|
| Python-based Hierarchical ENvironment for Integrated Xtallography |
| Documentation Home |
Sequence assignment and linkage of neighboring segments with assign_sequence
Author(s)
PurposeYou can now carry out an improved sequence assignment of a model that you have already built with phenix.assign_sequence. Further, once the sequence has been assigned, this method will use the sequence and proximity to identify chains that should be connected, and it will connect those that have the appropriate relationships using the new loop libraries available in phenix.fit_loops. The result is that you may be able to obtain a more complete model with more chains assigned to sequence than previously. assign_sequence is a command line tool for reanalyzing resolve sequence assignment for a model and a map including the non-crystallographic symmetry, exclusion of sequence by previously-assigned regions, and requirement for plausible distances and geometries between ends of fragments with assigned sequences. Additionally assign_sequence will use the fit_loops loop library to connect segments that are separated by a short loop. Note: assign_sequence is designed to be used after resolve model-building in which residues that are not assigned to sequence are given residue numbers higher than any residue in the input sequence file. If you input a model not built by resolve or in phenix, or if you would like to completely redo the sequence assignment for your model, be sure to set "allow_fixed_segments=False". NOTE: assign_sequence is normally called from phenix.phase_and_build but you can run it interactively if you want. UsageHow assign_sequence works:The starting point for assign_sequence is a set of segments of structure read in from the input model. assign_sequence then uses resolve to calculate the compatibility of each possible side chain with each residue in each segment. Then assign_sequence tests out possible combinations of alignments of all the segments in the input model and chooses the set of alignments that is most compatible with the density map, the number of NCS copies, and with the geometries and distances between ends of the segments. Sequence probabilities:assign_sequence uses the side-chain to map compatibility matrix calculated by resolve to assess the relative probabilities of each possible side chain at each position in the input model. Segments that are positively assigned to a sequence by resolve are (by default) maintained and used as anchors for further sequence assignment. All other segments have a relative probability associated with each possible alignment of the segment to the input sequence. The score for each alignment is the logarithm of this probability (essentially a log-likelihood LL score). Connection scores:Any pair of segments with some assignment of sequence to each segment has an additional score corresponding to the plausibility of a connection of the expected length existing between the segments. If the distance between ends is greater than can be bridged by the number of residues separating them, then the connection is not possible. If the connection is possible, it is scored based on the best density fit (CC) of a loop from the fit_loops loop library. This additional score is normally 10*CC. Generating sequence alignments and connectivitiesassign_sequence starts with the segments with the most convincing assignments of sequence. Often these are those with sequence positively assigned by resolve; otherwise they are those with the highest-probability assignments. This yields a starting arrangement (sequence assignment for a set of segments). Then each possible sequence assignment of each unassigned segment is tested for compatibility with the existing arrangement and the one that is most compatible (based on the connections that would result, duplication of sequence, and sequence-map matching) is added to the arrangement. Optionally many arrangements can be built up in parallel, but often a very good one can be found simply by taking the top one at each step. This process is repeated until no additional segments can be added to the arrangement to yield an increase in log-likelihood score of (by default) 2 or greater. NCS copies:assign_sequence builds up a set of possible sequence assignments and connectivities that depends on the expected number of copies in the asymmetric unit of the crystal. If there is only one copy of the molecule in the crystal, then no residues in the sequence can be used more than once in sequence assignment. If there are N copies, then a residue can be used up to N times. If there are multiple copies, then each molecule must be self-consistent, with plausible distances and geometries relating each segment to the next. Connecting segments:Once a final arrangement is found, including NCS if applicable, all segments that are separated by short loops (typically 0-3 residues) are connected using loops from the fit_loops loop library. This yields longer segments of structure with sequences fully assigned. The resulting model then has side chains added to match the newly-assigned sequence and is written out. Output files from assign_sequenceassign_sequence.pdb: A PDB file with your input model assigned to sequence (to the extent possible). Residues not assigned to sequence will be given a chain ID higher than those assigned, and they will be given residue numbers higher than any residue number in the sequence file. ExamplesStandard run of assign_sequence:Running assign_sequence is easy. From the command-line you can type: phenix.assign_sequence map_coeffs.mtz coords.pdb sequence.datIf you want (or need) to specify the column names from your mtz file, you will need to tell assign_sequence what FP and PHIB (and optionally FOM) are, in this format: phenix.assign_sequence map_coeffs.mtz coords.pdb \ labin="FP=2FOFCWT PHIB=PH2FOFCWT" sequence.dat Possible ProblemsSpecific limitations and problems:LiteratureAdditional informationList of all assign_sequence keywords
-------------------------------------------------------------------------------
Legend: black bold - scope names
black - parameter names
red - parameter values
blue - parameter help
blue bold - scope help
Parameter values:
* means selected parameter (where multiple choices are available)
False is No
True is Yes
None means not provided, not predefined, or left up to the program
"%3d" is a Python style formatting descriptor
-------------------------------------------------------------------------------
assign_sequence
input_files
seq_file= None File with 1-letter code sequence of molecule. Chains
separated by blank line or greater-than sign
pdb_in= None Optional starting PDB file (ends will be extended if
present)
mtz_in= None MTZ file with coefficients for a map
map_coeff_labels= None If map coefficients cannot be identified
automatically from your MTZ file, you can specify the
label or labels for them. (Please separate labels with
blank space, MTZ columns grouped together separated by
commas with no blanks.) You can specify:
map_coeff_labels (e.g., FWT,PHIFWT) amplitudes and
phases (e.g., FP,SIGFP PHIB) or amplitudes, phases,
weights (e.g., FP,SIGFP PHIB FOM)
labin= "" For backward compatibility only. Use instead map_coeff_labels.
Labin line for MTZ file with map coefficients. This is optional
if assign_sequence can guess the correct coefficients for FP PHIB
. Otherwise specify: LABIN FP=myFP PHIB=myPHI where myFP is your
column label for FP. NOTE: myFP and myPHI must be adjacent in the
mtz file.
prob_file= None File with sequence probability information from resolve
linkage_file= None File with linkage information from combine_models
Must have been run with the same input pdb file and the
same value of min_segment_length
loop_dict_file= None File with loop information from previous run as
pickle file
pair_object_dict_file= None pair object dict as pickle file
checked_connections_file= None checked connections dict as pickle file
density_removed_mtz_in= None MTZ file with density_removed coefficients
for a map
comparison_model= None Comparison model (normally just for testing)
output_files
pdb_out= assign_sequence.pdb Output PDB file
log= None Output log file
params_out= assign_sequence_params.eff Parameters file to rerun
assign_sequence
output_loop_dict_file= None loop dict as pickle file
output_pair_object_dict_file= None pair object dict as pickle file
output_checked_connections_file= None checked connections dict as pickle
file
assignment
ncs_resolution= None Resolution for NCS identification
find_ncs= False Try to find NCS in chains after sequence assignment
range_to_keep= 4.0 Keep solutions with score within range_to_keep of the
maximum
max_keep= 10 Number of possibilities to keep in optimization
max_write= 6 Number of possibilities to write out at end
linkage_score= 10. Score for creating a link between segments
max_linkage_score= 12. Maximum linkage score attainable
loop_score= 10. Score for a loop is loop_score*(1.+loop_cc)
length_mismatch_penalty= 0 Decrease in linkage score if linkage is not
correct length
depth_to_keep= 8.0 In full optimization solutions will be kept with
score depth_to_keep + max_linkage_score below the best
maximum_length_mismatch= 3 Maximum length mismatch in linkages
min_confidence= 0.9999 Sets required confidence level in a placement of
a segment to keep the best one.
convincing_score= 2. Score gain required to keep a sequence assignment
starting_convincing_score= 10. Score gain required to keep an initial
sequence assignment
minimum_length= 4 Minimum length of a segment to place
min_segment_length= 5 Segments shorter than this will be ignored on
read-in.
max_levels= None Number of segments to consider in building a complete
sequence assignment (quick default = 6, otherwise 20)
max_indiv_tries_per_level= None Number of sequence assignments to
consider for each segment (quick default = 1,
otherwise 3)
max_total_tries_per_level= None Number of sequence arrangements to
consider for all additional segments (quick
default = 1, otherwise 6)
max_placements= 100 Number of placements of any segment to consider
max_final_placements= 20 Number of final arrangements to consider
check_ncs_with_offset= True Check to verify that segments that seem to
show NCS are actually different if offset by 1
residue. If protein is just helices then you
might need to try check=False
list_only_complete= False Only include complete arrangements; ignore
those that have arrangements of some segments that
are subsequently removed as incompatible
allow_fixed_segments= True If True, then input segments with sequences
assigned are kept fixed. If fix_known=False, then
assigned segments are identified by sequence
numbers less than or equal to the longest segment
in the sequence file.
fix_known= False If True, then all input segments with except those
marked as segid=UNK will be considered fixed. (Instead of
identifying them based on sequence number in input file).
Forces allow_fixed_segments to be True.
keep_connectivity= False This is very useful if you expect your model to
have the same connectivity as your template. If True,
then input segments are kept in the order found in
the input PDB and kept assigned to the original
chains, but their assignments may change otherwise
(allowing insertions/deletions). NOTE: If True, then
allow_fixed_segments=False unless fix_known=True
include_chain_u_in_keep_connectivity= False If True, chains with chainID
of 'U' are included in analysis
with keep_connectivity=True
optimize_arrangements= True Try to optimize arrangements at the end,
including removal of uncertain segments
use_connectivity_in_optimize= True If True and keep_connectivity is True
and optimize_arrangements is True , then
connectivity will be used in chain
optimization
remove_uncertain_segments= True If True, then remove uncertain segments.
NOTE: at very end of iteration, if any, then
the removal of uncertain segments is
determined by
iteration.remove_uncertain_at_end.
optimize_sequence_alignment= True If True, then try to align fragments
to template sequence
minimize_alignment_changes= False If True, then try to keep sequence
numbers matched to original as much as
possible.
allow_longer_connections= True If a connection segment is available, try
with lower scoring also n+1,n+2,n+3
connections. This may be a good idea because
loops are often built short by one or a few
residues.
replace_side_chains= True At the end of sequence assignment identify
side-chain rotamers and replace existing
side-chains
replace_direct_joins= False Use fit-loops to rebuild all junctions that
are joined flush
short_segment_length= 12 Definition of a short segment
max_loop_length= 8 Maximum length of loop to try to fit
n_random_loop= 500 Number of loop versions to build
max_unassigned_short_segments= 20 Maximum number of segments
short_segment_length or fewer residues
that are not assigned to sequence to
consider in connections. Keeping too many
can make the analysis take a very long
time.
compare_only= False Just compare input model to comparison model
reset_sequence= False Adjust sequence numbering and sequence of segments
in pdb_in based on sequence numbers of matching
positions in comparison_model. Cannot be used with
compare_only.
make_unique= None Make segments of input model unique (no overlapping)
residues. Normally keep this at None. Used in iteration of
assign_sequence.
iteration
iterative_assignment= False You can iteratively assign sequence and fit
loops This can improve the assignment, but will
take longer
cycles= 3 Cycles of iteration
start_step= *assign_sequence fit_loops insert_loops get_connections You
can decide where to start in the cycle
end_step= *None assign_sequence fit_loops insert_loops get_connections
You can specify last step to carry out in iteration
skip_step= None You can specify steps to skip in iteration
assign_sequence_file= None File partially assigned to sequence (output
of assign_sequence)
loops_file= None File with loops to insert
assign_sequence_insertion_file= None File partially assigned to sequence
with insertions
connections_file_list= None list of files with possible connections to
consider
only_assign_sequence_on_last_cycle= True Just run assign_sequence step
on last iteration cycle
build_outside_model= True Build outside model in get_connections
build_outside_model_once= True Run build outside model a maximum of one
time (Run once if build_outside_model=True,
never if build_outside_model=False)
remove_uncertain_at_end= False If True, then remove uncertain segments
at very end of iteration (in addition to
removing them along the way if
remove_uncertain_segments=True )
connect_all_segments= True Connect all segments sequentially in
get_connections
fill_in_gaps= True After optimize and keep_connectivity, try to fill in
gaps
standard_gap_score= 0. Score for filling default gap
decrease_tries_with_levels= False Reduce number of tries each level of
search
directories
temp_dir= "temp_dir" Optional temporary work directory
output_dir= "" Optional output directory
crystal_info
resolution= 0. high-resolution limit for map calculation
solvent_fraction= 0.5 solvent fraction
chain_type= *PROTEIN DNA RNA Chain type (for identifying main-chain and
side-chain atoms)
ncs_copies= 1 NCS copies
model_building
dist_max= 20. Maximum distance ends can be apart to consider for linking
max_loop_lib_gap= 3 Maximum number of residues in working loop library
(This must match loop libraries that are available)
control
verbose= False Verbose output
quick= True Run quickly
raise_sorry= False Raise sorry if problems
debug= False Debugging output
dry_run= False Just read in and check parameter names
nproc= 1 You can specify the number of processors to use
check_wait_time= 1.0 You can specify the length of time (seconds) to
wait between checking for subprocesses to end
wait_between_submit_time= 1.0 You can specify the length of time
(seconds) to wait between each job that is
submitted when running sub-processes. This can
be helpful on NFS-mounted systems when running
with multiple processors to avoid file
conflicts. The symptom of too short a
wait_between_submit_time is File exists:....
resolve_command_list= None You can supply any resolve command here NOTE:
for command-line usage you need to enclose the
whole set of commands in double quotes (")
and each individual command in single quotes (')
like this: resolve_command_list="'no_build'
'b_overall 23' "
background= None run jobs in background or not (if nproc is greater than
1) Usually set automatically. If run_command is sh or csh,
True
run_command= "sh " Command for running jobs (e.g., sh or qsub )
non_user_params
print_citations= True Print citation information at end of run
| |