This command takes a list of profiles, specified in profile_list_file, to calculate their Position Specific Scoring Matrices (PSSM) and create a database of these PSSMs for use in Profile.scan().
The profiles listed in profile_list_file should be in a format that is understood by Profile.read(). For instance, like those created by Profile.build() or Alignment.to_profile. See documentation under Profile.read() for help on profile_format.
rr_file is the residue-residue substitution matrix to use when calculating the position-specific scoring matrix (PSSM). The current implementation is optimized only for the BLOSUM62 matrix.
matrix_offset is the value by which the scoring matrix is offset during dynamic programming. For the BLOSUM62 matrix use a value of -450.
pssmdb_name is the name for the output PSSM database.
# Example for: Profile.scan() from modeller import * env = Environ() # First create a database of PSSMs env.make_pssmdb(profile_list_file = 'profiles.list', matrix_offset = -450, rr_file = '${LIB}/blosum62.sim.mat', pssmdb_name = 'profiles.pssm', profile_format = 'TEXT', pssm_weights_type = 'HH1') # Read in the target profile prf = Profile(env, file='T3lzt-uniprot90.prf', profile_format='TEXT') # Read the PSSM database psm = PSSMDB(env, pssmdb_name = 'profiles.pssm', pssmdb_format = 'text') # Scan against all profiles in the 'profiles.list' file # The score_statistics flag is set to false since there are not # enough database profiles to calculate statistics. prf.scan(profile_list_file = 'profiles.list', psm = psm, matrix_offset = -450, ccmatrix_offset = -100, rr_file = '${LIB}/blosum62.sim.mat', gap_penalties_1d = (-700, -70), score_statistics = False, output_alignments = True, output_score_file = None, profile_format = 'TEXT', max_aln_evalue = 1, aln_base_filename = 'T3lzt-ppscan', pssm_weights_type = 'HH1', summary_file = 'T3lzt-ppscan.sum')