RDC Refinement with XPLOR-NIH

Brief Description

Residual Dipolar Couplings (RDCs) originate from the anisotropic component of the dipolar interaction, which is dependent on the angle between an inter-nuclear vector and the magnetic field. When a molecule samples orientations uniformly, as it does in normal solution NMR, dipolar couplings average to zero and are not observable. However, if a molecule is dissolved in a dilute liquid crystalline medium, or other orienting medium, it becomes partially aligned, and the dipolar couplings are not completely averaged to zero.  This leads to a small contribution to the splitting of NMR signals. The angular dependence of these contributions can provide valuable structural information that complements NOE distance restraints.  RDCs can be used to validate protein structures, to refine structures to improve quality, and to provide constraints as a part of an initial structure determination. Here, we describe the RDC-refinement protocol using XPLOR-NIH. The python version of the refinement script was taken from the example dataset (xplor-nih-2.22/eginput/gb1_rdc/refine.py) provided by the XPLOR-NIH package. The key features of this refinement are as follow:

- Variable tensor tools for floating the RDC tensors during refinement

- A radius of gyration term to represent the weak packing potential

(This potential is used when the calculated structures are too loosely packed)

- Database potentials of mean force to refine against:

- Multidimensional torsion angles

- Backbone hydrogen bonding database (Optional)

Software Information

XPLOR-NIH
http://nmr.cit.nih.gov/xplor-nih/

REDCAT
http://ifestos.cse.sc.edu/software.php

Files Need to Run XPLOR-NIH

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

prot_noe.tbl NOE restraint table (converted from CYANA upl file using a CYANA to XPLOR conversion script)

prot_dihe.tbl Dihedral angle restraint (Use CYANA for format conversion)

prot_rdc.tbl RDC restraint table

prot.psf and prot.pdb Startup psf and pdb files were generated using the lowest energy structure from CYANA.

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.

#                        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)