Key insight: Metrics work as filters (eliminating failures) not predictors (ranking successes).
Campaign Health Assessment
Quick assessment of your design campaign:
Pass Rate
Status
Interpretation
> 15%
Excellent
Above average, proceed
10-15%
Good
Normal, proceed
5-10%
Marginal
Below average, review issues
< 5%
Poor
Significant problems, diagnose
Failure Recovery Trees
Too Few Pass pLDDT Filter (< 5% with pLDDT > 0.85)
Low pLDDT across campaign
├── Check scRMSD distribution
│ ├── High scRMSD (>2.5Å): Backbone issue
│ │ └── Fix: Regenerate backbones with lower noise_scale (0.5-0.8)
│ └── Low scRMSD but low pLDDT: Disordered regions
│ └── Fix: Check design length, simplify topology
├── Try more sequences per backbone
│ └── modal run modal_proteinmpnn.py --num-seq-per-target 32 --sampling-temp 0.1
├── Use SolubleMPNN instead of ProteinMPNN
│ └── Better for expression-optimized sequences
└── Consider different design tool
└── BindCraft (integrated design) may work better
Too Few Pass ipTM Filter (< 5% with ipTM > 0.5)
Low ipTM across campaign
├── Review hotspot selection
│ ├── Are hotspots surface-exposed? (SASA > 20Ų)
│ ├── Are hotspots conserved? (check MSA)
│ └── Try 3-6 different hotspot combinations
├── Increase binder length (more contact area)
│ └── Try 80-100 AA instead of 60-80 AA
├── Check interface geometry
│ ├── Is target flat? → Try helical binders
│ └── Is target concave? → Try smaller binders
└── Try all-atom design tool
└── BoltzGen (all-atom, better packing)
High scRMSD (> 50% with scRMSD > 2.0Å)
Sequences don't specify intended structure
├── ProteinMPNN issue
│ ├── Lower temperature: --sampling-temp 0.1
│ ├── Increase sequences: --num-seq-per-target 32
│ └── Check fixed_positions aren't over-constraining
├── Backbone geometry issue
│ ├── Backbones may be unusual/strained
│ ├── Regenerate with lower noise_scale (0.5-0.8)
│ └── Reduce diffuser.T to 30-40
└── Try different sequence design
└── ColabDesign (AF2 gradient-based) may work better
Everything Passes But No Experimental Hits
In silico metrics don't predict affinity
├── Generate MORE designs (10x current)
│ └── Computational metrics have high false positive rate
├── Increase diversity
│ ├── Higher ProteinMPNN temperature (0.2-0.3)
│ ├── Different backbone topologies
│ └── Different hotspot combinations
├── Try different design approach
│ ├── BindCraft (different algorithm)
│ ├── ColabDesign (AF2 hallucination)
│ └── BoltzGen (all-atom diffusion)
└── Check if target is druggable
└── Some targets are inherently difficult
Too Many Designs Pass (> 50%)
Suspiciously high pass rate
├── Check if thresholds are too lenient
│ └── Use stringent thresholds: pLDDT > 0.90, ipTM > 0.60
├── Verify prediction quality
│ ├── Are predictions actually running? Check output files
│ └── Are complexes being predicted, not just monomers?
├── Check for data issues
│ ├── Same sequence being predicted multiple times?
│ └── Wrong FASTA format (missing chain separator)?
└── Apply diversity filter
└── Cluster at 70% identity, take top per cluster
