# Sample configuration file for evcouplings monomer protein prediction pipeline.
# This file determines all aspects of the computation:
# - which compute environment to use
# - which stages of the pipeline to run
# - what the settings for each of the stages are

# Minimal settings required before this configuration can be executed:
# - set your environment, paths to tools and databases (at the end of this file)
# - under "global", set prefix and sequence_id
# - run it! :)

# Configuration rules:
# 1) Global settings override settings for stages
# 2) Outputs of a stage are merged into "global" and fed into the input of subsequent stages
#    (e.g., the alignment_file output of align will be used by the alignment_file input of couplings)
# 3) All settings are explicitly specified here. No hidden defaults in code.
# 4) Each stage is also passed the parameters in the "databases" and "tools" sections

pipeline: protein_monomer

# which stages of workflow to run. Uncomment downstream stages using # (however, no stage can be run before the previous
# stage has been run)
stages:
    - align
#    - couplings
#    - compare
#    - mutate
#    - fold

# Global job settings (which protein, region). These will override settings of the same name in each of the stages.
# These are typically the settings you want to modify for each of your jobs, together with some settings in the align stage.
global:
    # mandatory output prefix of the job (e.g. output/HRAS will store outputs in folder "output", using files prefixed with "HRAS")
    prefix: 

    # mandatory sequence identifier (mandatory, even if sequence_file is given)
    sequence_id: 

    # optional FASTA file with target sequence (if blank, will fetch try to fetch sequence_id from databases.sequence_download_url)
    # if sequence_file is set, sequence_id must be defined, but can be arbitrary identifier (does not have to match ID in file)
    sequence_file: 

    # cut to subregion of sequence (specify as list, e.g. [24, 286], leave blank for full sequence)
    region:

    # Clustering threshold for downweighting redudant sequences (Meff computation). E.g. 0.8 will cluster sequences
    # at a 80% sequence identity cutoff
    theta: 0.8

    # number of cores to use. If running through evcouplings application, will be overriden by environment.cores
    cpu:

# Specify multiple batch jobs (if empty, only a single job will be run). Each entry (e.g. b_0.75) will be appended to
# global.prefix to uniquely identify the subjob. Parameters for individual stages that should be overridden for each
# subjob have to be specified, for all other parameters jobs share the same values.
batch:
#    _b0.75:
#       align: {domain_threshold: 0.75, sequence_threshold: 0.75}
#    _b0.3:
#       align: {domain_threshold: 0.3, sequence_threshold: 0.3}

# Sequence alignment generation/processing.
align:
    # standard: iterative sequence search and postprocessing using jackhmmer.
    protocol: standard

    # The following fields usually do not need to be set, since "global" defines them.
    # prefix:
    # sequence_id:
    # sequence_file:
    # region:
    # theta:

    # index of first residue in sequence_id / sequence_file. This can be used to renumber sequences that already have
    # been cut to a subsequence
    first_index: 1

    # Use bitscore threshold instead of E-value threshold for sequence search
    use_bitscores: True

    # jackhmmer domain- and sequence-level inclusion thresholds.
    # if use_bitscores is True:
    # - floating point number will be interpreted as a relative bitscore threshold (bits/residue)
    # - integer will be interpreted as an absolute bitscore threshold
    # if use_bitscore is False:
    # - mantissa-exponent string or float will be interpreted literally
    # - integer will be interpreted as negative of the exponent (10 -> 1E-10)
    domain_threshold: 0.5
    sequence_threshold: 0.5

    # number of jackhmmer iterations
    iterations: 5

    # sequence database (specify possible databases and paths in "databases" section below)
    database: uniref100

    # compute the redundancy-reduced number of effective sequences (M_eff) already in the alignment stage.
    # To save compute time, this computation is normally carried out in the couplings stage
    compute_num_effective_seqs: False

    # Filter sequence alignment at this % sequence identity cutoff. Can be used to cut computation time in
    # the couplings stage (e.g. set to 95 to remove any sequence that is more than 95% identical to a sequence
    # already present in the alignment). If blank, no filtering. If filtering, HHfilter must be installed.
    seqid_filter: 98

    # Only keep sequences that align to at least x% of the target sequence (i.e. remove fragments)
    minimum_sequence_coverage: 50

    # Only include alignment columns with at least x% residues (rather than gaps) during model inference
    minimum_column_coverage: 70

    # Create a file with extracted annotation from UniRef/UniProt sequence FASTA headers
    extract_annotation: True
    cpu:

    # set to True to turn of jackhmmer bias correction
    nobias: False

    # if align stage has been run previously, reuse the generated raw sequence alignment coming out of jackhmmer
    reuse_alignment: True

    # create checkpoint files of HMM and aligment after each iteration
    checkpoints_hmm: False
    checkpoints_ali: False

# Alternative protocol: reuse existing alignment and apply postprocessing to generate alignment that is consistent
# with pipeline requirements. Uncomment, and comment all values in align section above to enable the "existing" protocol
#    protocol: existing
#    prefix:
#    # Path of input alignment. Alignment needs to contain region in form SEQID/start-end, or first_index must be set
#    input_alignment:
#    sequence_id:
#    first_index:
#    compute_num_effective_seqs: False
#    theta:
#    seqid_filter:
#    minimum_sequence_coverage: 50
#    minimum_column_coverage: 70
#    extract_annotation: True

# Inference of evolutionary couplings from sequence alignment
couplings:
    # current options: 
    # - standard (model inference using plmc)
    # - mean_field (mean field direct coupling analysis, see below)
    protocol: standard

    # number of plmc iterations
    iterations: 100

    # specify custom alphabet as a string. Gap symbol must be first character
    alphabet:

    # Treat gaps as missing data during model inference
    ignore_gaps: True

    # strength of regularization on coupling parameters J
    lambda_J: 0.01

    # adjust for larger number of coupling parameters relative to number of fields h (multiply by model length and
    # number of states)
    lambda_J_times_Lq: True

    # strength of regularization on fields h
    lambda_h: 0.01
    lambda_group:
    scale_clusters:
    
    # reuse ECs and model parameters, if this stage has been run before
    reuse_ecs: True

    # Sequence separation filter for generation of CouplingScores_longrange.csv table (i.e. to take out short-range
    # ECs from table, only pairs with abs(i-j)>=min_sequence_distance will be kept.
    min_sequence_distance: 6

    # Post-inference scoring of ECs to derive probabilities
    # Options are: skewnormal, normal, logistic_regression
    scoring_model: logistic_regression
    
# Alternative protocol: compute couplings with mean field direct coupling analysis
# Uncomment, and comment all values in align section above to enable the mean_field protocol
    # protocol: mean_field

    # Options: cn, di, mi_apc, mi
    # ec_score_type: cn

    # Post-inference scoring of ECs to derive probabilities - only available if used_score == "cn"!
    # Options are: skewnormal, normal, logistic_regression
    # scoring_model: logistic_regression

    # pseudo_count: 0.5
    # alphabet:
    # ignore_gaps: False
    # reuse_ecs: True
    # min_sequence_distance: 6

    # Following input parameters will usually be overriden by "global" and outputs of "align" stage
    # prefix:
    # alignment_file:
    # focus_sequence:
    # focus_mode: True
    # segments:
    # cpu:
    # theta:

# Compare ECs to known 3D structures
compare:
    # Current options: standard
    protocol: standard

    # Following parameters will be usually overriden by global settings / output of previous stage
    prefix:
    sequence_id:
    ec_file:
    target_sequence_file:

    # If True, find structures by sequence alignment against the PDB, otherwise identify structures using
    # sequence_id and SIFTS database (sequence_id must be UniProt AC/ID in this case)
    by_alignment: True

    # Leave this parameter empty to use all PDB structures for given sequence_id, otherwise
    # will be limited to the given IDs (single value or list). Important: note that this acts only as a filter on the
    # structures found by alignment or in the SIFTS table (!)
    pdb_ids:

    # Limit number of structures and chains for comparison
    max_num_structures: 10
    max_num_hits: 25

    # compare to multimer contacts (if multiple chains of the same sequence or its homologs are present in a structure)
    compare_multimer: True

    # settings for sequence alignment against PDB sequences using jackhmmer
    # (additional settings like iterations possible, compare to align stage)
    sequence_file:
    first_index:
    region:
    alignment_min_overlap: 20
    use_bitscores: True
    domain_threshold: 0.1
    sequence_threshold: 0.1

    # Comparison and plotting settings
    
    # Filter that defines which atoms will be used for distance calculations. If empty/None, no filter will be
    # applied (resulting in the computation of minimum atom distances between all pairs of atoms). If setting to any
    # particular PDB atom type, only these atoms will be used for the computation (e.g. CA will give C_alpha distances,
    # CB will give C_beta distances, etc.)
    atom_filter:

    # Distance cutoff (Angstrom) for a true positive pair
    distance_cutoff: 5

    # Only long-range pairs with abs(i-j)>= min_sequence_distance will be used for CouplingScoresCompared_longrange.csv file
    min_sequence_distance: 6

    # Plot contact maps with ECs above these mixture model probability cutoffs
    plot_probability_cutoffs: [0.90, 0.99]

    # Plot fixed numbers of ECS. Integers will be interpreted as absolute numbers, floats as fractions of L (model length)
    plot_lowest_count: 0.05
    plot_highest_count: 1.0
    plot_increase: 0.05

    # Axis boundaries of contact map plot depending on range of ECs and structure.
    # Options: union, intersection, ecs, structure, [start, end] (e.g. [100, 200])
    boundaries: union

    # scale sizes of EC dots in scatter plot based on strength of EC score
    scale_sizes: True

    # draw secondary structure on contact map plots
    draw_secondary_structure: True

    # draw structure and alignment/EC coverage information on contact map plots
    draw_coverage: True

    # print information about used PDB structures on contact map plots
    print_pdb_information: True

    # Alignment method to use to search the PDB Seqres database. Options: jackhmmer, hmmsearch
    # Set to jackhmmer to search the PDB Seqres database using jackhmmer from the target sequence only (more stringent). 
    # Set to hmmsearch to search the PDB seqres database using an HMM built from the output monomer alignment (less stringent). 
    # Warning: searching by HMM may result in crystal structures from very distant homologs or even unrelated sequences. 
    pdb_alignment_method: jackhmmer

# Settings for Mutation effect predictions
mutate:
    # Options: standard
    protocol: standard

    # predict the following dataset file (.csv file, mutants like A102V or A102V,K199W in column "mutant")
    mutation_dataset_file:

    # Inputs set by global stage and output of previous stages
    # prefix:
    # model_file:

# Settings for 3D structure prediction
fold:
    # Options: standard
    protocol: standard

    # Options: cns_dgsa
    engine: cns_dgsa

    # Config file. If blank, default configuration (restraints.yml) in package will be used
    folding_config_file:

    # If True, limit 3D modeling only to that region of sequence that actually has sequence alignment coverage)
    cut_to_alignment_region: True

    # Method for secondary structure prediction (options: psipred, requires PSIPRED installation). Will be used
    # to generate distance and dihedral angle restraints for local geometry.
    sec_struct_method: psipred

    # If secondary structure was already predicted in previous execution of configuration, reuse the file
    reuse_sec_struct: True

    # Instead of predicting secondary structure in pipeline, can specify a secondary structure file:
    # Must be csv file with columns i (position), A_i (residue) and sec_struct_3state (secondary structure, H, E or C
    # for helix, sheet or coil)
    sec_struct_file:

    # Do not use EC pairs as distance restraints that are geometrically incompatible with predicted secondary structure
    filter_sec_struct_clashes: True

    # Only use ECs with sequence distance abs(i-j) >= min_sequence_distance for folding
    min_sequence_distance: 6

    # Predict structures using all ECs above these probability cutoffs according to mixture model
    fold_probability_cutoffs: [0.90, 0.99]

    # Predict structures with selected number of ECs.
    # Integers will be interpreted as absolute number of ECs, floats as a fraction of L (model length)
    fold_lowest_count: 0.5
    fold_highest_count: 1.3
    fold_increase: 0.05

    # number of trial structure to generate for each EC set
    num_models: 10

    # remove intermediate files created during folding and keep only final models
    cleanup: True

    # Inputs defined by "global" or previous stages
    # prefix:
    # ec_file:
    # target_sequence_file:
    # segments:
    # remapped_pdb_files:
    # cpu:

# These settings allow job status tracking using a database, and result collection in an archive
management:
    # URI of database
    database_uri:

    # unique job identifier
    job_name:

    # add the following output files to results archive
    archive: [target_sequence_file, statistics_file, alignment_file, frequencies_file, ec_file, ec_longrange_file,
              model_file, enrichment_file, evzoom_file, enrichment_pml_files, ec_lines_pml_file, contact_map_files,
              ec_compared_all_file, ec_compared_longrange_file, remapped_pdb_files, mutations_epistatic_pml_files,
              mutation_matrix_file, mutation_matrix_plot_files, secondary_structure_pml_file, folding_ec_file,
              folded_structure_files, folding_ranking_file, folding_comparison_file, folding_individual_comparison_files,
              ec_lines_compared_pml_file, pdb_structure_hits_file, sec_struct_file]

    # Delete the following output files after running the job if you don't need them, to save disk space.
    # Note that this may jeopardize your ability to rerun parts of the job if intermediate files are missing.
    # The following, deactivated default deletes the biggest output files.
    # delete: [raw_alignment_file, model_file]

# Computational environment for batch jobs (using evcouplings command line application)
environment:
    # current options for engine: lsf, local, slurm (for local, only set cores and leave all other fields blank)
    # If your batch engine of choice (e.g. SGE, Torque) is not available yet, please consider contributing by
    # implementing it and submitting a pull request!
    # Note that "cores" will override the "cpu" parameter for "global"
    engine: 
    queue: 
    cores: 6
    memory: 
    time: 

    # Special setting for "local" engine to define number of workers running in parallel
    # (note that "cores" has to be defined above to make sure each job only uses a defined
    # number of cores). If not defined or None, will default to number of cores / cores per job;
    # otherwise specify integer to limit number of workers (1 for serial execution of subjobs)
    # parallel_workers: 1

    # command that will be executed before running actual computation (can be used to set up environment)
    configuration:
        

# Paths to databases used by evcouplings.
databases:
    # Sequence databases (only download the ones you want to use). You can also specify arbitrary databases in FASTA format
    # using a database name of your choice here)
    uniprot: /n/groups/marks/databases/jackhmmer/uniprot/uniprot_current.o2.fasta
    uniref100: /home/tekpinar/databases/uniref100.fasta
    uniref90: /home/tekpinar/databases/uniref90.fasta

    # URL do download sequences if sequence_file is not given. {} will be replaced by sequence_id.
    sequence_download_url: http://rest.uniprot.org/uniprot/{}.fasta

    # Directory with PDB MMTF structures (leave blank to fetch structures from web)
    pdb_mmtf_dir:

    # SIFTS mapping information. Point to file paths in an existing directory, and if these files do not exist, they will be
    # automatically generated and saved at the given file path (this may take a while).
    # Periodically delete these files to more recent versions of SIFTS are used.
    sifts_mapping_table: /n/groups/marks/databases/SIFTS/pdb_chain_uniprot_plus_current.o2.csv
    sifts_sequence_db: /n/groups/marks/databases/SIFTS/pdb_chain_uniprot_plus_current.o2.fasta

# Paths to external tools used by evcouplings. Please refer to README.md for installation instructions and which tools are required.
tools:
    jackhmmer: /usr/bin/jackhmmer
    plmc: /usr/local/bin/plmc
    hmmbuild: /usr/bin/hmmbuild
    hmmsearch: /usr/bin/hmmsearch
    hhfilter: /home/tekpinar/research/lcqb/hhsuite/bin/hhfilter
    psipred: /n/groups/marks/software/runpsipred_o2
    cns: /n/groups/marks/pipelines/evcouplings/software/cns_solve_1.21/intel-x86_64bit-linux/bin/cns
    maxcluster: /n/groups/marks/pipelines/evcouplings/software/maxcluster64bit
