MAPK LABEL-seq — a pathway-scale variant effect atlas

Functional characterisation of variant effects across 17 core MAPK pathway proteins, measured with LABEL-seq.

Authors and contributions

• Sriram Pendyala — design, data, and analysis.
• Jessica Simon — the LABEL-seq method (Simon, Fowler & Maly, Nature Methods 2025) and experiments, data, and the original scoring and annotation analysis this pipeline was rebuilt from.
• Claude (Anthropic, Opus 5) — pipeline, analysis scripts and documentation, written with Sriram.

Fowler and Maly Labs, Department of Genome Sciences, University of Washington.

What is measured

Every variant is assayed in up to three ways, each read out by sequencing a barcode library:

assay	readout	question
abundance	Flag / HaloTag	how much protein is there
activity	pERK reporter ratio	what does it do to pathway output
interaction	Strep / Flag	does it still bind the partner (KRAS–LZTR1)

Abundance is also measured under HSP90 inhibition (pimitespib), which is what makes the chaperone-buffering analysis possible.

Proteins. RTKs EGFR, ERBB2, MET, RET · GTPases KRAS, MRAS · adaptor GRB2 · GEFs SOS1, SOS2 · RAFs ARAF, BRAF, CRAF · MEKs MEK1, MEK2 · scaffolds KSR1, KSR2 · phosphatase SHP2.

Scale. 531,797 variant effects over 168,896 distinct variants, across 22 libraries, 3 assays and 8 treatment arms. Every variant class is scored, not only the ones expressible as a single substitution: mid-codon 3-nt deletions, frameshifts and multi-mutants are named by their protein consequence and kept.

The notebooks

Run them in this order. Each writes the table the next one reads.

notebook	does
Scoring.ipynb	barcode counts → per-variant scores. Canonical protein-level identity, the filtering and normalisation cascade, the replicate gain correction, the standard curve, and the empty-vector dominant-negative thresholds.
Annotations.ipynb	joins the annotation layers — dominant negatives, curated PDB interfaces, HSP90/CDC37 contacts, conservation, ΔΔG, kinase motifs, population and tumour observation — into the single table the figure notebooks read.
DN.ipynb	figure 3: what dominant-negative variants are, where they sit in the structure, and how they are depleted from population databases.
HSP90i.ipynb	figure 4: chaperone buffering — which variants are rescued by HSP90 and what predicts it.

utils.py holds the shared loaders and the rendering helpers the figure notebooks use, so the notebooks stay about the analysis rather than about matplotlib.

Two things worth knowing before reading the tables

A row is a variant effect, not a variant. The key is (variant, library, assay, assay_treatment); the same variant appears in up to eight rows. Aggregating without grouping on that key mixes assays and treatments.

library is not protein. Several proteins were scanned in two halves — araf_cterm and araf_nterm are different constructs of ARAF, each spanning the full protein.

The two score tables

The pipeline writes two tables. Which one you want depends on what you are doing.

table	columns	what it is
raw_scores.tsv	38	The measurements. Identity, reference sequence (UniProt isoform, RefSeq, Ensembl, and GRCh38 coordinates where the protein change has a resolvable nucleotide route), barcode support, the uncorrected per-replicate ratios and scores with their standard curve, and the low/wt-like/high classification. Written by scripts/export_raw_scores.py.
scores_reannotated.tsv	96	Everything the figures need: the gain-corrected replicates, dominant-negative calls, the HSP90 dependence and buffering families, structure, conservation, PTMs, clinical and population annotation. Written by scripts/reannotate_scores.py.

Each table has its own column reference — every column, in file order, with what it means and where it came from: docs/scores_reannotated_columns.md and docs/raw_scores_columns.md.

raw_scores.tsv is a strict column selection of the annotated table, so the values agree exactly. Two names differ on purpose, and the difference has to survive a join on (variant, library, assay, assay_treatment):

• score_j is the raw WT-relative score in both tables. average_score in the raw table is the mean of those three; average score in the annotated table is the mean of the corrected replicates.
• std_adj_score_j in the raw table is the standard curve fitted on the raw replicates; intercept_0_std_adj_score_j in the annotated table is fitted on the corrected ones.

The correction. Two of 192 replicates resolve materially less of their assay's dynamic range than their two siblings do, and are corrected by a single exponent about wild type, clamped outside the measured range; the other 190 come through bit-identical. scripts/gain_correction.py carries the method, the thresholds and the evidence. The raw table has none of it.

What is here, and what is not

Only what the pipeline needs to run, plus the column reference:

• the four notebooks, and utils.py (shared loaders and rendering helpers)
• scripts/ — gain_correction.py (the replicate correction), reannotate_scores.py (builds the annotated table), export_raw_scores.py (builds the raw table)
• src/labelseq_mapk/ — config.py and annotation.py, the two modules the above import
• config/ — the four YAML files
• data/dn_cutoffs_empty_vector.tsv — the per-library empty-vector DN thresholds, the one data file small enough to version
• docs/ — a column reference for each of the two tables
• environment.yaml

Not here: any data. The barcode counts, the score tables and the annotated tables are orders of magnitude too large for GitHub, and the annotation source files are third-party downloads. config/paths.yaml is therefore a template: its paths point into a data/inputs/ tree that you populate, not at the locations they were built from. The tables are available on request and will be deposited with the manuscript.

Environment

conda env create -f environment.yaml
conda activate labelseq_mapk