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Digital Protein / Nucleic Acid Interaction Prediction Model

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

Deep learning models predict protein–protein and protein–nucleic acid interfaces as atomic-level structural hypotheses, each with its model confidence assessed. Covering DNA-binding proteins, RNA-binding proteins, transcription factors and nucleosome complexes, with optional partner-run validation.

DNA & RNA

Both supported

1–3 wk

Typical turnaround

Ranked

Model confidence

PDB + figures

Deliverable formats

Illustrative image representing computational analysis

No Reagents for the In Silico Phase

Confidence-Scored Results

Publication-Ready Figures

Validation Planned With Partners

Response Within One Business Day

Service Overview

ICDMO's Digital Protein/Nucleic Acid Interaction Prediction Model applies current deep-learning complex prediction to deliver structural predictions with explicit confidence metrics 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.

Schematic: pockets are detected across the whole surface, scored for druggability, and the residues carrying the binding energy are mapped before any screening begins.

Technical Advantages

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Multi-Model Ensemble Accuracy

Several independent prediction approaches are run in parallel where the task warrants it. Agreement between them strengthens a prediction, and disagreement is reported rather than hidden behind a single consensus number.

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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: complex models are generated across multiple seeds, with an orthogonal method run for comparison and rapid screening applied across variants. Consensus and divergence between methods are both recorded.

3Interface Analysis & Contact Map Generation: Predicted contacts (Cβ–Cβ < 8 Å) scored by confidence; key interface residues ranked by their computed contribution to binding energy.

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: publication-quality renders of the 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

What Published Work Reports

1Deep-learning complex prediction now models protein–nucleic acid assemblies directly rather than treating the nucleic acid as an afterthought, and on independent protein–ligand benchmarks the same generation of methods places roughly three quarters of predictions within 2 Å of the experimental pose (Abramson et al., Nature, 2024).

2Accuracy remains materially lower for flexible single-stranded RNA, for interfaces dominated by shape rather than base readout, and where ion coordination governs the contact. We state which regime your system sits in before starting.

3Interface confidence and predicted aligned error are reported per contact surface, since these — not global confidence — determine whether a modelled interface can carry a design or mutagenesis decision.

4Published results above come from independent groups and indicate what these methods achieve in benchmark conditions. They are not ICDMO project results and do not predict the outcome for any individual system.

What We Need From You

1Protein sequence or structure, with the relevant domain boundaries and isoform specified

2Nucleic acid sequence, length and whether it is single- or double-stranded, linear or structured

3Known binding motifs, SELEX or CLIP data, or footprinting results that constrain the interaction

4Ions, cofactors and buffer conditions relevant to the interaction

5The question: specificity, interface geometry, mutational effect or complex architecture

How the Analysis Is Performed

1Sequence and motif analysis, including comparison against known binding preferences for the protein family

2Protein–nucleic acid complex prediction with systems trained on nucleic acid complexes rather than protein-only models

3Per-interface confidence assessment and comparison across multiple seeds and, where useful, independent methods

4Interface characterisation: base readout, shape readout, electrostatics and water-mediated contacts

5Variant scanning to assess how sequence changes on either side alter the predicted interaction

6Optional molecular dynamics in explicit solvent with physiological ion concentrations to test stability

What You Receive

1Predicted complex structures in PDB format with per-model and per-interface confidence

2Interface analysis identifying base-specific contacts, backbone contacts and electrostatic complementarity

3Specificity assessment across sequence variants where a motif question is being asked

4Annotated figures and viewer sessions suitable for publication

5A written statement of which structural conclusions the confidence scores support

Interpretation and Limitations

Protein–nucleic acid prediction is less mature than protein-only structure prediction. Accuracy is lowest for flexible single-stranded RNA, for interactions dominated by shape rather than base readout, and where ion coordination governs the interface. Predicted complexes are structural hypotheses requiring EMSA, footprinting, crosslinking or structural confirmation, and are delivered for research use only.

Questions We Are Asked Before Scoping

Is RNA harder than DNA?

▸Generally yes. Structured RNA must be folded correctly before the complex can be predicted, and single-stranded regions are flexible enough that confidence is often low. We state which regime your system falls into before starting.

Can you predict binding specificity across a motif?

▸We can rank sequence variants by predicted interaction quality, which is useful for prioritising which sequences to test. It is not equivalent to a measured binding profile from SELEX or EMSA.

Can you model a protein–DNA complex with a drug bound?

▸Yes, ternary systems can be modelled, though confidence drops with each additional component. We report the interface confidence for each contact surface separately rather than as a single number.

Technical Workflow

Scientific Consultation

We discuss the objective, the decision the result has to support and the data you already hold. Nothing is quoted before that is clear.

Feasibility Review

We check that your inputs, target class and available evidence can support the approach, and say plainly where they cannot.

Technical Proposal

Scope, methods, deliverables, timeline and price are confirmed in writing, along with the limitations we expect to report.

Computation

Sequences and motifs are analysed, complexes are predicted with nucleic-acid-capable systems across multiple seeds, and interfaces are characterised and, where scoped, tested by simulation.

Report and Follow-Up

You receive the ranked complex models, the interface analysis, the specificity assessment and a clear statement of confidence.

Service Timeline & Deliverables

Service Module	Turnaround	Deliverables
Single protein–nucleic acid complex prediction (1–10 pairs)	5 working days	Full 3D complex structure (PDB), interface residue list, pLDDT/ipTM/PAE scores, contact map, molecular viewer session file, written analysis report
Batch protein–DNA or protein–RNA interaction prediction (10–200 pairs)	7–10 working days	Ranked 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 days	Full interaction confidence matrix, network visualization (Cytoscape-ready), ranked pair list, representative PDB structures for top hits
IDR-containing protein or phase-separation complex modeling	Quoted per complexity	Ensemble of predicted conformations, disordered binding motif analysis, condensate-forming propensity score, consultation report
Optional: Experimental validation (EMSA, Y1H, CLIP-qPCR, SPR)	Varies by method	Experimental validation report confirming top predicted interactions; gel images, binding curves, or qPCR data as appropriate

Is This the Right Service for You?

Choose this when

A protein–DNA or protein–RNA interface needs structural characterisation

Binding specificity across sequence variants must be prioritised for testing

A transcription factor, RNA-binding protein or nuclease complex is the subject

Consider another service when

Both partners are proteins

Protein Interaction & Binding Site Prediction

Editing outcomes rather than structure are the question

AI-Driven Gene Editing & Therapy

What Goes Wrong, and How We Handle It

These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.

Flexible single-stranded RNA modelled with unwarranted confidence

Accuracy for flexible regions is substantially lower; we state which regime the system is in before starting.

Ion coordination ignored where it governs the interface

Ions are included explicitly in the model and simulation, since leaving them out can invert the predicted geometry.

Predicted specificity read as a measured binding profile

Variant ranking is delivered as a prioritisation for EMSA, footprinting or SELEX, not as a substitute for them.

A Worked Example

Prioritising promoter variants for experimental testing

The situation

A transcription factor had a known consensus motif and dozens of candidate variant sites.

What the work involved

The complex was predicted with nucleic-acid-capable methods across multiple seeds, interfaces were characterised for base-specific and backbone contacts, and variants were scanned for predicted interaction quality.

What came back

A ranked variant list with the contacts each change disrupts, reducing the EMSA panel from several dozen sequences to nine that spanned the predicted range.

Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.

What Published Work Reports

The results below were published by independent groups and are cited to show what these computational approaches have achieved in the literature. They are not ICDMO project results, and outcomes vary substantially between targets, datasets and programmes. We do not promise comparable results for any particular project.

Around three quarters of predicted ligand poses fall within 2 Å

Current deep-learning structure prediction, evaluated on an independent protein–ligand benchmark, placed roughly 76% of ligands within 2 Å of the experimental pose, and reported approximately 80% by coordinate error — a substantial improvement over classical docking baselines on the same set, achieved without supplying a binding site. The corollary matters as much as the headline: roughly a quarter of cases still fall outside that threshold, which is why we report confidence per interface rather than delivering a single model.

Abramson et al., Nature, 2024 ↗