def _parse_domain(record: Record, domain: NRPSPKSQualifier.Domain,
feature: CDSFeature) -> JSONDomain:
""" Convert a NRPS/PKS domain string to a dict useable by json.dumps
Arguments:
record: the Record containing the domain
domain: the NRPSPKSQualifier.Domain in question
feature: the CDSFeature that the domain belongs to
Returns:
a populated JSONDomain instance
"""
predictions = list(domain.predictions.items())
# Create url_link to NaPDoS for C and KS domains
napdoslink = ""
domainseq = str(feature.translation)[domain.start:domain.end]
base = ("http://napdos.ucsd.edu/cgi-bin/process_request.cgi?"
"query_type=aa&ref_seq_file=all_{0}_public_12062011.faa"
"&Sequence=%3E{0}_domain_from_antiSMASH%0D{1}")
if domain.name == "PKS_KS":
napdoslink = base.format("KS", domainseq)
elif "Condensation" in domain.name:
napdoslink = base.format("C", domainseq)
blastlink = (
"http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins"
"&PROGRAM=blastp&BLAST_PROGRAMS=blastp"
"&QUERY={}"
"&LINK_LOC=protein&PAGE_TYPE=BlastSearch").format(domainseq)
dna_sequence = feature.extract(record.seq)
abbreviation = _get_domain_abbreviation(domain.name)
return JSONDomain(domain, predictions, napdoslink, blastlink, domainseq,
dna_sequence, abbreviation,
_get_domain_class(abbreviation, domain.name))
def parse_domain(self, domain: NRPSPKSQualifier.Domain, feature: CDSFeature
) -> JSONDomain:
"Convert a NRPS/PKS domain string to a dict useable by json.dumps"
predictions = parse_substrate_predictions(domain.predictions)
# Create url_link to NaPDoS for C and KS domains
napdoslink = ""
domainseq = str(feature.translation)[domain.start:domain.end]
base = ("http://napdos.ucsd.edu/cgi-bin/process_request.cgi?"
"query_type=aa&ref_seq_file=all_{0}_public_12062011.faa"
"&Sequence=%3E{0}_domain_from_antiSMASH%0D{1}")
if domain.name == "PKS_KS":
napdoslink = base.format("KS", domainseq)
elif "Condensation" in domain.name:
napdoslink = base.format("C", domainseq)
blastlink = ("http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins"
"&PROGRAM=blastp&BLAST_PROGRAMS=blastp"
"&QUERY={}"
"&LINK_LOC=protein&PAGE_TYPE=BlastSearch").format(domainseq)
dna_sequence = feature.extract(self.record.seq_record.seq)
return JSONDomain(domain, predictions, napdoslink, blastlink, domainseq, dna_sequence)
def generate_rodeo_svm_csv(
record: Record, query: CDSFeature, leader: str, core: str,
previously_gathered_tabs: List[Union[float,
int]], fimo_motifs: List[int],
fimo_scores: Dict[int, float]) -> List[Union[float, int]]:
"""Generates all the items for a single precursor peptide candidate"""
columns = [] # type: List[Union[float, int]]
# Precursor Index
columns.append(1)
# classification
columns.append(0)
columns += previously_gathered_tabs
# Cluster has PF00733?
if utils.distance_to_pfam(record, query, ['PF00733']) == -1 or \
utils.distance_to_pfam(record, query, ['PF00733']) > 10000:
columns.append(0)
else:
columns.append(1)
# Cluster has PF05402?
if utils.distance_to_pfam(record, query, ['PF05402']) == -1 or \
utils.distance_to_pfam(record, query, ['PF05402']) > 10000:
columns.append(0)
else:
columns.append(1)
# Cluster has PF13471?
if utils.distance_to_pfam(record, query, ['PF13471']) == -1 or \
utils.distance_to_pfam(record, query, ['PF13471']) > 10000:
columns.append(0)
else:
columns.append(1)
# Leader has LxxxxxT motif?
if re.search('(L[ARNDBCEQZGHILKMFPSTWYV]{5}T)', leader):
columns.append(1)
else:
columns.append(0)
# Core has adjacent identical aas (doubles)?
if any(core[i] == core[i + 1] for i in range(len(core) - 1)):
columns.append(1)
else:
columns.append(0)
# Core length (aa)
columns.append(len(core))
# Leader length (aa)
columns.append(len(leader))
# Precursor length (aa)
columns.append(len(leader) + len(core))
# Leader/core ratio
columns.append(len(core) / len(leader))
# Number of Pro in first 9 aa of core?
columns.append(core[:9].count("P"))
# Estimated core charge
charge_dict = {"E": -1, "D": -1, "K": 1, "H": 1, "R": 1}
columns.append(sum([charge_dict[aa] for aa in core if aa in charge_dict]))
# Estimated leader charge
columns.append(sum([charge_dict[aa] for aa in leader
if aa in charge_dict]))
# Estimated precursor charge
columns.append(
sum([charge_dict[aa] for aa in leader + core if aa in charge_dict]))
# Absolute value of core charge
columns.append(
abs(sum([charge_dict[aa] for aa in core if aa in charge_dict])))
# Absolute value of leader charge
columns.append(
abs(sum([charge_dict[aa] for aa in leader if aa in charge_dict])))
# Absolute value of precursor charge
columns.append(
abs(sum([charge_dict[aa] for aa in leader + core
if aa in charge_dict])))
# Counts of AAs in leader
columns += [leader.count(aa) for aa in "ARDNCQEGHILKMFPSTWYV"]
# Aromatics in leader
columns.append(sum([leader.count(aa) for aa in "FWY"]))
# Neg charged in leader
columns.append(sum([leader.count(aa) for aa in "DE"]))
# Pos charged in leader
columns.append(sum([leader.count(aa) for aa in "RK"]))
# Charged in leader
columns.append(sum([leader.count(aa) for aa in "RKDE"]))
# Aliphatic in leader
columns.append(sum([leader.count(aa) for aa in "GAVLMI"]))
# Hydroxyl in leader
columns.append(sum([leader.count(aa) for aa in "ST"]))
# Counts of AAs in core
columns += [core.count(aa) for aa in "ARDNCQEGHILKMFPSTWYV"]
# Aromatics in core
columns.append(sum([core.count(aa) for aa in "FWY"]))
# Neg charged in core
columns.append(sum([core.count(aa) for aa in "DE"]))
# Pos charged in core
columns.append(sum([core.count(aa) for aa in "RK"]))
# Charged in core
columns.append(sum([core.count(aa) for aa in "RKDE"]))
# Aliphatic in core
columns.append(sum([core.count(aa) for aa in "GAVLMI"]))
# Hydroxyl in core
columns.append(sum([core.count(aa) for aa in "ST"]))
# Counts (0 or 1) of amino acids within first AA position of core sequence
columns += [core[0].count(aa) for aa in "ARDNCQEGHILKMFPSTWYV"]
# Counts of AAs in leader+core
precursor = leader + core
columns += [precursor.count(aa) for aa in "ARDNCQEGHILKMFPSTWYV"
] # Temp to work with current training CSV
# Aromatics in precursor
columns.append(sum([precursor.count(aa) for aa in "FWY"]))
# Neg charged in precursor
columns.append(sum([precursor.count(aa) for aa in "DE"]))
# Pos charged in precursor
columns.append(sum([precursor.count(aa) for aa in "RK"]))
# Charged in precursor
columns.append(sum([precursor.count(aa) for aa in "RKDE"]))
# Aliphatic in precursor
columns.append(sum([precursor.count(aa) for aa in "GAVLMI"]))
# Hydroxyl in precursor
columns.append(sum([precursor.count(aa) for aa in "ST"]))
# Motifs
columns += [1 if motif in fimo_motifs else 0 for motif in range(1, 17)]
# Total motifs hit
columns.append(len(fimo_motifs))
# Motif scores
columns += [
fimo_scores[motif] if motif in fimo_motifs else 0
for motif in range(1, 17)
]
# Sum of MEME scores
columns.append(
sum([
fimo_scores[motif] if motif in fimo_motifs else 0
for motif in range(1, 17)
]))
# No Motifs?
if not fimo_motifs:
columns.append(1)
else:
columns.append(0)
# Alternate Start Codon?
if not str(query.extract(record.seq)).startswith("ATG"):
columns.append(1)
else:
columns.append(0)
return columns
def acquire_rodeo_heuristics(record: Record, cluster: Cluster,
query: CDSFeature, leader: str,
core: str) -> Tuple[int, List[Union[float, int]]]:
"""Calculate heuristic scores for RODEO"""
tabs = [] # type: List[Union[float, int]]
score = 0
# Calcd. lasso peptide mass (Da) (with Xs average out)
core_analysis = utils.RobustProteinAnalysis(core,
monoisotopic=True,
ignore_invalid=False)
tabs.append(float(core_analysis.molecular_weight()))
# Distance to any biosynthetic protein (E, B, C)
hmmer_profiles = ['PF13471', 'PF00733', 'PF05402']
distance = utils.distance_to_pfam(record, query, hmmer_profiles)
tabs.append(distance)
# Within 500 nucleotides of any biosynthetic protein (E, B, C) +1
if distance < 500:
score += 1
tabs.append(1)
else:
tabs.append(0)
# Within 150 nucleotides of any biosynthetic protein (E, B, C) +1
if distance < 150:
score += 1
tabs.append(1)
else:
tabs.append(0)
# Greater than 1000 nucleotides from every biosynthetic protein (E, B, C) -2
if distance > 1000:
score -= 2
tabs.append(1)
else:
tabs.append(0)
# Core region has 2 or 4 Cys residues +1
if core.count("C") in [2, 4]:
score += 1
tabs.append(1)
else:
tabs.append(0)
# Leader region is longer than core region +2
if len(leader) > len(core):
score += 2
tabs.append(1)
else:
tabs.append(0)
# Core has 7 (Glu) or 8(Glu/Asp) or 9 (Asp) membered ring possible +1
if 'E' in core[6:8] or 'D' in core[7:9]:
score += 1
tabs.append(1)
else:
tabs.append(0)
# Leader region contains GxxxxxT +3
if re.search('(G[ARNDBCEQZGHILKMFPSTWYV]{5}T)', leader):
score += 3
tabs.append(1)
else:
tabs.append(0)
# Core starts with G +2
if core.startswith("G"):
score += 2
tabs.append(1)
else:
tabs.append(0)
# Peptide and lasso cyclase are on same strand +1
if is_on_same_strand_as(cluster, query, 'PF00733'):
score += 1
tabs.append(1)
else:
tabs.append(0)
# Leader/core region length ratio < 2 and > 0.5 +1
if 0.5 <= len(leader) / len(core) <= 2:
score += 1
tabs.append(1)
else:
tabs.append(0)
# Core starts with Cys and has an even number of Cys 0
if core.startswith("C") and core.count("C") % 2 == 0:
score += 0
tabs.append(1)
else:
tabs.append(0)
# Core contains no Gly -4
if "G" not in core:
score -= 4
tabs.append(1)
else:
tabs.append(0)
# Core has at least one aromatic residue +1
if set("FWY") & set(core):
score += 1
tabs.append(1)
else:
tabs.append(0)
# Core has at least 2 aromatic residues +2
if sum([core.count(aa) for aa in list("FWY")]) >= 2:
score += 2
tabs.append(1)
else:
tabs.append(0)
# Core has odd number of Cys -2
if core.count("C") % 2 != 0:
score -= 2
tabs.append(1)
else:
tabs.append(0)
# Leader region contains Trp -1
if "W" in leader:
score -= 1
tabs.append(1)
else:
tabs.append(0)
# Leader region contains Lys +1
if "K" in leader:
score += 1
tabs.append(1)
else:
tabs.append(0)
# Leader region has Cys -2
if "C" in leader:
score -= 2
tabs.append(1)
else:
tabs.append(0)
# Gene cluster does not contain PF13471 -2
if utils.distance_to_pfam(record, query, ['PF13471']) == -1 or \
utils.distance_to_pfam(record, query, ['PF13471']) > 10000:
score -= 2
# Peptide utilizes alternate start codon -1
if not str(query.extract(record.seq)).startswith("ATG"):
score -= 1
return score, tabs
def get_description(
record: Record, feature: CDSFeature, type_: str, options: ConfigType,
mibig_result: List[clusterblast.results.MibigEntry]) -> str:
"Get the description text of a CDS feature"
blastp_url = "http://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins&" \
"PROGRAM=blastp&BLAST_PROGRAMS=blastp&QUERY=%s&" \
"LINK_LOC=protein&PAGE_TYPE=BlastSearch" % feature.translation
genomic_context_url = "http://www.ncbi.nlm.nih.gov/projects/sviewer/?" \
"Db=gene&DbFrom=protein&Cmd=Link&noslider=1&"\
"id=%s&from=%s&to=%s"
template = '<span class="svgene-tooltip-bold">%s</span><br>\n' % feature.product or feature.get_name(
)
template += 'Locus-tag: %s; Protein-ID: %s<br>\n' % (feature.locus_tag,
feature.protein_id)
if feature.get_qualifier('EC_number'):
template += "EC-number(s): %s<br>\n" % ",".join(
feature.get_qualifier('EC_number'))
for gene_function in feature.gene_functions:
template += "%s<br>\n" % str(gene_function)
template += "Location: %d - %d<br><br>\n" % (
feature.location.start + 1, # 1-indexed
feature.location.end)
if mibig_result:
cluster_number = feature.cluster.get_cluster_number()
mibig_homology_file = os.path.join(
options.output_dir, "knownclusterblast",
"cluster%d" % cluster_number,
feature.get_accession() + '_mibig_hits.html')
generate_html_table(mibig_homology_file, mibig_result)
mibig_path = mibig_homology_file[len(options.output_dir) + 1:]
template += '<br><a href="%s" target="_new">MiBIG Hits</a><br>\n' % mibig_path
if type_ == 'transport':
url = "http://blast.jcvi.org/er-blast/index.cgi?project=transporter;" \
"program=blastp;database=pub/transporter.pep;" \
"sequence=sequence%%0A%s" % feature.translation
template += '<a href="%s" target="_new">TransportDB BLAST on this gene<br>' % url
key = record.id + "_" + feature.get_name()
if key in searchgtr_links:
url = searchgtr_links[key]
template += '<a href="%s" target="_new">SEARCHGTr on this gene<br>\n' % url
template += '<a href="%s" target="_new">NCBI BlastP on this gene</a><br>\n' % blastp_url
context = genomic_context_url % (
record.id, max(feature.location.start - 9999,
0), min(feature.location.end + 10000, len(record)))
template += """<a href="%s" target="_new">View genomic context</a><br>\n""" % context
if options.smcogs_trees:
for note in feature.notes: # TODO find a better way to store image urls
if note.startswith('smCOG tree PNG image:'):
url = note.split(':')[-1]
entry = '<a href="%s" target="_new">View smCOG seed phylogenetic tree with this gene</a>\n'
template += entry % url
break
template += generate_asf_tooltip_section(record, feature)
go_notes = generate_pfam2go_tooltip(record, feature)
if go_notes:
template += '<br><span class="bold">Gene Ontology terms for PFAM domains:</span><br>\n' \
'%s<br><br>\n' % "<br>".join(go_notes)
clipboard_fragment = """<a href="javascript:copyToClipboard('%s')">Copy to clipboard</a>"""
template += "AA sequence: %s<br>\n" % (clipboard_fragment %
feature.translation)
template += "Nucleotide sequence: %s<br>\n" % (clipboard_fragment %
feature.extract(record.seq))
return "".join(char for char in template if char in string.printable)
