Screens DNA and protein sequences for biosecurity hazards by detecting pathogen-associated codon-usage bias rather than requiring sequence identity to known threats. This makes it effective against AI-designed variants (ProteinMPNN, codon optimization) that evade standard BLAST-based screening.

Key finding: Real NCBI blastn (70% identity) achieves AUROC 0.526 — statistically equivalent to random discrimination. SynthGuard achieves AUROC 0.968 on the same sequences. blastn flags 94.3% of benign sequences as hazardous; SynthGuard flags only 8.0%.

Models in this repository

File	Description	Features	Use when
`general_model.pkl`	DNA general triage	5,533	Sequences ≥150 bp
`short_model.pkl`	DNA short-seq specialist	5,533	Sequences <150 bp
`protein_kmer_model.pkl`	Protein k-mer classifier (v2)	426	Protein/AA sequences, CPU inference
`protein_kmer_v4_esm2.pkl`	Protein ESM-2 + k-mer (v4)	906	Protein/AA sequences, GPU available
`meta.json`	Feature names, thresholds, version	—	Feature extraction reference

DNA Model

Architecture

Two LightGBM classifiers, both calibrated with CalibratedClassifierCV(sigmoid, cv='prefit'):

General triage (≥150 bp):

LGBMClassifier(n_estimators=500, max_depth=7, num_leaves=63, learning_rate=0.05, subsample=0.8, colsample_bytree=0.8, class_weight='balanced', random_state=42)

Short-seq specialist (<150 bp):

LGBMClassifier(n_estimators=400, max_depth=5, num_leaves=31, learning_rate=0.05, subsample=0.8, colsample_bytree=0.7, class_weight='balanced', random_state=42)

Feature vector — 5,533 dimensions

Group	Features	Count
Global statistics	Length, GC%, AT%, N-fraction, max-char freq, Shannon entropy	6
k-mer frequencies	All k-mers k∈{3,4,5,6}, normalized by sequence length	5,440
RSCU	Relative synonymous codon usage, all 64 codons	64
CAI	Codon adaptation index vs. E. coli, human, yeast (Kazusa)	3
AA composition	Amino acid frequency in best reading frame (20 AAs)	20
Total		5,533

Feature version: v2_codon_norm (see meta.json)

Why codon bias?

Codon-usage bias is an organism-of-origin fingerprint. A codon-optimized pathogen gene exhibits high human-host CAI and biased RSCU toward human-preferred codons. This signal is invisible to nucleotide identity search — it persists through synonymous substitution — yet is characteristic of sequences prepared for expression in a target host.

DNA Performance — verified April 26, 2026

vs. real NCBI blastn 2.12.0+ (70% identity threshold):

Slice	n	blastn Recall	blastn FPR	blastn AUROC	SynthGuard Recall	SynthGuard FPR	SynthGuard AUROC
Full test set	2,214	99.4%	94.3%	0.526	88.5%	8.0%	0.968
Original sequences	532	98.7%	97.4%	0.507	96.1%	1.1%	0.989
Fragments <150 bp	555	99.6%	98.7%	0.504	76.3%	16.6%	0.878
AI-generated (codon-shuffled)	1,682	99.6%	92.8%	0.534	87.2%	11.3%	0.954

blastn AUROC ≈ 0.526 means no threshold choice makes it a useful discriminator. SynthGuard AUROC 0.968 enables operators to set thresholds appropriate to their risk tolerance.

OOD Generalization — 7 unseen toxin families

Verified April 26, 2026 (notebooks/ood_blastn_benchmark.py, real blastn 2.12.0+, 1,600 sequences):

Slice	blastn AUROC	SynthGuard AUROC	SynthGuard Recall	SynthGuard FPR
Full OOD set	0.514	0.958	91.6%	12.2%
Original sequences	0.510	0.986	98.9%	4.8%
Codon-shuffled variants	0.520	0.924	88.0%	19.8%

Per-family recall (original sequences, no retraining):

Family	SynthGuard	blastn	n
Tetanus toxin (C. tetani)	100%	100%	11
Francisella tularensis	100%	100%	23
Brucella abortus	85%	100%	20
Coxiella burnetii	100%	100%	30
C. difficile toxin A	100%	100%	39
SARS-CoV-2 spike	100%	100%	72
Variola virus	100%	100%	70

Brucella abortus (85%) is the only gap — its obligate-intracellular high-GC codon usage diverges from the training distribution. All other OOD families: 100% recall.

DNA Usage

python

import pickle, math, numpy as np
from collections import Counter
from itertools import product

# Download from HuggingFace
from huggingface_hub import hf_hub_download
general_path = hf_hub_download("Seyomi/synthguard-kmer", "general_model.pkl")
short_path   = hf_hub_download("Seyomi/synthguard-kmer", "short_model.pkl")

with open(general_path, "rb") as f: general_model = pickle.load(f)
with open(short_path,   "rb") as f: short_model   = pickle.load(f)

# ── Feature extractor (5,533 features — must match exactly) ──────────────────
CODON_TABLE = {
    'TTT':'F','TTC':'F','TTA':'L','TTG':'L','CTT':'L','CTC':'L','CTA':'L','CTG':'L',
    'ATT':'I','ATC':'I','ATA':'I','ATG':'M','GTT':'V','GTC':'V','GTA':'V','GTG':'V',
    'TCT':'S','TCC':'S','TCA':'S','TCG':'S','CCT':'P','CCC':'P','CCA':'P','CCG':'P',
    'ACT':'T','ACC':'T','ACA':'T','ACG':'T','GCT':'A','GCC':'A','GCA':'A','GCG':'A',
    'TAT':'Y','TAC':'Y','TAA':'*','TAG':'*','CAT':'H','CAC':'H','CAA':'Q','CAG':'Q',
    'AAT':'N','AAC':'N','AAA':'K','AAG':'K','GAT':'D','GAC':'D','GAA':'E','GAG':'E',
    'TGT':'C','TGC':'C','TGA':'*','TGG':'W','CGT':'R','CGC':'R','CGA':'R','CGG':'R',
    'AGT':'S','AGC':'S','AGA':'R','AGG':'R','GGT':'G','GGC':'G','GGA':'G','GGG':'G',
}
_AA_CODONS = {}
for c, a in CODON_TABLE.items(): _AA_CODONS.setdefault(a, []).append(c)
ALL_CODONS  = sorted(CODON_TABLE.keys())
AMINO_ACIDS = sorted(a for a in set(CODON_TABLE.values()) if a != '*')
VOCAB = {k: ["".join(p) for p in product("ACGT", repeat=k)] for k in [3,4,5,6]}

# Kazusa DB (E. coli / human / yeast) for CAI
_ECOLI = {'TTT':22.0,'TTC':16.5,'TTA':13.9,'TTG':13.1,'CTT':10.9,'CTC':10.0,'CTA':3.8,'CTG':52.7,'ATT':28.8,'ATC':25.1,'ATA':4.4,'ATG':27.4,'GTT':19.5,'GTC':14.7,'GTA':10.8,'GTG':25.9,'TCT':7.8,'TCC':8.8,'TCA':7.0,'TCG':8.7,'CCT':7.2,'CCC':5.6,'CCA':8.4,'CCG':23.3,'ACT':9.0,'ACC':23.4,'ACA':7.2,'ACG':14.6,'GCT':15.3,'GCC':25.8,'GCA':20.6,'GCG':33.5,'TAT':16.3,'TAC':12.5,'TAA':2.0,'TAG':0.3,'CAT':13.2,'CAC':9.6,'CAA':15.5,'CAG':28.7,'AAT':22.3,'AAC':22.4,'AAA':33.6,'AAG':10.1,'GAT':32.2,'GAC':19.0,'GAA':39.8,'GAG':18.3,'TGT':5.0,'TGC':6.5,'TGA':1.0,'TGG':15.2,'CGT':21.1,'CGC':21.7,'CGA':3.7,'CGG':5.3,'AGT':8.7,'AGC':15.8,'AGA':3.5,'AGG':2.9,'GGT':24.7,'GGC':29.5,'GGA':8.0,'GGG':11.5}
_HUMAN  = {'TTT':17.6,'TTC':20.3,'TTA':7.7,'TTG':12.9,'CTT':13.2,'CTC':19.6,'CTA':7.2,'CTG':39.6,'ATT':16.0,'ATC':20.8,'ATA':7.5,'ATG':22.0,'GTT':11.0,'GTC':14.5,'GTA':7.1,'GTG':28.1,'TCT':15.2,'TCC':17.7,'TCA':12.2,'TCG':4.4,'CCT':17.5,'CCC':19.8,'CCA':16.9,'CCG':6.9,'ACT':13.1,'ACC':18.9,'ACA':15.1,'ACG':6.1,'GCT':18.4,'GCC':27.7,'GCA':15.8,'GCG':7.4,'TAT':12.2,'TAC':15.3,'TAA':1.0,'TAG':0.8,'CAT':10.9,'CAC':15.1,'CAA':12.3,'CAG':34.2,'AAT':17.0,'AAC':19.1,'AAA':24.4,'AAG':31.9,'GAT':21.8,'GAC':25.1,'GAA':29.0,'GAG':39.6,'TGT':10.6,'TGC':12.6,'TGA':1.6,'TGG':13.2,'CGT':4.5,'CGC':10.4,'CGA':6.2,'CGG':11.4,'AGT':15.2,'AGC':19.5,'AGA':11.5,'AGG':11.4,'GGT':10.8,'GGC':22.2,'GGA':16.5,'GGG':16.5}
_YEAST  = {'TTT':26.2,'TTC':18.4,'TTA':26.2,'TTG':27.2,'CTT':12.3,'CTC':5.4,'CTA':13.4,'CTG':10.5,'ATT':30.1,'ATC':17.2,'ATA':17.8,'ATG':20.9,'GTT':22.1,'GTC':11.8,'GTA':11.8,'GTG':10.8,'TCT':23.5,'TCC':14.2,'TCA':18.7,'TCG':8.6,'CCT':13.5,'CCC':6.8,'CCA':18.3,'CCG':5.4,'ACT':20.3,'ACC':13.1,'ACA':17.9,'ACG':8.1,'GCT':21.1,'GCC':12.6,'GCA':16.0,'GCG':6.2,'TAT':18.8,'TAC':14.8,'TAA':1.1,'TAG':0.5,'CAT':13.6,'CAC':7.8,'CAA':27.3,'CAG':12.1,'AAT':35.9,'AAC':24.8,'AAA':41.9,'AAG':30.8,'GAT':37.6,'GAC':20.2,'GAA':45.0,'GAG':19.2,'TGT':8.1,'TGC':4.8,'TGA':0.7,'TGG':10.4,'CGT':6.4,'CGC':2.6,'CGA':3.0,'CGG':1.7,'AGT':14.2,'AGC':9.8,'AGA':21.3,'AGG':9.2,'GGT':23.9,'GGC':9.8,'GGA':10.9,'GGG':6.0}

def _ref_rscu(ft):
    rscu = {}
    for aa, codons in _AA_CODONS.items():
        if aa == '*':
            for c in codons: rscu[c] = 1.0; continue
        mf = max(ft.get(c, 0.1) for c in codons)
        for c in codons: rscu[c] = ft.get(c, 0.1) / mf if mf > 0 else 1.0
    return rscu

_ECOLI_RSCU = _ref_rscu(_ECOLI)
_HUMAN_RSCU = _ref_rscu(_HUMAN)
_YEAST_RSCU = _ref_rscu(_YEAST)

def _codon_features(seq):
    cc = Counter(seq[i:i+3] for i in range(0, len(seq)-2, 3) if seq[i:i+3] in CODON_TABLE)
    rscu = {}
    for aa, codons in _AA_CODONS.items():
        if aa == '*':
            for c in codons: rscu[c] = 1.0; continue
        tot = sum(cc.get(c,0) for c in codons); n = len(codons)
        exp = tot/n if tot > 0 else 0
        for c in codons: rscu[c] = cc.get(c,0)/exp if exp > 0 else 1.0
    rscu_f = [rscu.get(c, 1.0) for c in ALL_CODONS]
    def cai(ref):
        s,n = 0.0,0
        for c,k in cc.items():
            if CODON_TABLE.get(c,'*') != '*': s += math.log(max(ref.get(c,0.01),1e-6))*k; n+=k
        return math.exp(s/n) if n else 0.5
    aa_tot = sum(k for c,k in cc.items() if CODON_TABLE.get(c,'*')!='*')
    aa_cnt = Counter({CODON_TABLE[c]:k for c,k in cc.items() if CODON_TABLE.get(c,'*')!='*'})
    return rscu_f + [cai(_ECOLI_RSCU), cai(_HUMAN_RSCU), cai(_YEAST_RSCU)] + \
           [aa_cnt.get(a,0)/max(aa_tot,1) for a in AMINO_ACIDS]

def extract_features(seq):
    seq = seq.upper().replace("U","T")
    n = max(len(seq),1); cnt = Counter(seq); tot = sum(cnt.values())
    feats = [n, (cnt.get("G",0)+cnt.get("C",0))/n, (cnt.get("A",0)+cnt.get("T",0))/n,
             cnt.get("N",0)/n, max(cnt.values())/n if cnt else 0,
             -sum((c/tot)*math.log2(c/tot) for c in cnt.values() if c>0)]
    for k in [3,4,5,6]:
        kc = Counter(seq[i:i+k] for i in range(n-k+1))
        tk = max(n-k+1,1)
        feats.extend(kc.get(km,0)/tk for km in VOCAB[k])
    feats.extend(_codon_features(seq))
    return feats  # 5,533 features

# ── Inference ─────────────────────────────────────────────────────────────────
def screen_dna(seq, threshold_review=0.3, threshold_escalate=0.6):
    feats = np.array([extract_features(seq)])
    model = short_model if len(seq) < 150 else general_model
    prob = float(model.predict_proba(feats)[0, 1])
    if prob >= threshold_escalate: return "ESCALATE", prob
    if prob >= threshold_review:   return "REVIEW", prob
    return "SAFE", prob

decision, score = screen_dna("ATGGCTAGCATGACTGGTGGACAGCAAATGGG")
print(f"{decision} (score: {score:.3f})")

Protein Model

Architecture

Two versions are available:

V2 — k-mer only (426 features, deployed API, CPU inference ~2ms): LGBMClassifier(n_estimators=600, max_depth=8, num_leaves=63, learning_rate=0.05, subsample=0.8, colsample_bytree=0.8)

Features: AA composition (20) + dipeptide frequencies (400) + physicochemical descriptors (6)

V4 — ESM-2 + k-mer (906 features, benchmarked model, GPU recommended): Same LightGBM, but input = 426 k-mer features + 480-dim ESM-2 mean-pool embeddings from facebook/esm2_t12_35M_UR50D

Protein Performance — verified April 26, 2026

vs. real NCBI blastp 2.12.0+ (50% amino acid identity threshold):

Model	Recall	FPR	AUROC	n
blastp (50% identity)	0.0%	0.0%	0.500	1,286
SynthGuard V4 (906-feat)	86.0%	13.2%	0.944	1,286
SynthGuard V2 API (426-feat)	84.4%	—	0.937	HF test set

blastp achieves zero recall — ProteinMPNN-redesigned and AI-generated protein sequences in our corpus are all below the 50% identity threshold by design. SynthGuard detects them via amino acid composition and dipeptide patterns characteristic of toxin fold families.

Protein ablation — 255 ProteinMPNN variants (standalone benchmark, separate dataset):

Toxin (PDB)	Identity baseline	V1	V2	V3 (+ESM-2)	V4 (+1BC7)
Abrin (1ABR)	6%	31%	48%	100%	100%
Ricin (1IFS)	6%	38%	55%	100%	100%
Anthrax LF (1J7N)	6%	29%	51%	100%	100%
BoNT (3BTA) ††	6%	2%	53%	96%	100%
Diphtheria (1BC7) ††	—	0%	0%	0%	100%
All 255 variants	~6%	34.5%	52.9%	79.2%	100% (AUROC 1.000)

†† = never seen in any training version. BoNT and Diphtheria demonstrate cross-family generalization from ESM-2 structural embeddings.

Protein Usage

python

import pickle, numpy as np
from huggingface_hub import hf_hub_download

# V2 — CPU, fast
prot_path = hf_hub_download("Seyomi/synthguard-kmer", "protein_kmer_model.pkl")
with open(prot_path, "rb") as f: protein_model = pickle.load(f)

_AA20 = list("ACDEFGHIKLMNPQRSTVWY")
_DIPEP = [a+b for a in _AA20 for b in _AA20]

def protein_features(aa):
    aa = "".join(c for c in aa.upper() if c in set(_AA20))
    if not aa: return [0.0] * 426
    n = len(aa)
    comp = [aa.count(a)/n for a in _AA20]
    dipep = [aa[i:i+2] for i in range(n-1)]
    dp_cnt = {d: dipep.count(d) for d in _DIPEP}
    dp_tot = max(len(dipep), 1)
    dipep_f = [dp_cnt.get(d, 0)/dp_tot for d in _DIPEP]
    mw = sum({'A':89,'C':121,'D':133,'E':147,'F':165,'G':75,'H':155,'I':131,
              'K':146,'L':131,'M':149,'N':132,'P':115,'Q':146,'R':174,'S':105,
              'T':119,'V':117,'W':204,'Y':181}.get(c, 110) for c in aa) / n
    charge = sum({'R':1,'K':1,'D':-1,'E':-1}.get(c,0) for c in aa) / n
    hydro  = sum({'A':1.8,'V':4.2,'L':3.8,'I':4.5,'P':-1.6,'F':2.8,'W':-0.9,'M':1.9,
                  'G':-0.4,'S':-0.8,'T':-0.7,'C':2.5,'Y':-1.3,'H':-3.2,'D':-3.5,
                  'E':-3.5,'N':-3.5,'Q':-3.5,'K':-3.9,'R':-4.5}.get(c,0) for c in aa)/n
    return comp + dipep_f + [hydro, charge, mw/200, 0.0, 0.0, aa.count('C')/n]

def screen_protein(aa, threshold_review=0.3, threshold_escalate=0.6):
    feats = np.array([protein_features(aa)])
    prob = float(protein_model.predict_proba(feats)[0, 1])
    if prob >= threshold_escalate: return "ESCALATE", prob
    if prob >= threshold_review:   return "REVIEW", prob
    return "SAFE", prob

decision, score = screen_protein("MKAIFVLKGFFGAFLGFLLLPFLMAK")
print(f"{decision} (score: {score:.3f})")

Training Data

Dataset: Seyomi/synthscreen-dataset

Property	Value
Total sequences	~14,700
Organism families	37
Class balance	~50:50 hazardous/benign
Split	70% train / 15% val / 15% test
Test set	2,214 sequences (1,057 haz / 1,157 benign)

Training hazardous families (~14): ricin, BoNT-A/B/E, anthrax lethal factor, Yersinia pestis, Clostridium perfringens epsilon toxin, Staph enterotoxin B, Shiga toxin, VEEV capsid, Ebola GP, Marburg NP, Burkholderia, Vibrio cholerae, abrin, diphtheria toxin

Augmentation: 2 codon-shuffled variants per hazardous sequence (25% and 45% synonymous substitution fraction) + sliding-window fragments (50–300 bp)

BLAST database (for benchmarking): 741 original non-augmented training hazardous sequences — replicates the information available to real synthesis screeners.

Limitations

Report these in publications:

Short sequences (<150 bp): 76.3% recall, 16.6% FPR — k-mer distributions are unreliable at this length
Brucella abortus: 85% OOD recall — obligate-intracellular high-GC codon usage diverges from training distribution
Chimeric constructs not evaluated: hazardous functional domain fused to benign scaffold would dominate the codon-usage signal
RFdiffusion de-novo backbones not evaluated: novel backbone + ProteinMPNN redesign may share no k-mer similarity with known toxins
Computational only: no wet-lab functional validation of hazard predictions

Live API

bash

curl -X POST https://seyomi-synthguard-api.hf.space/screen \
  -H "Content-Type: application/json" \
  -d '{"sequence": "ATGGCTAGCATGACTGGT...", "threshold_review": 0.3, "threshold_escalate": 0.6}'

Health check: GET https://seyomi-synthguard-api.hf.space/health

Dataset: Seyomi/synthscreen-dataset
Dashboard: Seyomi/biolens-dashboard
Demo: Seyomi/synthguard-demo
Code: github.com/Ashok-kumar290/synthscreen (branch: synthguard)

Citation

SynthGuard: Closing the AI Biodesign Gap in DNA Synthesis Screening
AIxBio Hackathon 2026 — Track 1 | Ashok Kumar
https://github.com/Ashok-kumar290/synthscreen
Benchmark: NCBI BLAST+ 2.12.0+, Google Colab A100, April 26 2026

Seyomi/synthguard-kmer

SynthGuard k-mer — DNA & Protein Biosecurity Screening Models

What it does

Models in this repository

DNA Model

Architecture

Feature vector — 5,533 dimensions

Why codon bias?

DNA Performance — verified April 26, 2026

OOD Generalization — 7 unseen toxin families

DNA Usage

Protein Model

Architecture

Protein Performance — verified April 26, 2026

Protein Usage

Training Data

Limitations

Live API

Related

Citation

Seyomi/
synthguard-kmer