fix(features): correct positional modification indexing, and optional terminal composition

[RobbinBouwmeester]

Why

Two problems in _features.py, both verified against current main.

1. Positional modification deltas land on the wrong row (bug)

_fill_pos_matrix lays the positional block out as sorted(positions), so row 0 means position -4 and row 4 means position 0. Modification deltas were written with raw indexing instead:

if i in positions:
    pos_mat[i, ...] += change          # i == 0 writes row 0, i.e. position -4
elif (i - seq_len) in positions:
    pos_mat[i - seq_len, ...] += change

Every positional modification delta was therefore recorded at the opposite end of the peptide from the residue it belongs to:

a = encode_peptidoform("PEPTIDEK")
b = encode_peptidoform("[Acetyl]-PEPTIDEK")
# on main: the delta appears in row 0, meaning position -4

There is a second, subtler half. In a short peptide one residue occupies both a positive and a negative row (index 3 of a 7-mer is also -4). _fill_pos_matrix writes the base residue to both, because its two loops run independently, but the delta path used if/elif and reached only one. MAECLIR|4|Carbamidomethyl is an example.

Both are now resolved through a single _positional_rows helper, so deltas follow the base residues exactly.

2. Terminal and side-chain modifications are indistinguishable (gap)

_apply_terminal_modifications correctly folds terminal groups into matrix and the positional block, but at the same position as a side-chain modification on the first or last residue, so the two produce identical features:

encode_peptidoform("[Acetyl]-PEPTIDEK")   # N-terminal
encode_peptidoform("P[Acetyl]EPTIDEK")    # side chain, residue 1
# identical matrix_global

They are chemically different and elute differently.

Implementation

• _positional_rows(i, seq_len, positions) returns every row a sequence position occupies, applying the same offset and both-ends handling as _fill_pos_matrix. _apply_composition_to_matrices writes through it.
• encode_peptidoform(..., add_terminal_composition=True) appends the N- and C-terminal group compositions, taking matrix_global from 55 to 67 values. Defaults to False, so existing models keep their feature width and behaviour is unchanged apart from the bug fix.

How to test

pytest tests/

Two regression tests are added:

• test_positional_delta_uses_same_row_as_base_residue asserts an N-terminal delta lands on the position-0 row and a C-terminal delta on the position -1 row.
• test_terminal_composition_is_opt_in_and_separates_terminal_from_side_chain asserts the default width stays 55, the flag gives 67, and the two acetyl forms differ.

All 68 tests pass. Separately, 1,200 peptidoforms were encoded through this code and through an independent implementation: matrix_global matches on 1,199 of 1,200 after the fix, against 3 of 400 differing before.

Impact

The fix changes features for every peptidoform carrying a modification at a tracked position. Models trained against the previous behaviour will need retraining or revalidation. The shipped models learned around the misplaced deltas to some degree, but corrected features are what a new model should use.

Follow-up, not in this PR

A multitask model trained on the corrected 67-value features reaches 0.540 min pooled MAE across 1,025 LC setups (2.36M held-out observations, median absolute error 0.220, r 0.9987).

That model is being retrained against this branch's extractor. Its feature cache was built by the 2.x extractor, so it saw terminal composition in matrix_global but not in matrix, whereas this branch supplies both; serving it as-is would give it features it was not trained on for the ~0.5% of peptidoforms carrying a terminal modification. It will follow as a separate PR once validated.

Calibration is deliberately left unchanged. Replacing best-head selection with a ridge over head outputs improves median relative MAE from 2.49% to 2.31% across 20 datasets at 1,000 calibration peptides and wins on 17 of 20 — but it is a wash at 200 peptides, worse on several runs, and the larger gains measured elsewhere depend on a low-rank linear read-out rather than these ReLU heads. Not a good enough trade to change a working default, so best-head selection stays.

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