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ICDMO AI Services

Digital Protein / Nucleic Acid Interaction Prediction Model

AI-Driven Structural Prediction for Protein–DNA, Protein–RNA, and PPI Networks

Deploy state-of-the-art deep learning models to predict protein–protein and protein–nucleic acid interaction interfaces with atomic precision. Covering DNA-binding proteins, RNA-binding proteins, transcription factors, and nucleosome complexes — with results in 5–10 days and optional downstream yeast validation.

Zero Experimental Cost
Confidence-Scored Results
Publication-Ready Figures
Wet-Lab Validation Available
24h Scientific Support

Service Overview

ICDMO's Digital Protein/Nucleic Acid Interaction Prediction Model leverages ensemble deep learning architectures (AlphaFold2-Multimer, RoseTTAFold, ESMFold) combined with proprietary in-house training datasets to deliver high-confidence structural predictions for protein–nucleic acid complexes. The platform models protein–DNA binding (transcription factor–promoter, CRISPR-Cas complexes, nucleosome interactions), protein–RNA binding (RBP–mRNA, lncRNA scaffolding, ribosome assembly), and complex protein–protein interaction networks, including intrinsically disordered regions often missed by classical methods. All models output 3D structural coordinates, interface confidence scores (pLDDT / ipTM), and predicted contact maps — formatted for immediate downstream use in drug design, mutagenesis planning, and publication.

Technical Advantages

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Multi-Model Ensemble Accuracy
Integrates AlphaFold2-Multimer, RoseTTAFold, and ESMFold predictions with proprietary scoring functions — consistently outperforming single-model outputs on benchmarked datasets.
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Full Nucleic Acid Coverage
Predicts protein–dsDNA, protein–ssDNA, protein–RNA, protein–G-quadruplex, and protein–modified nucleotide interactions with specialized structural encoding.
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Intrinsically Disordered Region (IDR) Modeling
Dedicated IDR-aware modules capture disordered binding motifs that govern transcription factor activity and phase-separated condensate formation — critical for oncology targets.
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Confidence-Ranked Interaction Outputs
Every predicted complex receives pLDDT, ipTM, and PAE confidence metrics, enabling researchers to prioritize the most reliable predictions for experimental follow-up.
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Network-Level Interaction Mapping
Scale from single pair analysis to full protein–nucleic acid interactome mapping for pathway-level or genome-wide studies — supported up to 10,000 pairs per project.
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Seamless Experimental Integration
Directly paired with EMSA, ChIP-seq, RIP-seq, CLIP-seq, and yeast one-hybrid (Y1H) validation platforms for confirmed experimental readout of predicted interactions.

Supported Interaction Types & Model Coverage

Protein–DNA Interactions

▸Transcription factor (TF)–promoter binding: predict TF binding pose, contact residues, and DNA consensus motif engagement
▸CRISPR-Cas9/Cas12 PAM recognition and gRNA–target DNA complex modeling
▸Zinc finger, homeodomain, and TALE nuclease DNA-binding domain analysis
▸Nucleosome–histone modifier interactions: bromodomain, chromodomain, PWWP domain binding to modified histones
▸DNA methyltransferase (DNMT) and demethylase substrate specificity modeling

Protein–RNA Interactions

▸RNA-binding protein (RBP)–mRNA interaction: predict RRM, KH, DEAD-box, and zinc finger binding motifs
▸lncRNA scaffolding complex prediction: ternary complexes with chromatin regulators (PRC2, CoREST)
▸Splicing factor–pre-mRNA exonic/intronic splicing enhancer binding analysis
▸Ribosomal protein–rRNA and translation factor interaction modeling
▸miRNA–Argonaute–target mRNA ternary complex prediction for RISC assembly

Protein–Protein Interactions (PPI)

▸Binary PPI prediction with interface residue identification and ΔΔG estimation
▸Ternary and quaternary complex modeling for multi-subunit assemblies
▸PROTAC-relevant E3 ligase–POI ternary complex prediction
▸Antibody–antigen and nanobody–epitope interaction modeling
▸Intrinsically disordered protein (IDP) binding motif prediction for hub proteins

Technical Workflow

1Sequence Intake & Quality Check: Submit FASTA sequences or UniProt IDs for proteins; provide sequence or PDB for nucleic acid partners. Automated quality filtering removes low-complexity, signal peptides, and membrane anchors.
2Structure Prediction Pipeline: AlphaFold2-Multimer generates initial complex models; RoseTTAFold provides orthogonal prediction; ESMFold offers rapid screening of all variants. Ensemble consensus scoring applied.
3Interface Analysis & Contact Map Generation: Predicted contacts (Cβ–Cβ < 8 Å) scored by confidence; key interface residues ranked by contribution to binding energy using MM/GBSA decomposition.
4Confidence Scoring & Ranking: pLDDT (per-residue confidence), ipTM (interface TM-score), and PAE (predicted aligned error) matrices generated for each complex. Pairs ranked by composite confidence.
53D Visualization & Report Generation: PyMOL and ChimeraX renders of top complexes; predicted contact maps; interface residue lists; confidence heatmaps — all formatted for direct journal submission.
6Optional Experimental Validation: Top predictions flagged for EMSA, Y1H, ChIP-PCR, RIP-qPCR, or SPR/BLI confirmation via ICDMO's wet-lab validation services.

Application Scenarios in Drug Discovery & Basic Research

1Target Identification: Map previously uncharacterized protein–nucleic acid interactions in disease-relevant pathways (oncology, neurology, infectious disease)
2Hit Prioritization: Score thousands of candidate TF–DNA or RBP–RNA interactions computationally before committing to ChIP-seq or CLIP-seq experiments
3Resistance Mechanism Analysis: Predict how point mutations in binding interfaces alter interaction affinity — critical for resistance profiling in oncology
4PROTAC & Molecular Glue Design: Model ternary POI–linker–E3 ligase complexes to optimize degrader geometry before synthesis
5Antibody Epitope Mapping: Predict antibody binding sites on protein–nucleic acid complexes relevant to autoantigens or viral targets
6CRISPR Off-Target Assessment: Model Cas9 binding geometry at predicted off-target sites to assess cleavage probability

Key Algorithmic References

1AlphaFold2-Multimer (Evans et al., bioRxiv 2021): State-of-the-art deep learning for protein complex structure prediction
2RoseTTAFold (Baek et al., Science 2021): Three-track neural network architecture combining 1D, 2D, and 3D information for protein structure prediction
3ESMFold (Lin et al., Science 2023): Large language model-based protein folding for rapid high-throughput prediction
4Nucleic Acid Prediction: RNAComposer, 3dDNA, RoseTTAFoldNA (Baek et al. 2024) for protein–RNA and protein–DNA complex modeling
5Interaction Scoring: MM/GBSA free energy decomposition (AMBER 2022), GNN-based interface scoring (HADDOCK3, 2023)

Service Timeline & Deliverables

Service ModuleTurnaroundDeliverables
Single protein–nucleic acid complex prediction (1–10 pairs)5 working daysFull 3D complex structure (PDB), interface residue list, pLDDT/ipTM/PAE scores, contact map, PyMOL session file, written analysis report
Batch protein–DNA or protein–RNA interaction prediction (10–200 pairs)7–10 working daysRanked interaction table with confidence scores, top-ranked 3D structures (PDB), contact map heatmaps, summary Excel, analysis report
Large-scale PPI or protein–nucleic acid network (200–10,000 pairs)14–21 working daysFull interaction confidence matrix, network visualization (Cytoscape-ready), ranked pair list, representative PDB structures for top hits
IDR-containing protein or phase-separation complex modelingQuoted per complexityEnsemble of predicted conformations, disordered binding motif analysis, condensate-forming propensity score, consultation report
Optional: Experimental validation (EMSA, Y1H, CLIP-qPCR, SPR)Varies by methodExperimental validation report confirming top predicted interactions; gel images, binding curves, or qPCR data as appropriate

Service Process

1
Online Consultation
2
Solution Matching
3
Service Contract
4
AI Computation
5
Project Report

Note: All services are for research use only and not intended for diagnostic or therapeutic purposes.

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Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your research goals.

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Standard Deliverables

✓Detailed analysis report (PDF)
✓Raw data files & processed outputs
✓High-resolution publication figures
✓Interaction scoring tables (Excel)
✓Project summary presentation

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