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1bf37a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | """Audit #17 + #18: multiple alignment, and conservation counted from it.
ESM-2 gives a learned opinion about whether a substitution looks plausible.
Conservation across real homologs gives an OBSERVED fact: in 40 orthologs this
position is serine 40 times. They fail differently — the model is weakest
exactly where the audit says (membrane, disordered, multi-domain) and a
frequency count is unaffected by any of that.
The tests that matter are about the INPUT, not the algorithm. Conservation is
only as meaningful as the homolog set, and a set of near-identical sequences
makes every column look invariant. A tool that reports that as "conserved"
without comment is worse than no tool.
"""
import pytest
from dee.core import agent_tools as t
from dee.core import conservation as C
from dee.core import orchestrator as orch
# Position 1,3,4,5,6 invariant; position 2 varies across all five.
HOMOLOGS = ["MAKQWLTVEG", "MSKQWLTVEG", "MTKQWLSVEG", "MAKQWLTVDG", "MGKQWLTIEG"]
def test_an_invariant_column_is_found_and_a_variable_one_is_not():
r = C.score(HOMOLOGS)
by_pos = {c["anchor_position"]: c for c in r["conservation"]}
assert by_pos[5]["call"] == "invariant" and by_pos[5]["agreement_pct"] == 100.0
assert by_pos[2]["call"] == "variable" and by_pos[2]["agreement_pct"] < 60
def test_entropy_is_never_reported_as_negative_zero():
"""-sum(...) over an all-agreeing column yields IEEE -0.0, which prints as
'-0.0 bits' and reads like a bug in a number users are asked to trust."""
r = C.score(HOMOLOGS)
for c in r["conservation"]:
assert c["entropy_bits"] >= 0.0
assert str(c["entropy_bits"]) != "-0.0"
def test_a_redundant_homolog_set_is_flagged_and_marked_untrustworthy():
"""The failure that would make this tool actively misleading. Forty
sequences at 99% identity are ONE sequence counted forty times, and every
column will look invariant."""
r = C.score(["MAKQWLTVEG"] * 4 + ["MAKQWLTVEA"])
assert r["trustworthy"] is False
assert r["diversity_warning"] and "redundancy" in r["diversity_warning"]
assert "more divergent" in r["diversity_warning"]
def test_a_diverse_set_is_trusted():
"""A checker that never trusts anything is a checker nobody reads."""
r = C.score(HOMOLOGS)
assert r["trustworthy"] is True and r["diversity_warning"] is None
def test_too_few_sequences_is_refused_with_a_way_forward():
r = C.score(["MAKQ", "MSKQ"])
assert r["ok"] is False and r["kind"] == "too_few"
assert "BLAST" in r["next"]
def test_positions_are_numbered_along_the_anchor_the_user_designs_against():
r = C.score(HOMOLOGS, positions=[5])
assert [(c["anchor_position"], c["anchor_residue"]) for c in r["conservation"]] \
== [(5, "W")]
def test_indels_do_not_shift_the_anchor_numbering():
"""A homolog with a deletion must not renumber the sequence the user is
designing against — that is how a conservation call lands on the wrong
residue."""
r = C.score(["MAKQWLTVEG", "MAKWLTVEG", "MAKQWLTVEG"])
positions = [c["anchor_position"] for c in r["conservation"]
if c["anchor_position"]]
assert positions == sorted(positions)
assert max(positions) == 10 # anchor length, unchanged by the gap
def test_the_alignment_says_it_is_approximate():
"""Progressive alignment is not a simultaneous optimum. Every practical
tool approximates; pretending otherwise is the dishonest part."""
m = C.align_many(HOMOLOGS)
assert m["ok"] and "Approximate" in m["method"]
assert all(len(r["aligned"]) == m["columns"] for r in m["rows"])
def test_the_anchor_is_the_longest_sequence():
m = C.align_many(["MAK", "MAKQWLTVEG", "MAKQ"], ["a", "b", "c"])
assert m["anchor"] == "b"
def test_it_positions_itself_against_esm2_rather_than_as_a_confirmation():
"""Two independent signals. Averaging them would destroy the only thing
that makes having both worthwhile."""
r = C.score(HOMOLOGS)
assert "complements ESM-2" in r["caveat"]
assert "disagreement is worth investigating" in r["caveat"]
def test_conservation_is_framed_as_an_observation_not_a_property():
r = C.score(HOMOLOGS)
assert "OBSERVATION" in r["caveat"]
assert "not a property of the protein" in r["caveat"]
def test_it_is_reachable_ungated_and_specced():
assert "check_conservation" in t._TOOLS
assert any(s["function"]["name"] == "check_conservation" for s in orch.TOOL_SPECS)
assert orch._requires_confirm("check_conservation") is False
def test_the_summary_leads_with_untrustworthiness_when_it_applies():
r = t.execute_tool("check_conservation",
{"sequences": ["MAKQWLTVEG"] * 4 + ["MAKQWLTVEA"]},
auth_anonymous=True)
assert "too redundant to trust" in orch._summarize("check_conservation", r)
def test_the_spec_makes_the_agent_relay_both_limits():
d = next(s["function"]["description"] for s in orch.TOOL_SPECS
if s["function"]["name"] == "check_conservation")
assert "approximate" in d
assert "diversity_warning" in d and "trustworthy" in d
assert "complements ESM-2" in d
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