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▀ protein mutation burden
# 1 2 3 4 5 6
# CVQLQQSGA VQLQQSGAE QLQQSGAEL LQQSGAELA QQSGAELAR QSGAELARP queries
# QVQLQQSGP VQLMQSGAE QLMQSGAEV LHQSGSELA QQSGSELAL PSAAELARP hits
# ^ ^ ^ ^ + ^ + + ^ ^ ^
Python library for computing protein humanness (or mouse-ness, dog-ness, cow-ness, ...) based on average number of mutations to nearest peptide in a reference proteome (Human SwissProt, human antibody repertoires in OAS, or a custom reference DB).
A database of human peptides (or other reference proteome) is loaded in-memory into a big python set for fast lookups.
Near matches are found using an optimized lookup strategy implemented with cdist function in scipy.
Human SwissProt and Human OAS are stored as part of this package. A custom reference DB can be provided as a gzipped fasta or text file.
Note
More human doesn't always mean "good". Peptides like "GGGGGGGGG" or "EKEKEKEKE" are human but you don't necessarily want your protein to contain those. Make sure to check for sequence entropy, AlphaFold2 confidence, or other quality metrics.
Try out promb on HuggingFace Spaces: https://huggingface.co/spaces/prihodad/promb-humanness
Install using pip:
pip install prombCheck which peptides in a sequence are present in the DB (exact match, very fast):
# FASTA file(s)
promb exact -d human-reference -l 9 -o output_matches.csv query.fasta
# PDB file(s)
promb exact -d human-reference -l 9 -o output_matches.csv --chain A ./pdb_directory/
# Output:
# File,ID,Position,Peptide,Found
# test_antibody.fa,Pembrolizumab,1,EIVLTQSPA,True
# test_antibody.fa,Pembrolizumab,2,IVLTQSPAT,True
# test_antibody.fa,Pembrolizumab,3,VLTQSPATL,True
Find nearest human peptides (a lot slower than exact match search). Peptides are ranked by identity (number of mutations), ties are resolved by BLOSUM similarity. The search is exhaustive - we don't use any pre-filtering step, all peptides from reference database are compared to the query.
# FASTA file(s)
promb nearest -d human-reference -l 9 -o output_matches.csv query.fasta
# PDB file(s)
promb nearest -d human-reference -l 9 -o output_matches.csv --chain A ./pdb_directory/
# Output:
# File,ID,Position,Peptide,Nearest,Mutations
# test_antibody.fa,Pembrolizumab,1,EIVLTQSPA,EIVLTQSPA,0
# test_antibody.fa,Pembrolizumab,2,IVLTQSPAT,IVLTQSPAT,0
# test_antibody.fa,Pembrolizumab,3,VLTQSPATL,VLTQSPATL,0You can use promb for very fast computation of the OASis antibody humanness score from BioPhi:
# FASTA file(s)
promb oasis -o output_scores.csv ./input/sequences/
# Output:
# File,ID,Identity
# test_antibody.fa,Pembrolizumab,0.7572815533980582
# test_antibody.fa,Pembrolizumab,0.7142857142857143First, initialize the in-memory peptide database using init_db:
from promb import init_db
# Use pre-defined Human reference proteome (UP000005640)
db = init_db('human-reference', 9)
# Use pre-defined Human SwissProt DB
db = init_db('human-swissprot', 9)
# Use pre-defined Human OAS DB (antibody peptides found in >=10% of subjects)
db = init_db('human-oas')
# Use custom path with gzipped fasta file
db = init_db('path/to/db.fasta.gz', 9)
# Use custom path with gzipped text file, one peptide per line
db = init_db('path/to/db.txt.gz')Check which peptides in a sequence are present in the DB (exact match, very fast):
from promb import init_db
db = init_db('human-oas')
db.contains('HWGRRKAWC')
# True
db.contains('MGPLHQFLL')
# False
# Use 'compute_peptide_content' to compute OASis-like score (% of peptides that are human, exact match)
db.compute_peptide_content('QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS')
# 0.7142857142857143Find nearest human peptides (a lot slower than exact match search). Peptides are ranked by identity (number of mutations), ties are resolved by BLOSUM similarity. The search is exhaustive - we don't use any pre-filtering step, all peptides from reference database are compared to the query.
from promb import init_db, print_nearest
db = init_db('human-reference', 9)
peptides = db.chop_seq_peptides('CVQLQQSGAELARPPASVKMSCKAS')
# ['CVQLQQSGA', 'VQLQQSGAE', 'QLQQSGAEL', ...]
nearest = db.find_nearest_peptides(peptides, 1)
# [['QVQLQQSGP'], ['VQLMQSGAE'], ['QLMQSGAEV'], ...]
print_nearest(peptides, nearest)
# ^ = mutation, + = similar residue
#
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# CVQLQQSGA VQLQQSGAE QLQQSGAEL LQQSGAELA QQSGAELAR QSGAELARP SGAELARPP GAELARPPA AELARPPAS ELARPPASV queries
# QVQLQQSGP VQLMQSGAE QLMQSGAEV LHQSGSELA QQSGSELAL PSAAELARP SGAELRQPP GAELLEPPA LELQRPPAS ELQRPPAST hits
# ^ ^ ^ ^ + ^ + + ^ ^ ^ ^+ ^^ ^ ^ ^ ^
# 11 12 13 14 15 16 17
# LARPPASVK ARPPASVKM RPPASVKMS PPASVKMSC PASVKMSCK ASVKMSCKA SVKMSCKAS queries
# LAMPPASVK AMPPASVKV RPPASVGMS PPASYKSSC VASTKMSCK ASVKVSCKA SVKVSCKAS hits
# ^ ^ + ^ ^ ^ ^ ^ + + Compute average number of non-human mutations per peptide:
mut = db.compute_average_mutations('CVQLQQSGAELARPPASVKMSCKAS')
mut
# 1.176 mutations per peptideSince this is slow for peptides with more than 2 mutations, we enable capping the number of mutations per peptide to speed up computation:
mut = db.compute_average_mutations('ELVISISALIVE', max=3)
mut
# 2.75 mutations per peptide (capped at 3 - peptides with more mutations will be counted as 3 mutations)Promb is building upon our previous work - OASis antibody humanness metric:
David Prihoda, Jad Maamary, Andrew Waight, Veronica Juan, Laurence Fayadat-Dilman, Daniel Svozil & Danny A. Bitton (2022) BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning, mAbs, 14:1, DOI: https://doi.org/10.1080/19420862.2021.2020203
OASis is based on antibody repertoires from the Observed Antibody Space:
Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018). Observed Antibody Space: A Resource for Data Mining Next-Generation Sequencing of Antibody Repertoires. The Journal of Immunology, 201(8), 2502–2509. https://doi.org/10.4049/jimmunol.1800708