restraint Table

An example of the NOE restraint table in XPLOR format is shown below (converted from CYANA upl file using a CYANA to XPLOR conversion script):

assign ( resid    2 and name HA   )   ( resid    2 and name HD*  )   4.00  2.20  1.00
assign ( resid    2 and name HA   )   ( resid    2 and name HG1  )   4.00  2.20  1.00
assign ( resid    2 and name HA   )   ( resid    2 and name HE*  )   4.00  2.20  1.00

Generate Dihedral Angle Table

An example of the Dihedral angle restraint in XPLOR format is shown below (Use CYANA for format conversion):

assign ( resid   7 and name N    )   ( resid    7 and name CA   ) 
       (resid    7 and name C    )   ( resid    8 and name N    )  1  -34.00   20.00 2 # For Phi Angle
assign ( resid   7 and name C    )   ( resid    8 and name N    ) 
       (resid    8 and name CA   )   ( resid    8 and name C    )  1  -71.00   34.00 2 # For Psi Angle

Generate RDC Table

An example of the RDC table in XPLOR format is shown below:

( resid 500 and name OO ): resid 500 is the residue number for the tensor
( resid 3 and name N ): resid 3: residue number of each amino acid
( resid 3 and name HN ) 2.586 1.5: RDC_value, Error_value
Note: The error_value is not critical due to the minimization style of this script.
# For NH Coupling
assign ( resid 500  and name OO  )
       ( resid 500  and name Z   )
       ( resid 500  and name X   )
       ( resid 500  and name Y   )
       ( resid 3    and name N   )
       ( resid 3    and name HN   )  2.586  1.5

assign ( resid 500  and name OO  )
       ( resid 500  and name Z   )
       ( resid 500  and name X   )
       ( resid 500  and name Y   )
       ( resid 4    and name N   )
       ( resid 4    and name HN   )  7.785  1.5

# For NC Coupling, normalized to NH magnitude
assign ( resid 500  and name OO  )
       ( resid 500  and name Z   )
       ( resid 500  and name X   )
       ( resid 500  and name Y   )
       ( resid 2    and name C   )
       ( resid 3    and name N   )  -9.26475  1.5

assign ( resid 500  and name OO  )
       ( resid 500  and name Z   )
       ( resid 500  and name X   )
       ( resid 500  and name Y   )
       ( resid 3    and name C   )
       ( resid 4    and name N   )  -3.9435  1.5

Generate RCSA Restraint Table

An example of the RCSA table in XPLOR format is shown below:

RCSA can also be used to validate protein structure using REDCAT or Prestegard in house script. It is important to check measurement of the RCSA and apply reference correction if necessary.

(resid 2 and name C) (resid 3 and name N) (resid 3 and name HN) 29.040 24.750: 29.040 is the N RCSA shift in PPB unit and 24.750 is a constant
(resid 4 and name C) (resid 4 and name O) (resid 5 and name N) -24.400 13.333: -24.400 is the C RCSA shift in PPB unit and 13.333 is a constant
 #For N RCSA
assign (resid 500 and name OO ) (resid 500 and name Z) (resid 500 and name X ) (resid 500 and name Y )
(resid 2 and name C) (resid 3 and name N) (resid 3 and name HN) 29.040  24.750
assign (resid 500 and name OO ) (resid 500 and name Z) (resid 500 and name X ) (resid 500 and name Y )
(resid 3 and name C) (resid 4 and name N) (resid 4 and name HN) 62.205  24.750
assign (resid 500 and name OO ) (resid 500 and name Z) (resid 500 and name X ) (resid 500 and name Y )
(resid 4 and name C) (resid 5 and name N) (resid 5 and name HN) 55.110  24.750

# For C RCSA
assign (resid 500 and name OO ) (resid 500 and name Z) (resid 500 and name X ) (resid 500 and name Y )
(resid 4 and name C) (resid 4 and name O) (resid 5 and name N) -24.400  13.333
assign (resid 500 and name OO ) (resid 500 and name Z) (resid 500 and name X ) (resid 500 and name Y )
(resid 7 and name C) (resid 7 and name O) (resid 8 and name N) 36.533   13.333
assign (resid 500 and name OO ) (resid 500 and name Z) (resid 500 and name X ) (resid 500 and name Y )
(resid 8 and name C) (resid 8 and name O) (resid 9 and name N) -8.400   13.333

Protocol for RDC Refinement

First, obtain a good estimate of the magnitude of Da and R from alignment tensors using either REDCAT or PALES program and use this as a starting point for the refinement. Then edit the following portion of the refine.py script. Note: text on the same line and following a “#” sign is not read by the XPLOR program.

('t', -6.5, 0.62): this line refers to the Da and rhombicity for alignment medium t
#                        medium  Da   rhombicity
for (medium,Da,Rh) in [ ('t',   -6.5, 0.62),
                        ('b',   -9.9, 0.23) ]:
    oTensor = create_VarTensor(medium)
    oTensor.setDa(Da)
    oTensor.setRh(Rh)
    media[medium] = oTensor
    pass


The example below contains NH, NCO, and HNC RDCs from two different alignment media. The Da rescaling factor was used since the magnitude of the non-NH RDCs were not normalized to the magnitude of NH RDCs.

from rdcPotTools import create_RDCPot, scale_toNH
rdcs = PotList('rdc')
for (medium,expt,file,                 scale) in \
    [('t','NH' ,'tmv107_nh.tbl'       ,1),
     ('t','NCO','tmv107_nc.tbl'       ,.05),
     ('t','HNC','tmv107_hnc.tbl'      ,.108),
     ('b','NH' ,'bicelles_new_nh.tbl' ,1),
     ('b','NCO','bicelles_new_nc.tbl' ,.05),
     ('b','HNC','bicelles_new_hnc.tbl',.108)
     ]:
    rdc = create_RDCPot("%s_%s"%(medium,expt),file,media[medium])

    #1) scale prefactor relative to NH
    #   see python/rdcPotTools.py for exact calculation
    # scale_toNH(rdc) - not needed for these datasets -
    #                        but non-NH reported rmsd values will be wrong.

    #3) Da rescaling factor (separate multiplicative factor)
    # scale *= ( 1. / rdc.oTensor.Da(0) )**2
    rdc.setScale(scale)
    rdc.setShowAllRestraints(1) #all restraints are printed during analysis
    rdc.setThreshold(1.5)       # in Hz
    rdcs.append(rdc)
    pass
potList.append(rdcs)
rampedParams.append( MultRamp(0.05,5.0, "rdcs.setScale( VALUE )") )


Allow Da and R to float by using the setFreedom method associated with the medium object. To fix the peptide plane, the IVM_groupRigidBackbone tool were used (First two lines and the last line).

from selectTools import IVM_groupRigidBackbone
IVM_groupRigidBackbone(dyn)

for m in media.values():
#    m.setFreedom("fixDa, fixRh")        #fix tensor Rh, Da, vary orientation
    m.setFreedom("varyDa, varyRh")      #vary tensor Rh, Da, vary orientation
protocol.torsionTopology(dyn,oTensors=media.values())

# minc used for final cartesian minimization
#
minc = IVM()
protocol.initMinimize(minc)
IVM_groupRigidBackbone(minc)