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KP.2.yaml
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KP.2.yaml
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escapecalculator:
virus: BA.2 # virus to use for escape calculator
weight: 1 # weight assigned to escape calculator, score is raised to this power
# ACE2 affinity / RBD expression deep mutational scanning
rbd_dms:
data: https://media.githubusercontent.com/media/tstarrlab/SARS-CoV-2-RBD_DMS_Omicron-XBB-BQ/main/results/final_variant_scores/final_variant_scores.csv
target: Omicron_BA2
clip: 2 # the contribution to score is exponential of the delta DMS value, clipped at this max
weights: # weights assigned to each phenotype
delta_bind: 1
delta_expr: 1
# Fitness estimates from natural sequences
# Note these also capture nucleotide diversity
# We also average in favorable clade-specific estimates for the recent clade to help
# better capture recently favorable mutations, but only keep positive values for those
fitness_estimates:
fitness: https://raw.githubusercontent.com/jbloomlab/SARS2-mut-fitness/main/results_gisaid_2024-04-24/aa_fitness/aa_fitness.csv
by_clade: https://media.githubusercontent.com/media/jbloomlab/SARS2-mut-fitness/main/results_gisaid_2024-04-24/aa_fitness/aamut_fitness_by_clade.csv
clade: 23I # BA.2.86
clade_min_count: 5 # use clade-specific estimate if >0 and either expected or actual counts exceed this
fitness_min_count: 10 # drop mutation if expected count < this in fitness estimates
clip: 2 # contribution to the score is exponential of fitness effect, clipped at this max
weights:
# weights assigned to each phenotype
fitness: 0.5
by_clade_effect: 0.5
# full-spike deep mutational scanning
spike_dms:
csv: https://raw.githubusercontent.com/dms-vep/SARS-CoV-2_XBB.1.5_spike_DMS/main/results/summaries/summary.csv
weights: # weights assigned to each phenotype
sera escape: 3
spike mediated entry: 1
ACE2 binding: 0.5
# manually adjust weights of these mutations by these factors:
manual_weights:
Q493E: 200 # found in KP.3 which is growing fast, and Yunlong Cao shows has escape, but scores badly for ACE2 binding in DMS