AI-Based Structure Prediction & Analysis

Production-Grade AI Folding with Experimental Validation

Key Features:

• Optimized MSA Construction — Custom sequence database curation and MSA depth optimization for eukaryotic, membrane-bound, and multi-domain targets.
• Template-Enhanced Folding — Integration of experimental structures, homology models, or Cryo-EM maps as folding templates to boost confidence in critical regions.
• Confidence-Gated Downstream Design — pLDDT/pTM scores directly inform virtual screening strategy: high-confidence pockets enter SBVS; low-confidence regions trigger FBDD or experimental Cryo-EM validation.

What We Offer: For virtual biotechs, structural starting points within days of receiving a FASTA file — no protein expression delay. For pharma, high-throughput structural coverage of entire target portfolios, each model validated against experimental data and flagged for risk where confidence is low.

Template-Based Modeling for Targets with Known Folds

Key Features:

• Sensitive Template Detection — HHpred, HMMER, and custom profile libraries identify remote homologs invisible to standard BLAST searches.
• Loop & Side-Chain Refinement — MD-based relaxation and rotamer optimization correct template-induced distortions in binding pockets.
• Ligand-Guided Model Optimization — When co-crystal structures of homologs exist, bound-ligand conformations template active-site geometry, improving docking accuracy by 30–50%.

What We Offer: For targets lacking direct homologs, our threading pipelines identify fold relationships across protein families, enabling structure-based design where sequence-based methods fail. Every model ships with per-residue confidence scores and a validation report ready for SBDD deployment.

Folding Orphan Targets Without Templates

Key Features:

• Co-evolutionary Contact Prediction — GREMLIN, CCMPred, and deep learning contact maps extract structural constraints from sequence covariance, enabling accurate folding even for orphan proteins.
• Fragment Assembly & Refinement — Rosetta fragment insertion combined with MD relaxation generates physically realistic conformations.
• Pocket Prediction on De Novo Folds — FPocket and FTMap scan predicted surfaces for druggable cavities, turning sequence-only targets into virtual screening candidates.

What We Offer: For first-in-class targets with no known homologs, this is often the only path to a structural starting point. We deliver ranked structural ensembles with pocket annotations, enabling hit identification campaigns to proceed in parallel with experimental structure determination.

Ensuring Models Are Ready for Drug Design

Key Features:

• Comprehensive Validation Metrics — MolProbity clash scores, Ramachandran distributions, rotamer outliers, and EMRinger scores for Cryo-EM models.
• B-Factor & Local Resolution Refinement — Real-space refinement in Phenix/Coot with local resolution filtering for Cryo-EM maps; anisotropic B-factor modeling for high-resolution X-ray data.
• Stability Validation — Microsecond-scale MD tests structural integrity under thermal stress; unstable regions are flagged for re-refinement or experimental reprocessing.

What We Offer: A model with poor geometry generates false-positive docking hits and wasted synthesis cycles. Our quality gates ensure every coordinate file is drug-design-ready, with validation statistics that satisfy peer-review and regulatory scrutiny.

From Raw Data to Atomic Coordinates

Key Features:

• X-ray Crystallography Pipeline — HKL2000/XDS data reduction, Phaser/Molrep molecular replacement, ARP/wARP or Phenix AutoBuild model building, and REFMAC5/Phenix refinement. We handle anomalous phasing (SAD/MAD) and native data alike.
• Cryo-EM/ET Single-Particle Analysis — RELION 4.0 / cryoSPARC v4.0 motion correction, CTF estimation, particle picking (Topaz/DeepPicker), 2D classification, 3D reconstruction, and local resolution estimation. Sub-tomogram averaging for in situ structures.
• AI-Assisted Model Building — DeepTracer, Phenix map-to-model, and AlphaFold2 seeding accelerate model building in low-resolution maps, cutting manual tracing time by 60%+.

What We Offer: For pharma teams with in-house data collection, we operate as your remote structure solution center — turning diffraction images or movie stacks into deposition-ready coordinates. For biotechs, this means accessing synchrotron-grade data processing without maintaining a structural bioinformatics team.

Turning Structures into Actionable Targets

Key Features:

• Pocket Detection & Characterization — FPocket, Concavity, and SiteMap identify orthosteric, allosteric, and cryptic pockets; pocket volume, hydrophobicity, and hydrogen-bond potential are quantified.
• Druggability Scoring — DrugScore, FTMap hot-spot analysis, and MD-based pocket stability assessments rank pockets by ligandability.
• Cryptic Pocket Discovery — Apo/holo MD simulations reveal transient pockets invisible in static crystal structures, opening new chemical space for "undruggable" targets.

What We Offer: Every predicted or experimental structure is useless without a validated binding site. Our pocket analysis transforms raw coordinates into prioritized virtual screening targets, with druggability reports that guide library design and medicinal chemistry strategy.

Modeling Multi-Component Assemblies

Key Features:

• Protein-Protein Docking — HADDOCK, ClusPro, and ZDOCK generate ranked complex poses; MD refinement validates interface stability.
• Peptide-Protein Interaction Modeling — Flexible peptide docking with induced-fit protocols; macrocycle and stapled-peptide conformational sampling.
• Nucleic Acid-Protein Complexes — Specialized force fields and MD protocols for DNA/RNA-binding proteins, including transcription factors and CRISPR complexes.

What We Offer: For PPI targets and large assemblies where experimental structures are scarce, our computational assembly pipelines provide testable models for fragment-based screening and biophysical assay design. Every complex model ships with interface scores and MM/PBSA binding free energy estimates.

AI-Driven Antibody Modeling for Biologics Discovery

Key Features:

• Fv/VH-VL Orientation Prediction — IgFold and DeepAb predict antibody variable domain structures with CDR loop accuracy approaching experimental quality for canonical classes.
• CDR H3 Refinement — MD-based loop remodeling and ensemble docking correct H3 predictions for non-canonical sequences.
• Antibody-Antigen Docking — Protein-protein docking with antibody-specific scoring; epitope constraint mapping from mutagenesis or HDX-MS data.

What We Offer: For biotechs building antibody pipelines without structural biology infrastructure, we deliver humanization-ready models, affinity maturation guidance, and developability risk flags — all from sequence alone.