ov.synbio IV — advanced SOTA: mRNA design, de-novo binders, prime editing

ov.synbio IV — advanced SOTA: mRNA design, de-novo binders, prime editing#

This tutorial tours the advanced-SOTA layer of ov.synbio — real, published state-of-the-art models wired behind the same method= convention. Each shows a baseline ↔ SOTA comparison so you can see what the heavy model buys you.

  • mRNA therapeutics — LinearDesign (joint codon+structure optimisation)

  • RNA design — inverse folding, siRNA, antisense oligos

  • Genome editing — prime editing (pegRNA), CRISPRi/a, Cas13

  • Metabolic — dynamic FBA, minimal cut sets, retrobiosynthesis

  • De-novo binder design — RFdiffusion → ProteinMPNN → Boltz-2

Heavy backends (LinearDesign, Boltz-2, RFdiffusion) run from their own environments via subprocess; import omicverse needs none of them. pip install 'omicverse[synbio]'.

import omicverse as ov

ov.plot_set()
print('omicverse', ov.__version__)

🔬 Starting plot initialization...
🧬 Detecting GPU devices…
✅ NVIDIA CUDA GPUs detected: 1
    • [CUDA 0] NVIDIA H100 80GB HBM3
      Memory: 79.1 GB | Compute: 9.0

   ____            _     _    __                  
  / __ \____ ___  (_)___| |  / /__  _____________ 
 / / / / __ `__ \/ / ___/ | / / _ \/ ___/ ___/ _ \ 
/ /_/ / / / / / / / /__ | |/ /  __/ /  (__  )  __/ 
\____/_/ /_/ /_/_/\___/ |___/\___/_/  /____/\___/                                              

🔖 Version: 2.2.1rc1   📚 Tutorials: https://omicverse.readthedocs.io/
✅ plot_set complete.

omicverse 2.2.1rc1

1 — mRNA therapeutics design (LinearDesign)#

Designing an mRNA is not just codon optimisation: secondary-structure stability (MFE) and codon adaptation (CAI) trade off. mrna_design(method='lineardesign') runs the real LinearDesign (Zhang et al., Nature 2023) — a lattice dynamic program that finds the jointly MFE/CAI-optimal mRNA. lambda tunes the balance.

protein = 'MGSSHHHHHHSSGLVPRGSHMKIEEGKLVIWINGDKGYNGLAEVGKKFEKDTGIKVTVEHPDKLEEKFPQVAATGDGPDIIFWAHDRFGG'
# baseline (DNAchisel codon-opt) vs LinearDesign at two lambdas
base = ov.synbio.mrna_design(protein, method='baseline')
ld0  = ov.synbio.mrna_design(protein, method='lineardesign', lam=0.0)
ld3  = ov.synbio.mrna_design(protein, method='lineardesign', lam=3.0)
print(base); print(ld0); print(ld3)

MRNADesign(len=270nt, MFE=-94.0 kcal/mol, CAI=0.648, baseline)
MRNADesign(len=270nt, MFE=-158.4 kcal/mol, CAI=0.817, lineardesign)
MRNADesign(len=270nt, MFE=-142.2 kcal/mol, CAI=0.945, lineardesign)

LinearDesign dominates the baseline on both axes — a more negative folding energy (stabler mRNA) and a higher CAI. lambda=0 is the most-structured; raising it trades structure for codon optimality:

labels = ['baseline', 'LinearDesign λ=0', 'LinearDesign λ=3']
ov.synbio.plot_method_comparison(labels,
    {'-MFE (kcal/mol, stabler↑)': [-base.mfe, -ld0.mfe, -ld3.mfe],
     'CAI × 40 (codon opt↑)':     [40*base.cai, 40*ld0.cai, 40*ld3.cai]},
    ylabel='score', title='mRNA design: baseline vs LinearDesign')
None
../_images/9986213d3e23f62a9011376fa6e00b036eede95f9eff6e6ce67a1d6df9223188.png 
# the secondary structure LinearDesign optimises — fold the designed mRNA and draw it
s = ov.synbio.rna_fold(ld3.mrna[:90])
ov.synbio.plot_rna_structure(s.sequence, s.structure, title='LinearDesign mRNA — 5\' region')
None
../_images/4338aedd6ac391b5c45e570f64149297d9a393c8a70b6bfe5c2cef150a2b6a20.png

2 — RNA design: inverse folding, siRNA, antisense#

rna_inverse_design solves the inverse folding problem — find a sequence that folds into a target structure (riboswitches, aptamers, thermometers).

target = '((((((((....))))))))'    # a hairpin
designs = ov.synbio.rna_inverse_design(target, n=3)
for d in designs:
    print(f'{d.sequence}  ->  {d.structure}  (dist={d.distance}, mfe={d.mfe:.1f})')

GCGCGCGCGCGCGCGCGCGU  ->  ((((((((....))))))))  (dist=0, mfe=-15.3)
GGGCGCGCGCGCGCGCGCCU  ->  ((((((((....))))))))  (dist=0, mfe=-15.8)
GCGCGCGCGCGCGCGCGUGC  ->  ((((((((....))))))))  (dist=0, mfe=-14.8)

# the designed sequence folds exactly into the requested hairpin
ov.synbio.plot_rna_structure(designs[0].sequence, designs[0].structure,
                             title='inverse-designed hairpin')
None
../_images/37802c3b3b5ed5b0937b6cfaed0d04907f81db53dbfd64bc3d80b5020faee21f.png

sirna_design ranks 19-mer siRNAs by the published Reynolds-2004 rules; aso_design designs antisense gapmers scored by target accessibility, Tm and liability motifs:

mrna = ('ATGGGGAAGGTGAAGGTCGGAGTCAACGGATTTGGTCGTATTGGGCGCCTGGTCACCAGGGCTGCTTTTAACTCTGGT'
        'AAAGTGGATATTGTTGCCATCAATGACCCCTTCATTGACCTCAACTACATGGTTTACATGTTCCAATATGATTCCACCC')
si = ov.synbio.sirna_design(mrna, n=3, method='reynolds')
for s in si: print('siRNA', s.antisense, 'score', s.score)
aso = ov.synbio.aso_design(mrna, length=18, n=2)
for a in aso: print('ASO', a.aso, f'Tm={a.tm:.0f} open={a.accessibility:.1f}')

siRNA UUGAUGGCAACAAUAUCCAUU score 6
siRNA AUUGAUGGCAACAAUAUCCUU score 6
siRNA UCAUUGAUGGCAACAAUAUUU score 6
ASO TCCACTTTACCAGAGTTA Tm=43 open=3.9
ASO TTACCAGAGTTAAAAGCA Tm=42 open=3.9

Where do they bind? plot_binding_sites maps the designed siRNAs and antisense oligos onto the target transcript:

# where the siRNAs and antisense oligos bind on the transcript
ov.synbio.plot_binding_sites(mrna, sirnas=si, asos=aso)
None
../_images/354c115baebd5692dc8a2988d89d0eb9f86b3fa844609cb97c942acdd70f239b.png

3 — Prime editing (pegRNA)#

prime_editing_design designs a pegRNA (spacer + RT template + primer-binding site) and a PE3 nicking guide for a desired edit. method='baseline' uses transparent Anzalone-2019 rules; method='primedesign' runs the vendored PrimeDesign (Pinello lab). Here we install a point mutation:

locus = 'ATGTGCTGTGATGGTATGCCGGCGTAGTAATCGTAGCACGTTGCACTGACCATCGGACTGACATGGCTAGATTTTGGGGCC'
ep = 17
pb = ov.synbio.prime_editing_design(locus, edit_pos=ep, ref=locus[ep], alt='A', method='baseline')[0]
pd = ov.synbio.prime_editing_design(locus, edit_pos=ep, ref=locus[ep], alt='A', method='primedesign')[0]
print('baseline    :', pb.spacer, '| ext', pb.extension_3p)
print('PrimeDesign :', pd.spacer, '| ext', pd.extension_3p, '| PE3', pd.pe3_nick['spacer'] if pd.pe3_nick else None)

baseline    : ACGTGCTACGATTACTACGC | ext GTGATGGTATACCGGCGTAGTAATCGTAGC
PrimeDesign : ACGTGCTACGATTACTACGC | ext ATGTGCTGTGATGGTATACCGGCGTAGTAATCGTAGCAC | PE3 TAGCACGTTGCACTGACCAT

Visualise the pegRNA. plot_pegrna maps the PrimeDesign result onto the target locus — the protospacer, the Cas9 nick, the PBS and RT template (which carries the edit), the edit position, and the PE3 secondary nick.

# map both pegRNA designs onto the target locus — baseline vs PrimeDesign
ov.synbio.plot_pegrna(locus, pb, edit_pos=ep, label='baseline')
ov.synbio.plot_pegrna(locus, pd, edit_pos=ep, label='PrimeDesign')
None
../_images/509fbc84d4073d7d1c60e0d61bdfede4df75af037ab2e1811245e7141741227b.png ../_images/02bda4cdeeea8e29699fb56fe3f79466278f9a144737ba24eb2ad7832e423fdd.png

Both pick the same spacer / nick — the transparent baseline agrees with the reference tool, which adds ranged PBS/RTT and PE3b ngRNAs. crispr_regulation and design_cas13_guides cover CRISPRi/a and RNA-targeting Cas13:

promoter = ('GCATGCATGC'*4) + 'ATGGCACGTGGATCCGGACATGCACCGGTTAACGGGACCGGT'*3
mrna_u = mrna.replace('T', 'U')
gi = ov.synbio.crispr_regulation(promoter, tss=50, mode='crispri')
print('CRISPRi best:', gi[0].spacer, 'TSS%+d' % gi[0].tss_offset)
c13 = ov.synbio.design_cas13_guides(mrna_u, n=4)
print('Cas13 crRNA :', c13[0].spacer, 'PFS', c13[0].pfs)

CRISPRi best: TCCGGACATGCACCGGTTAA TSS+86
Cas13 crRNA : AUGGUUUACAUGUUCCAAUAUGAUUCCA PFS C

The same map works for guides — CRISPRi guides along the promoter (relative to the TSS) and Cas13 crRNAs along the transcript:

# guide binding sites — CRISPRi on the promoter, Cas13 on the transcript
ov.synbio.plot_binding_sites(promoter, guides=gi[:4], title='CRISPRi guides on promoter')
ov.synbio.plot_binding_sites(mrna_u, cas13_guides=c13, title='Cas13 crRNAs on transcript')
None
../_images/4b944ed8999a591ce087acf12f067f557821e9304686f9ac63c3fb7ab6672384.png ../_images/15d8fc999913fdf830545c8bc003a1f3c99574a12eaed3da6aabb03898ee332b.png

4 — Metabolic: dynamic FBA, minimal cut sets, retrobiosynthesis#

dynamic_fba turns a genome-scale model into a batch-fermentation time course (Mahadevan 2002): Michaelis–Menten uptake sets the FBA bound each step, and biomass/substrate/product integrate forward.

m = ov.synbio.load_gem('e_coli_core')
m.reactions.EX_o2_e.lower_bound = -12          # O2-limited -> acetate overflow
df = ov.synbio.dynamic_fba(m, 'EX_glc__D_e', biomass_0=0.01, substrate_0=15,
                           t_end=12, products=['EX_ac_e'])
ov.synbio.plot_dynamic_fba(df)
None



../_images/e7b38db19b2a1e317266c3f2dd53518671e807fc6184d9c5761c6c9acd6c26b0.png






strain_design(method='mcs') computes minimal cut sets that growth-couple a product; retro_biosynthesis proposes novel enzymatic steps to a target molecule via reaction rules:






res = ov.synbio.strain_design(m, 'EX_succ_e', method='mcs', max_knockouts=2)
print('succinate cut sets:', list(res.knockout['knockouts'].head(3)))
routes = ov.synbio.retro_biosynthesis('CC(=O)C(=O)O')   # pyruvate
print('pyruvate precursors:', routes[0].steps[0].rule, '->', routes[0].precursors)









succinate cut sets: [['CO2t', 'PGI'], ['EX_co2_e', 'PGI']]
pyruvate precursors: aldehyde_oxidation -> ['CC(=O)C=O']













# draw the retrobiosynthetic routes as molecule structures
ov.synbio.plot_retro_routes(routes, n=3)
None








../_images/954d2ecbb175658e6f0b69399c8f82208811eef341747f052ad661604445603d.png










5 — De-novo binder design (RFdiffusion → ProteinMPNN → Boltz-2)#


The flagship protein-design workflow. denovo_binder (1) diffuses binder backbones docked on the target’s hotspots with RFdiffusion, (2) designs the binder sequence with ProteinMPNN (target fixed), and (3) validates each design by folding it. Here we design a binder against the insulin receptor:






# RFdiffusion ships this target (insulin receptor) in its examples
import os
target = os.path.join(os.environ['OMICOS_SYNBIO_WEIGHTS'],
                      'RFdiffusion/examples/input_pdbs/insulin_target.pdb')
designs = ov.synbio.denovo_binder(target, hotspot_res=['A59','A83','A91'],
                                  binder_length=70, num_backbones=1, num_seqs=1,
                                  validate='esmfold')
for d in designs:
    print(d)
    print('  binder:', d.sequence)









[2026-07-21 05:20:10,146][__main__][INFO] - Found GPU with device_name NVIDIA H100 80GB HBM3. Will run RFdiffusion on NVIDIA H100 80GB HBM3
Reading models from /scratch/users/steorra/.omicverse_synbio_weights/RFdiffusion/models
[2026-07-21 05:20:10,147][rfdiffusion.inference.model_runners][INFO] - Reading checkpoint from /scratch/users/steorra/.omicverse_synbio_weights/RFdiffusion/models/Complex_base_ckpt.pt
This is inf_conf.ckpt_path
/scratch/users/steorra/.omicverse_synbio_weights/RFdiffusion/models/Complex_base_ckpt.pt
Assembling -model, -diffuser and -preprocess configs from checkpoint
USING MODEL CONFIG: self._conf[model][n_extra_block] = 4
USING MODEL CONFIG: self._conf[model][n_main_block] = 32
USING MODEL CONFIG: self._conf[model][n_ref_block] = 4
USING MODEL CONFIG: self._conf[model][d_msa] = 256
USING MODEL CONFIG: self._conf[model][d_msa_full] = 64
USING MODEL CONFIG: self._conf[model][d_pair] = 128
USING MODEL CONFIG: self._conf[model][d_templ] = 64
USING MODEL CONFIG: self._conf[model][n_head_msa] = 8
USING MODEL CONFIG: self._conf[model][n_head_pair] = 4
USING MODEL CONFIG: self._conf[model][n_head_templ] = 4
USING MODEL CONFIG: self._conf[model][d_hidden] = 32
USING MODEL CONFIG: self._conf[model][d_hidden_templ] = 32
USING MODEL CONFIG: self._conf[model][p_drop] = 0.15
USING MODEL CONFIG: self._conf[model][SE3_param_full] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 8, 'l0_out_features': 8, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 32}
USING MODEL CONFIG: self._conf[model][SE3_param_topk] = {'num_layers': 1, 'num_channels': 32, 'num_degrees': 2, 'n_heads': 4, 'div': 4, 'l0_in_features': 64, 'l0_out_features': 64, 'l1_in_features': 3, 'l1_out_features': 2, 'num_edge_features': 64}
USING MODEL CONFIG: self._conf[model][freeze_track_motif] = False
USING MODEL CONFIG: self._conf[model][use_motif_timestep] = True
USING MODEL CONFIG: self._conf[diffuser][T] = 50
USING MODEL CONFIG: self._conf[diffuser][b_0] = 0.01
USING MODEL CONFIG: self._conf[diffuser][b_T] = 0.07
USING MODEL CONFIG: self._conf[diffuser][schedule_type] = linear
USING MODEL CONFIG: self._conf[diffuser][so3_type] = igso3
USING MODEL CONFIG: self._conf[diffuser][crd_scale] = 0.25
USING MODEL CONFIG: self._conf[diffuser][so3_schedule_type] = linear
USING MODEL CONFIG: self._conf[diffuser][min_b] = 1.5
USING MODEL CONFIG: self._conf[diffuser][max_b] = 2.5
USING MODEL CONFIG: self._conf[diffuser][min_sigma] = 0.02
USING MODEL CONFIG: self._conf[diffuser][max_sigma] = 1.5
USING MODEL CONFIG: self._conf[preprocess][sidechain_input] = False
USING MODEL CONFIG: self._conf[preprocess][motif_sidechain_input] = True
USING MODEL CONFIG: self._conf[preprocess][d_t1d] = 24
USING MODEL CONFIG: self._conf[preprocess][d_t2d] = 44
USING MODEL CONFIG: self._conf[preprocess][prob_self_cond] = 0.5
USING MODEL CONFIG: self._conf[preprocess][str_self_cond] = True
USING MODEL CONFIG: self._conf[preprocess][predict_previous] = False
[2026-07-21 05:20:51,699][rfdiffusion.inference.model_runners][INFO] - Loading checkpoint.
[2026-07-21 05:20:51,812][rfdiffusion.diffusion][INFO] - Using cached IGSO3.
Successful diffuser __init__
[2026-07-21 05:20:51,837][__main__][INFO] - Making design /tmp/ovsynbio_binder_ujp3904e/backbone_0_0
[2026-07-21 05:20:51,847][rfdiffusion.inference.model_runners][INFO] - Using contig: ['A1-150/0 70-70']
With this beta schedule (linear schedule, beta_0 = 0.04, beta_T = 0.28), alpha_bar_T = 0.00013696050154976547
[2026-07-21 05:20:51,998][rfdiffusion.inference.model_runners][INFO] - Sequence init: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:20:56,466][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.14
[2026-07-21 05:20:56,483][rfdiffusion.inference.model_runners][INFO] - Timestep 50, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:20:59,513][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.14
[2026-07-21 05:20:59,525][rfdiffusion.inference.model_runners][INFO] - Timestep 49, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:02,545][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:02,560][rfdiffusion.inference.model_runners][INFO] - Timestep 48, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:05,595][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:05,610][rfdiffusion.inference.model_runners][INFO] - Timestep 47, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:08,644][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:08,659][rfdiffusion.inference.model_runners][INFO] - Timestep 46, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:11,722][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:11,733][rfdiffusion.inference.model_runners][INFO] - Timestep 45, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:14,755][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:14,770][rfdiffusion.inference.model_runners][INFO] - Timestep 44, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:17,781][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:17,800][rfdiffusion.inference.model_runners][INFO] - Timestep 43, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:20,771][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:20,784][rfdiffusion.inference.model_runners][INFO] - Timestep 42, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:23,832][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:23,847][rfdiffusion.inference.model_runners][INFO] - Timestep 41, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:26,844][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:26,856][rfdiffusion.inference.model_runners][INFO] - Timestep 40, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:29,926][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:29,941][rfdiffusion.inference.model_runners][INFO] - Timestep 39, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:32,978][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:32,994][rfdiffusion.inference.model_runners][INFO] - Timestep 38, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:35,976][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:35,981][rfdiffusion.inference.model_runners][INFO] - Timestep 37, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:38,159][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:38,174][rfdiffusion.inference.model_runners][INFO] - Timestep 36, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:41,219][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:41,234][rfdiffusion.inference.model_runners][INFO] - Timestep 35, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:44,272][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:44,287][rfdiffusion.inference.model_runners][INFO] - Timestep 34, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:47,274][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:47,289][rfdiffusion.inference.model_runners][INFO] - Timestep 33, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:50,299][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:50,314][rfdiffusion.inference.model_runners][INFO] - Timestep 32, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:53,331][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:53,346][rfdiffusion.inference.model_runners][INFO] - Timestep 31, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:56,396][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:56,412][rfdiffusion.inference.model_runners][INFO] - Timestep 30, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:21:59,422][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:21:59,437][rfdiffusion.inference.model_runners][INFO] - Timestep 29, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:02,475][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:02,491][rfdiffusion.inference.model_runners][INFO] - Timestep 28, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:05,532][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:05,544][rfdiffusion.inference.model_runners][INFO] - Timestep 27, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:08,512][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:08,528][rfdiffusion.inference.model_runners][INFO] - Timestep 26, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:11,526][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:11,541][rfdiffusion.inference.model_runners][INFO] - Timestep 25, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:14,598][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:14,614][rfdiffusion.inference.model_runners][INFO] - Timestep 24, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:17,638][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:17,653][rfdiffusion.inference.model_runners][INFO] - Timestep 23, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:20,557][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:20,573][rfdiffusion.inference.model_runners][INFO] - Timestep 22, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:23,490][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:23,505][rfdiffusion.inference.model_runners][INFO] - Timestep 21, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:26,419][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:26,434][rfdiffusion.inference.model_runners][INFO] - Timestep 20, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:29,350][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:29,361][rfdiffusion.inference.model_runners][INFO] - Timestep 19, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:32,263][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:32,283][rfdiffusion.inference.model_runners][INFO] - Timestep 18, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:35,214][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:35,230][rfdiffusion.inference.model_runners][INFO] - Timestep 17, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:38,154][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:38,171][rfdiffusion.inference.model_runners][INFO] - Timestep 16, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:41,097][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:41,113][rfdiffusion.inference.model_runners][INFO] - Timestep 15, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:43,995][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:44,011][rfdiffusion.inference.model_runners][INFO] - Timestep 14, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:46,928][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:46,943][rfdiffusion.inference.model_runners][INFO] - Timestep 13, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:49,863][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:49,878][rfdiffusion.inference.model_runners][INFO] - Timestep 12, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:52,753][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:52,768][rfdiffusion.inference.model_runners][INFO] - Timestep 11, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:55,660][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:55,675][rfdiffusion.inference.model_runners][INFO] - Timestep 10, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:22:58,622][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:22:58,634][rfdiffusion.inference.model_runners][INFO] - Timestep 9, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:01,565][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:23:01,580][rfdiffusion.inference.model_runners][INFO] - Timestep 8, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:04,465][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:23:04,476][rfdiffusion.inference.model_runners][INFO] - Timestep 7, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:07,364][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:23:07,380][rfdiffusion.inference.model_runners][INFO] - Timestep 6, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:10,308][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:23:10,323][rfdiffusion.inference.model_runners][INFO] - Timestep 5, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:13,239][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:23:13,254][rfdiffusion.inference.model_runners][INFO] - Timestep 4, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:16,164][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:23:16,179][rfdiffusion.inference.model_runners][INFO] - Timestep 3, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:19,109][rfdiffusion.inference.utils][INFO] - Sampled motif RMSD: 0.13
[2026-07-21 05:23:19,128][rfdiffusion.inference.model_runners][INFO] - Timestep 2, input to next step: ----------------------------------------------------------------------EVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNHIVLNKDDNEEC
[2026-07-21 05:23:24,690][__main__][INFO] - Finished design in 2.55 minutes
[ov.synbio.predict_structure] device=cuda (NVIDIA H100 80GB HBM3, 79 GB) len=70
[ov.synbio.predict_structure] done: mean pLDDT=94.4
BinderDesign(len=70, mpnn=0.932, pLDDT=0.94, seq=SETLKAAGYQVKALSYASEA...)
  binder: SETLKAAGYQVKALSYASEAEELAKKNPETKELAEKAKEAAEKAIKSTDAEEAKKYAEEAERLLKEMKEA













# fold the designed binder and view it in interactive 3D (coloured by pLDDT)
pred = ov.synbio.predict_structure(designs[0].sequence)
ov.synbio.view_structure(pred)









[ov.synbio.predict_structure] device=cuda (NVIDIA H100 80GB HBM3, 79 GB) len=70
[ov.synbio.predict_structure] done: mean pLDDT=94.4






        
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<py3Dmol.view at 0x7f87a565d8a0>









A single command produced a folded, target-docked mini-binder. predict_complex (Boltz-2) can co-fold and score any protein–ligand or protein–protein complex for binding affinity:






gb1 = 'MTYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTE'
r = ov.synbio.predict_complex([{'id':'A','protein':gb1}, {'id':'B','smiles':'c1ccccc1'}],
                              affinity_binder='B')
print(f'pLDDT={r.plddt:.2f}  pTM={r.ptm:.2f}  affinity p_bind={r.affinity["binder_probability"]:.2f}')









pLDDT=0.91  pTM=0.94  affinity p_bind=0.04













Summary#


Every advanced method follows the omicverse convention — one function, SOTA behind method=:





Task


baseline


SOTA (method=)






mRNA design


DNAchisel codon-opt


LinearDesign (Nature 2023)




prime editing


Anzalone rules


PrimeDesign




complex + affinity


—


Boltz-2 (AF3-class)




de-novo binder


—


RFdiffusion → ProteinMPNN → Boltz




strain design


FSEOF


OptKnock / MCS









Combine with the core, circuits and CRISPR tutorials for the full ov.synbio surface.







                

              

              
              
              
              
                
              
            

            
            
              
                



  

  

     Contents